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class="user-summary-container"><div class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" alt="Heena Rathore" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/26678070/8232471/15250726/s200_heena.rathore.jpg" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Heena Rathore</h1><div class="affiliations-container fake-truncate js-profile-affiliations"><div><a class="u-tcGrayDarker" href="https://iitj.academia.edu/">IIT Jodhpur</a>, <a class="u-tcGrayDarker" href="https://iitj.academia.edu/Departments/Computer_Science/Documents">Computer Science</a>, <span class="u-tcGrayDarker">Doctor of Philosophy</span></div></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" 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class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Heena Rathore</h3></div><div class="js-work-strip profile--work_container" data-work-id="78356346"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356346/A_review_of_security_challenges_attacks_and_resolutions_for_wireless_medical_devices"><img alt="Research paper thumbnail of A review of security challenges, attacks and resolutions for wireless medical devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356346/A_review_of_security_challenges_attacks_and_resolutions_for_wireless_medical_devices">A review of security challenges, attacks and resolutions for wireless medical devices</a></div><div class="wp-workCard_item"><span>2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Evolution of implantable medical devices for human beings has provided a radical new way for trea...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356346"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356346"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356346; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356346]").text(description); $(".js-view-count[data-work-id=78356346]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356346; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356346']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356346, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356346]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356346,"title":"A review of security challenges, attacks and resolutions for wireless medical devices","translated_title":"","metadata":{"abstract":"Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. 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Today,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356345"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356345"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356345; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356345]").text(description); $(".js-view-count[data-work-id=78356345]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356345; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356345']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356345, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356345]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356345,"title":"DLRT: Deep Learning Approach for Reliable Diabetic Treatment","translated_title":"","metadata":{"abstract":"Diabetic therapy or insulin treatment enables patients to control the blood glucose level. 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We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"GLOBECOM 2017 - 2017 IEEE Global Communications Conference"},"translated_abstract":"Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.","internal_url":"https://www.academia.edu/78356345/DLRT_Deep_Learning_Approach_for_Reliable_Diabetic_Treatment","translated_internal_url":"","created_at":"2022-05-03T12:23:24.841-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"DLRT_Deep_Learning_Approach_for_Reliable_Diabetic_Treatment","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20178628,"url":"http://xplorestaging.ieee.org/ielx7/8253768/8253909/08255028.pdf?arnumber=8255028"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356344"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356344/Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks"><img alt="Research paper thumbnail of Mathematical Evaluation of Human Immune Systems For Securing Software Defined Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356344/Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks">Mathematical Evaluation of Human Immune Systems For Securing Software Defined Networks</a></div><div class="wp-workCard_item"><span>2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM)</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The immune system of the human body has massive potential in defending it against multiple harmfu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356344"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356344"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356344; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356344]").text(description); $(".js-view-count[data-work-id=78356344]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356344; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356344']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356344, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356344]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356344,"title":"Mathematical Evaluation of Human Immune Systems For Securing Software Defined Networks","translated_title":"","metadata":{"abstract":"The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM)"},"translated_abstract":"The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.","internal_url":"https://www.academia.edu/78356344/Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks","translated_internal_url":"","created_at":"2022-05-03T12:23:24.690-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20178627,"url":"http://xplorestaging.ieee.org/ielx7/8622653/8629585/08629728.pdf?arnumber=8629728"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356341"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356341/Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework"><img alt="Research paper thumbnail of Invited Paper: mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using a Smart Edge Framework" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356341/Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework">Invited Paper: mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using a Smart Edge Framework</a></div><div class="wp-workCard_item"><span>2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases, it is practical and efficient to manage and process test configuration and data analysis as close to the testbed as possible (inline) instead of offline compute platforms. We present mmMoReEdge, a mmWave modular and reconfigurable testbed inspired by a smart edge networking and communication framework, typically found in IoT devices. In mmMoReEdge, complex signal processing is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. Using a mathematical model for processing time (the proposed figure of merit), we present results which show that mmMoReEdge is 50% to 70% faster as compared to an offline general-purpose processor based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356341"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356341"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356341; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356341]").text(description); $(".js-view-count[data-work-id=78356341]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356341; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356341']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356341, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356341]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356341,"title":"Invited Paper: mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using a Smart Edge Framework","translated_title":"","metadata":{"abstract":"As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases, it is practical and efficient to manage and process test configuration and data analysis as close to the testbed as possible (inline) instead of offline compute platforms. We present mmMoReEdge, a mmWave modular and reconfigurable testbed inspired by a smart edge networking and communication framework, typically found in IoT devices. In mmMoReEdge, complex signal processing is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. Using a mathematical model for processing time (the proposed figure of merit), we present results which show that mmMoReEdge is 50% to 70% faster as compared to an offline general-purpose processor based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)"},"translated_abstract":"As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases, it is practical and efficient to manage and process test configuration and data analysis as close to the testbed as possible (inline) instead of offline compute platforms. We present mmMoReEdge, a mmWave modular and reconfigurable testbed inspired by a smart edge networking and communication framework, typically found in IoT devices. In mmMoReEdge, complex signal processing is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. Using a mathematical model for processing time (the proposed figure of merit), we present results which show that mmMoReEdge is 50% to 70% faster as compared to an offline general-purpose processor based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.","internal_url":"https://www.academia.edu/78356341/Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework","translated_internal_url":"","created_at":"2022-05-03T12:23:24.428-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20178624,"url":"http://xplorestaging.ieee.org/ielx7/9145943/9156071/09156144.pdf?arnumber=9156144"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356340"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356340/Towards_a_Practical_Pedestrian_Distraction_Detection_Framework_using_Wearables"><img alt="Research paper thumbnail of Towards a Practical Pedestrian Distraction Detection Framework using Wearables" class="work-thumbnail" src="https://attachments.academia-assets.com/85433105/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356340/Towards_a_Practical_Pedestrian_Distraction_Detection_Framework_using_Wearables">Towards a Practical Pedestrian Distraction Detection Framework using Wearables</a></div><div class="wp-workCard_item"><span>2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e87b3c9de2c134f4fd67bfd63e61b7a0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433105,"asset_id":78356340,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433105/download_file?st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356340"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356340"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356340; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356340]").text(description); $(".js-view-count[data-work-id=78356340]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356340; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356340']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356340, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356339"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356339/Blockchain_applications_for_healthcare"><img alt="Research paper thumbnail of Blockchain applications for healthcare" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356339/Blockchain_applications_for_healthcare">Blockchain applications for healthcare</a></div><div class="wp-workCard_item"><span>Energy Efficiency of Medical Devices and Healthcare Applications</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Healthcare systems control and monitor the health of patients with the help of advanced ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Healthcare systems control and monitor the health of patients with the help of advanced technologies. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchain and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a de-centralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient, and enables the provision of financial services, thereby giving users more freedom and power. This emerging technology provides internet users the capability to create value and authenticate the digital information. It has the capability to revolutionize a diverse set of business applications ranging from sharing economy to data management and prediction markets. In this paper, we present a survey of blockchain applications in healthcare. Healthcare systems play a very crucial role in people&#39;s life and there are diverse ways by which it can benefit from the blockchain technology and have been discussed in the chapter. The survey results demonstrate that Blockchain has distinct advantages for healthcare applications as compared to other applications. The benefits of blockchain can be further amplified by using a light-weight distributed ledger system like IOTA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356339"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356339"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356339; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356339]").text(description); $(".js-view-count[data-work-id=78356339]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356339; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356339']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356339, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356339]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356339,"title":"Blockchain applications for healthcare","translated_title":"","metadata":{"abstract":"Abstract Healthcare systems control and monitor the health of patients with the help of advanced technologies. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchain and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a de-centralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient, and enables the provision of financial services, thereby giving users more freedom and power. This emerging technology provides internet users the capability to create value and authenticate the digital information. It has the capability to revolutionize a diverse set of business applications ranging from sharing economy to data management and prediction markets. In this paper, we present a survey of blockchain applications in healthcare. Healthcare systems play a very crucial role in people\u0026#39;s life and there are diverse ways by which it can benefit from the blockchain technology and have been discussed in the chapter. The survey results demonstrate that Blockchain has distinct advantages for healthcare applications as compared to other applications. The benefits of blockchain can be further amplified by using a light-weight distributed ledger system like IOTA.","publisher":"Elsevier","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Energy Efficiency of Medical Devices and Healthcare Applications"},"translated_abstract":"Abstract Healthcare systems control and monitor the health of patients with the help of advanced technologies. 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It has the capability to revolutionize a diverse set of business applications ranging from sharing economy to data management and prediction markets. In this paper, we present a survey of blockchain applications in healthcare. Healthcare systems play a very crucial role in people\u0026#39;s life and there are diverse ways by which it can benefit from the blockchain technology and have been discussed in the chapter. The survey results demonstrate that Blockchain has distinct advantages for healthcare applications as compared to other applications. The benefits of blockchain can be further amplified by using a light-weight distributed ledger system like IOTA.","internal_url":"https://www.academia.edu/78356339/Blockchain_applications_for_healthcare","translated_internal_url":"","created_at":"2022-05-03T12:23:24.113-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Blockchain_applications_for_healthcare","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1131312,"name":"Academic","url":"https://www.academia.edu/Documents/in/Academic"}],"urls":[{"id":20178622,"url":"https://api.elsevier.com/content/article/PII:B978012819045600008X?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356338"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356338/mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_an_Edge_Inspired_Architecture"><img alt="Research paper thumbnail of mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using an Edge-Inspired Architecture" class="work-thumbnail" src="https://attachments.academia-assets.com/85433042/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356338/mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_an_Edge_Inspired_Architecture">mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using an Edge-Inspired Architecture</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing arc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing architecture found in IoT devices. In mmMoReEdge, complex signal processing, typically required for 5G testing, is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. We present simulation results that show that mmMoReEdge is 50% to 70% faster as compared to an offline CPU-based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5fa409609fcf9226ba729f821c44fccf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433042,"asset_id":78356338,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433042/download_file?st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356338"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356338"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356338; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356338]").text(description); $(".js-view-count[data-work-id=78356338]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356338; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356338']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356338, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5fa409609fcf9226ba729f821c44fccf" } } $('.js-work-strip[data-work-id=78356338]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356338,"title":"mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using an Edge-Inspired Architecture","translated_title":"","metadata":{"abstract":"We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing architecture found in IoT devices. In mmMoReEdge, complex signal processing, typically required for 5G testing, is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. We present simulation results that show that mmMoReEdge is 50% to 70% faster as compared to an offline CPU-based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing architecture found in IoT devices. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356336"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356336/A_Bio_Inspired_Framework_to_Mitigate_DoS_Attacks_in_Software_Defined_Networking"><img alt="Research paper thumbnail of A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking" class="work-thumbnail" src="https://attachments.academia-assets.com/85433102/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356336/A_Bio_Inspired_Framework_to_Mitigate_DoS_Attacks_in_Software_Defined_Networking">A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking</a></div><div class="wp-workCard_item"><span>2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Software Defined Networking (SDN) is an emerging architecture providing services on a priority ba...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer an...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="85cc92cd02c60942fcbfc520b3c4b216" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433102,"asset_id":78356336,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433102/download_file?st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356336"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356336"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356336; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356336]").text(description); $(".js-view-count[data-work-id=78356336]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356336; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356336']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356336, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "85cc92cd02c60942fcbfc520b3c4b216" } } $('.js-work-strip[data-work-id=78356336]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356336,"title":"A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking","translated_title":"","metadata":{"abstract":"Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. 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The two layer immune inspired framework, viz innate layer an...","publisher":"2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)"},"translated_abstract":"Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer an...","internal_url":"https://www.academia.edu/78356336/A_Bio_Inspired_Framework_to_Mitigate_DoS_Attacks_in_Software_Defined_Networking","translated_internal_url":"","created_at":"2022-05-03T12:23:23.749-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85433102,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433102/thumbnails/1.jpg","file_name":"66615.pdf","download_url":"https://www.academia.edu/attachments/85433102/download_file?st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Bio_Inspired_Framework_to_Mitigate_DoS.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433102/66615-libre.pdf?1651606122=\u0026response-content-disposition=attachment%3B+filename%3DA_Bio_Inspired_Framework_to_Mitigate_DoS.pdf\u0026Expires=1732471281\u0026Signature=V-BciBEW7hUIET1axy-MIKO25uBmS2jQ7FHcqM8mnai0KoPdGzuvdYCXjkg2NFJRcu4aoChuwb0YOn7N4y4K7huf2Y7GfiOqlGntLvM4miHGyopNUJlcwRj9v1WUNTXsxF0xlpGIkoSNrjYUwhwFIArJk8HyH7RFZaKHq9kizzopSY92jj2IyzoCBaOLVkBobEvIwq5c1zAvzvgb-5tolMtmYTHMxvVm0DGNqrFnEOiIrnkgWZivievXH~noMWNjHeOWYGLhhFNzdskrBxWWK13tr-o4PvkAvdcvWPq4tgUb62VDHU4WhjqG21w3KyRlSAwucIHJPX941kWT7ghzcQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Bio_Inspired_Framework_to_Mitigate_DoS_Attacks_in_Software_Defined_Networking","translated_slug":"","page_count":2,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[{"id":85433102,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433102/thumbnails/1.jpg","file_name":"66615.pdf","download_url":"https://www.academia.edu/attachments/85433102/download_file?st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"A_Bio_Inspired_Framework_to_Mitigate_DoS.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433102/66615-libre.pdf?1651606122=\u0026response-content-disposition=attachment%3B+filename%3DA_Bio_Inspired_Framework_to_Mitigate_DoS.pdf\u0026Expires=1732471281\u0026Signature=V-BciBEW7hUIET1axy-MIKO25uBmS2jQ7FHcqM8mnai0KoPdGzuvdYCXjkg2NFJRcu4aoChuwb0YOn7N4y4K7huf2Y7GfiOqlGntLvM4miHGyopNUJlcwRj9v1WUNTXsxF0xlpGIkoSNrjYUwhwFIArJk8HyH7RFZaKHq9kizzopSY92jj2IyzoCBaOLVkBobEvIwq5c1zAvzvgb-5tolMtmYTHMxvVm0DGNqrFnEOiIrnkgWZivievXH~noMWNjHeOWYGLhhFNzdskrBxWWK13tr-o4PvkAvdcvWPq4tgUb62VDHU4WhjqG21w3KyRlSAwucIHJPX941kWT7ghzcQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":37747,"name":"OpenFlow","url":"https://www.academia.edu/Documents/in/OpenFlow"},{"id":193315,"name":"Software defined networking","url":"https://www.academia.edu/Documents/in/Software_defined_networking"}],"urls":[{"id":20178620,"url":"https://doi.org/10.1109/NTMS.2019.8763818"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356335"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356335/TangleCV"><img alt="Research paper thumbnail of TangleCV" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356335/TangleCV">TangleCV</a></div><div class="wp-workCard_item"><span>Proceedings of the ACM Workshop on Automotive Cybersecurity</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Connected vehicles are designed to make informed safety-related decisions based on data/informati...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Connected vehicles are designed to make informed safety-related decisions based on data/information they receive from various on-board sensors and other vehicles in the vicinity. However, attacks directed towards on-board sensors and network attacks on the wireless communication channels can adversely impact the correctness and integrity of this information and present a grave security and safety challenge to connected vehicles. Current centralized security solutions are not a good fit as they do not scale well and are unable to validate the correctness of the shared data. Decentralizing security provisioning in connected vehicles by means of the upcoming blockchain technology is an interesting alternative for overcoming these limitations. However, current permission-less linear hash-chain based blockchain solutions have low transaction throughput, high computational cost and are resource intensive, thereby making their adoption for designing a security solution for resource constrained connected cars difficult. In this paper, we present TangleCV, a decentralized technique for secure message sharing and recording for connected vehicles using an approach like Tangle, a directed acyclic graph based blockchain architecture. We introduce an initial design of TangleCV and describe how it provides improved efficiency and scalability against information correctness and information integrity attacks in connected vehicle networks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356335"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356335"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356335; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356335]").text(description); $(".js-view-count[data-work-id=78356335]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356335; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356335']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356335, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356335]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356335,"title":"TangleCV","translated_title":"","metadata":{"abstract":"Connected vehicles are designed to make informed safety-related decisions based on data/information they receive from various on-board sensors and other vehicles in the vicinity. However, attacks directed towards on-board sensors and network attacks on the wireless communication channels can adversely impact the correctness and integrity of this information and present a grave security and safety challenge to connected vehicles. Current centralized security solutions are not a good fit as they do not scale well and are unable to validate the correctness of the shared data. Decentralizing security provisioning in connected vehicles by means of the upcoming blockchain technology is an interesting alternative for overcoming these limitations. However, current permission-less linear hash-chain based blockchain solutions have low transaction throughput, high computational cost and are resource intensive, thereby making their adoption for designing a security solution for resource constrained connected cars difficult. In this paper, we present TangleCV, a decentralized technique for secure message sharing and recording for connected vehicles using an approach like Tangle, a directed acyclic graph based blockchain architecture. We introduce an initial design of TangleCV and describe how it provides improved efficiency and scalability against information correctness and information integrity attacks in connected vehicle networks.","publisher":"ACM","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of the ACM Workshop on Automotive Cybersecurity"},"translated_abstract":"Connected vehicles are designed to make informed safety-related decisions based on data/information they receive from various on-board sensors and other vehicles in the vicinity. However, attacks directed towards on-board sensors and network attacks on the wireless communication channels can adversely impact the correctness and integrity of this information and present a grave security and safety challenge to connected vehicles. Current centralized security solutions are not a good fit as they do not scale well and are unable to validate the correctness of the shared data. Decentralizing security provisioning in connected vehicles by means of the upcoming blockchain technology is an interesting alternative for overcoming these limitations. However, current permission-less linear hash-chain based blockchain solutions have low transaction throughput, high computational cost and are resource intensive, thereby making their adoption for designing a security solution for resource constrained connected cars difficult. In this paper, we present TangleCV, a decentralized technique for secure message sharing and recording for connected vehicles using an approach like Tangle, a directed acyclic graph based blockchain architecture. We introduce an initial design of TangleCV and describe how it provides improved efficiency and scalability against information correctness and information integrity attacks in connected vehicle networks.","internal_url":"https://www.academia.edu/78356335/TangleCV","translated_internal_url":"","created_at":"2022-05-03T12:23:23.600-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"TangleCV","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":20178619,"url":"https://dl.acm.org/doi/pdf/10.1145/3309171.3309177"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356334"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356334/Deep_learning_based_security_schemes_for_implantable_medical_devices"><img alt="Research paper thumbnail of Deep learning-based security schemes for implantable medical devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356334/Deep_learning_based_security_schemes_for_implantable_medical_devices">Deep learning-based security schemes for implantable medical devices</a></div><div class="wp-workCard_item"><span>Energy Efficiency of Medical Devices and Healthcare Applications</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns in the data for solving a diverse set of problems such as recognition, classification, and segmentation. It is a neural network-based, biologically inspired model, which has benefitted health, transport, energy, and public safety sectors in diverse ways. It has enabled new potential innovations in these domains, including data analytics, security, treatment, and diagnostics. Intelligent healthcare enables medical specialists to remotely monitor patients, thereby leading to an increase in the popularity of this field in recent years. Doctors are able to provide a better quality of treatment to their patients through a variety of implanted medical devices. The addition of communication ability enables such devices to talk with one another and to the Internet, which leads to the concept of the Internet of Things applied for medical devices. Such devices now have 802.11x or LTE chips on, with the goal that they can converse with one another, in addition to the conventional jobs of sensing and actuating. However, on the other end, the addition of wireless connectivity now makes these devices too prone to be hacked, leading sometimes to lethal events for patients if they are not mitigated. This chapter focuses on how deep learning can be utilized to make these devices more secure while addressing the tradeoffs related to constrained computations, and energy available on such devices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356334"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356334"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356334; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356334]").text(description); $(".js-view-count[data-work-id=78356334]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356334; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356334']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356334, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356334]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356334,"title":"Deep learning-based security schemes for implantable medical devices","translated_title":"","metadata":{"abstract":"Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns in the data for solving a diverse set of problems such as recognition, classification, and segmentation. It is a neural network-based, biologically inspired model, which has benefitted health, transport, energy, and public safety sectors in diverse ways. It has enabled new potential innovations in these domains, including data analytics, security, treatment, and diagnostics. Intelligent healthcare enables medical specialists to remotely monitor patients, thereby leading to an increase in the popularity of this field in recent years. Doctors are able to provide a better quality of treatment to their patients through a variety of implanted medical devices. The addition of communication ability enables such devices to talk with one another and to the Internet, which leads to the concept of the Internet of Things applied for medical devices. Such devices now have 802.11x or LTE chips on, with the goal that they can converse with one another, in addition to the conventional jobs of sensing and actuating. However, on the other end, the addition of wireless connectivity now makes these devices too prone to be hacked, leading sometimes to lethal events for patients if they are not mitigated. This chapter focuses on how deep learning can be utilized to make these devices more secure while addressing the tradeoffs related to constrained computations, and energy available on such devices.","publisher":"Elsevier","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Energy Efficiency of Medical Devices and Healthcare Applications"},"translated_abstract":"Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns in the data for solving a diverse set of problems such as recognition, classification, and segmentation. It is a neural network-based, biologically inspired model, which has benefitted health, transport, energy, and public safety sectors in diverse ways. It has enabled new potential innovations in these domains, including data analytics, security, treatment, and diagnostics. Intelligent healthcare enables medical specialists to remotely monitor patients, thereby leading to an increase in the popularity of this field in recent years. Doctors are able to provide a better quality of treatment to their patients through a variety of implanted medical devices. The addition of communication ability enables such devices to talk with one another and to the Internet, which leads to the concept of the Internet of Things applied for medical devices. Such devices now have 802.11x or LTE chips on, with the goal that they can converse with one another, in addition to the conventional jobs of sensing and actuating. However, on the other end, the addition of wireless connectivity now makes these devices too prone to be hacked, leading sometimes to lethal events for patients if they are not mitigated. This chapter focuses on how deep learning can be utilized to make these devices more secure while addressing the tradeoffs related to constrained computations, and energy available on such devices.","internal_url":"https://www.academia.edu/78356334/Deep_learning_based_security_schemes_for_implantable_medical_devices","translated_internal_url":"","created_at":"2022-05-03T12:23:23.416-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Deep_learning_based_security_schemes_for_implantable_medical_devices","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1131312,"name":"Academic","url":"https://www.academia.edu/Documents/in/Academic"}],"urls":[{"id":20178618,"url":"https://api.elsevier.com/content/article/PII:B9780128190456000066?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356332"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356332/Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations"><img alt="Research paper thumbnail of Using Control Theory and Bayesian Reinforcement Learning for Policy Management in Pandemic Situations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356332/Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations">Using Control Theory and Bayesian Reinforcement Learning for Policy Management in Pandemic Situations</a></div><div class="wp-workCard_item"><span>2021 IEEE International Conference on Communications Workshops (ICC Workshops)</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic outbreak and see how public health policies can be effectively managed to reduce the severe loss of lives and minimize the impact on people’s livelihood. Non-pharmaceutical interventions, such as in-place sheltering and social distancing, are typically introduced to slow the spread (flatten the curve) and reverse the growth of the virus. However, such approaches have the unintended consequences of causing economic activities to plummet and bringing local businesses to a standstill, thereby putting millions of jobs at risk. City administrators have generally resorted to an open loop, belief-based decision-making process, thereby struggling to manage (identify and enforce) timely and optimal policies. To overcome this challenge, this position paper explores a systematically designed, feedback-based strategy, to modulate parameters that control suppression and mitigation. Our work leverages advances in Bayesian Reinforcement Learning algorithms and known techniques in control theory, to stabilize and diminish the rate of propagation in pandemic situations. This paper discusses how offline exploitation using pre-trigger data, online exploration using observations from the environment, and a careful orchestration between the two using granular control of multiple on-off control signals can be used to modulate policy enforcement based on established metrics, such as reproduction number.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356332"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356332"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356332; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356332]").text(description); $(".js-view-count[data-work-id=78356332]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356332; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356332']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356332, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356332]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356332,"title":"Using Control Theory and Bayesian Reinforcement Learning for Policy Management in Pandemic Situations","translated_title":"","metadata":{"abstract":"As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic outbreak and see how public health policies can be effectively managed to reduce the severe loss of lives and minimize the impact on people’s livelihood. Non-pharmaceutical interventions, such as in-place sheltering and social distancing, are typically introduced to slow the spread (flatten the curve) and reverse the growth of the virus. However, such approaches have the unintended consequences of causing economic activities to plummet and bringing local businesses to a standstill, thereby putting millions of jobs at risk. City administrators have generally resorted to an open loop, belief-based decision-making process, thereby struggling to manage (identify and enforce) timely and optimal policies. To overcome this challenge, this position paper explores a systematically designed, feedback-based strategy, to modulate parameters that control suppression and mitigation. Our work leverages advances in Bayesian Reinforcement Learning algorithms and known techniques in control theory, to stabilize and diminish the rate of propagation in pandemic situations. This paper discusses how offline exploitation using pre-trigger data, online exploration using observations from the environment, and a careful orchestration between the two using granular control of multiple on-off control signals can be used to modulate policy enforcement based on established metrics, such as reproduction number.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)"},"translated_abstract":"As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic outbreak and see how public health policies can be effectively managed to reduce the severe loss of lives and minimize the impact on people’s livelihood. Non-pharmaceutical interventions, such as in-place sheltering and social distancing, are typically introduced to slow the spread (flatten the curve) and reverse the growth of the virus. However, such approaches have the unintended consequences of causing economic activities to plummet and bringing local businesses to a standstill, thereby putting millions of jobs at risk. City administrators have generally resorted to an open loop, belief-based decision-making process, thereby struggling to manage (identify and enforce) timely and optimal policies. To overcome this challenge, this position paper explores a systematically designed, feedback-based strategy, to modulate parameters that control suppression and mitigation. Our work leverages advances in Bayesian Reinforcement Learning algorithms and known techniques in control theory, to stabilize and diminish the rate of propagation in pandemic situations. This paper discusses how offline exploitation using pre-trigger data, online exploration using observations from the environment, and a careful orchestration between the two using granular control of multiple on-off control signals can be used to modulate policy enforcement based on established metrics, such as reproduction number.","internal_url":"https://www.academia.edu/78356332/Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations","translated_internal_url":"","created_at":"2022-05-03T12:23:23.274-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1688,"name":"Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Reinforcement_Learning"}],"urls":[{"id":20178617,"url":"http://xplorestaging.ieee.org/ielx7/9473476/9473480/09473604.pdf?arnumber=9473604"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356331"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356331/Neuro_fuzzy_analytics_in_athlete_development_NueroFATH_a_machine_learning_approach"><img alt="Research paper thumbnail of Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356331/Neuro_fuzzy_analytics_in_athlete_development_NueroFATH_a_machine_learning_approach">Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Athletes represent the apex of physical capacity filling in a social picture of performance and b...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356331"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356331"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356331; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356331]").text(description); $(".js-view-count[data-work-id=78356331]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356331; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356331']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356331, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356331]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356331,"title":"Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach","translated_title":"","metadata":{"abstract":"Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Neural Computing and Applications"},"translated_abstract":"Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.","internal_url":"https://www.academia.edu/78356331/Neuro_fuzzy_analytics_in_athlete_development_NueroFATH_a_machine_learning_approach","translated_internal_url":"","created_at":"2022-05-03T12:23:23.118-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Neuro_fuzzy_analytics_in_athlete_development_NueroFATH_a_machine_learning_approach","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"}],"urls":[{"id":20178616,"url":"http://link.springer.com/content/pdf/10.1007/s00521-021-05704-5.pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356329"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356329/A_Survey_of_Blockchain_Enabled_Cyber_Physical_Systems"><img alt="Research paper thumbnail of A Survey of Blockchain Enabled Cyber-Physical Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/85433035/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356329/A_Survey_of_Blockchain_Enabled_Cyber_Physical_Systems">A Survey of Blockchain Enabled Cyber-Physical Systems</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchains and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a decentralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. It has...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="daabaf3dda25d66dea04f6bed71068cb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433035,"asset_id":78356329,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433035/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356329"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356329"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356329; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356329]").text(description); $(".js-view-count[data-work-id=78356329]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356329; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356329']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356329, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "daabaf3dda25d66dea04f6bed71068cb" } } $('.js-work-strip[data-work-id=78356329]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356329,"title":"A Survey of Blockchain Enabled Cyber-Physical Systems","translated_title":"","metadata":{"abstract":"Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356328"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356328/Multi_layer_security_scheme_for_implantable_medical_devices"><img alt="Research paper thumbnail of Multi-layer security scheme for implantable medical devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356328/Multi_layer_security_scheme_for_implantable_medical_devices">Multi-layer security scheme for implantable medical devices</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the area...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356328; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356328]").text(description); $(".js-view-count[data-work-id=78356328]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356328; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356328']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356328, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356328]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356328,"title":"Multi-layer security scheme for implantable medical devices","translated_title":"","metadata":{"abstract":"Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Neural Computing and Applications"},"translated_abstract":"Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.","internal_url":"https://www.academia.edu/78356328/Multi_layer_security_scheme_for_implantable_medical_devices","translated_internal_url":"","created_at":"2022-05-03T12:23:22.646-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Multi_layer_security_scheme_for_implantable_medical_devices","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":20178613,"url":"http://link.springer.com/content/pdf/10.1007/s00521-018-3819-0.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356327/Multi_Layer_Perceptron_Model_on_Chip_for_Secure_Diabetic_Treatment"><img alt="Research paper thumbnail of Multi-Layer Perceptron Model on Chip for Secure Diabetic Treatment" class="work-thumbnail" src="https://attachments.academia-assets.com/85433037/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356327/Multi_Layer_Perceptron_Model_on_Chip_for_Secure_Diabetic_Treatment">Multi-Layer Perceptron Model on Chip for Secure Diabetic Treatment</a></div><div class="wp-workCard_item"><span>IEEE Access</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9fd68528b76da12122de5efe797a0437" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433037,"asset_id":78356327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433037/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356327; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356327]").text(description); $(".js-view-count[data-work-id=78356327]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356327; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356327']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356327, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9fd68528b76da12122de5efe797a0437" } } $('.js-work-strip[data-work-id=78356327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356327,"title":"Multi-Layer Perceptron Model on Chip for Secure Diabetic Treatment","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","grobid_abstract":"Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for the infusion of insulin to control blood glucose level. While these devices offer many clinical benefits, there has been a recent increase in the number of cases, wherein, the wireless communication channel of such devices has been compromised. This not only causes the device to malfunction but also potentially threatens the patient's life. In this paper, a neural networks-based multi-layer perceptron model was designed for real-time medical device security. Machine learning algorithms are among the most effective and broadly utilized systems for classification, identification, and segmentation. Although they are effective, they are both computationally and memory intensive, making them hard to be deployed on low-power embedded frameworks. In this paper, we present an on-chip neural system network for securing diabetic treatment. The model achieved 98.1% accuracy in classifying fake versus genuine glucose measurements. The proposed model was comparatively evaluated with a linear support vector machine which achieved only 90.17% accuracy with negligible precision and recall. Moreover, the proposal estimates the reliability of the framework through the use of the Bayesian network. The proposed approach enhances the reliability of the overall framework by 18% when only one device is secured, and over 90% when all devices are secured. INDEX TERMS Security, machine learning, insulin pumps, deep learning, implantable medical devices.","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"IEEE Access","grobid_abstract_attachment_id":85433037},"translated_abstract":null,"internal_url":"https://www.academia.edu/78356327/Multi_Layer_Perceptron_Model_on_Chip_for_Secure_Diabetic_Treatment","translated_internal_url":"","created_at":"2022-05-03T12:23:22.497-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85433037,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433037/thumbnails/1.jpg","file_name":"08409455.pdf","download_url":"https://www.academia.edu/attachments/85433037/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_Layer_Perceptron_Model_on_Chip_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433037/08409455-libre.pdf?1651606133=\u0026response-content-disposition=attachment%3B+filename%3DMulti_Layer_Perceptron_Model_on_Chip_for.pdf\u0026Expires=1732471282\u0026Signature=KO86pIIs0iBryrielntJEbdaC99RwWfZ-sK--6aI7irg95CGSa0tTP2r3Vwr1jPSGTgQarUoRAu6ofb8zRvGO10jbydLYZt5oESdvuLCNQWVBfinmxxpoUZJQwFfDprYQlCOfwLJ98EHV98hYgT4kXeklHG6Vq0B67nEgEQKrPq0FdHdnk0JEkov6-X1iqOZCsY~oeSFTwGh1eOHSZRSR7SmqfoBxxg~V~G7lCaIErlpkW-14XflCyQ-OLCaJ1th1E3XrL~LJ84vBvKRvDLzjnJCQXKdN1ziJz9r8217t0Da8ViEPdYoD71Mh4ofj22t3To50V8Hf8nDN0uRltc~NA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Multi_Layer_Perceptron_Model_on_Chip_for_Secure_Diabetic_Treatment","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[{"id":85433037,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433037/thumbnails/1.jpg","file_name":"08409455.pdf","download_url":"https://www.academia.edu/attachments/85433037/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Multi_Layer_Perceptron_Model_on_Chip_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433037/08409455-libre.pdf?1651606133=\u0026response-content-disposition=attachment%3B+filename%3DMulti_Layer_Perceptron_Model_on_Chip_for.pdf\u0026Expires=1732471282\u0026Signature=KO86pIIs0iBryrielntJEbdaC99RwWfZ-sK--6aI7irg95CGSa0tTP2r3Vwr1jPSGTgQarUoRAu6ofb8zRvGO10jbydLYZt5oESdvuLCNQWVBfinmxxpoUZJQwFfDprYQlCOfwLJ98EHV98hYgT4kXeklHG6Vq0B67nEgEQKrPq0FdHdnk0JEkov6-X1iqOZCsY~oeSFTwGh1eOHSZRSR7SmqfoBxxg~V~G7lCaIErlpkW-14XflCyQ-OLCaJ1th1E3XrL~LJ84vBvKRvDLzjnJCQXKdN1ziJz9r8217t0Da8ViEPdYoD71Mh4ofj22t3To50V8Hf8nDN0uRltc~NA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":85433040,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433040/thumbnails/1.jpg","file_name":"08409455.pdf","download_url":"https://www.academia.edu/attachments/85433040/download_file","bulk_download_file_name":"Multi_Layer_Perceptron_Model_on_Chip_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433040/08409455-libre.pdf?1651606132=\u0026response-content-disposition=attachment%3B+filename%3DMulti_Layer_Perceptron_Model_on_Chip_for.pdf\u0026Expires=1732471282\u0026Signature=dF9fw6Kz4iTUxIXZpSysTiDncjVHrhU6B71l5KNbl1oLDPxV5Hc6DJULmCBA9E0wKoMLRlK2szpvg51HpYbqIRMPBGfOXScw0WFUigX1-SMaBZPTmine4wCc-c--~PIGjmhNElUeth72La6NnTJpvCQBFyPyhfzk2YeOuzvQDzR2z581NnSaf0UsOImErAcioLMwBOryDC3RyHPdeE8jWMR7PC3cmb0OeW-NrjvmKENc5vw6gjX-WeKmkUtrf~SyqWT9HX-RorG9~fR4JlWC1VKJOy2fMYdvV8SUJy4U2hCJ4EN7isOXrcS~ybxUNyCWzpBy1B4jpJzQQ9Ek2ft4SQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":238159,"name":"Multilayer Perceptron","url":"https://www.academia.edu/Documents/in/Multilayer_Perceptron"},{"id":1004747,"name":"Perceptron","url":"https://www.academia.edu/Documents/in/Perceptron"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":20178612,"url":"http://xplorestaging.ieee.org/ielx7/6287639/8274985/08409455.pdf?arnumber=8409455"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356323"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356323/TangleCV"><img alt="Research paper thumbnail of TangleCV" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356323/TangleCV">TangleCV</a></div><div class="wp-workCard_item"><span>ACM Transactions on Cyber-Physical Systems</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Connected vehicles are set to define the future of transportation; however, this upcoming technol...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Connected vehicles are set to define the future of transportation; however, this upcoming technology continues to be plagued with serious security risks. If these risks are not addressed in a timely fashion, then they could threaten the adoption and success of this promising technology. This article deals with a specific class of attacks in connected vehicles, namely tampering attacks caused due to compromise of on-board sensors. Current centralized solutions that employ trusted infrastructure to protect against adversarial manipulation of information cannot validate the correctness of the shared data and do not scale well. To overcome these issues, decentralized protection mechanisms by means of blockchain technology have emerged as a promising research direction. However, current permission-less, linear blockchain-based solutions have low transaction performance and high computational cost, thereby making it difficult to adopt them for security in connected vehicles. 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In this artic...","publisher":"Association for Computing Machinery (ACM)","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"ACM Transactions on Cyber-Physical Systems"},"translated_abstract":"Connected vehicles are set to define the future of transportation; however, this upcoming technology continues to be plagued with serious security risks. If these risks are not addressed in a timely fashion, then they could threaten the adoption and success of this promising technology. This article deals with a specific class of attacks in connected vehicles, namely tampering attacks caused due to compromise of on-board sensors. Current centralized solutions that employ trusted infrastructure to protect against adversarial manipulation of information cannot validate the correctness of the shared data and do not scale well. To overcome these issues, decentralized protection mechanisms by means of blockchain technology have emerged as a promising research direction. However, current permission-less, linear blockchain-based solutions have low transaction performance and high computational cost, thereby making it difficult to adopt them for security in connected vehicles. In this artic...","internal_url":"https://www.academia.edu/78356323/TangleCV","translated_internal_url":"","created_at":"2022-05-03T12:23:16.055-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"TangleCV","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[],"urls":[{"id":20178608,"url":"https://dl.acm.org/doi/pdf/10.1145/3404500"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="2619380" id="papers"><div class="js-work-strip profile--work_container" data-work-id="78356346"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356346/A_review_of_security_challenges_attacks_and_resolutions_for_wireless_medical_devices"><img alt="Research paper thumbnail of A review of security challenges, attacks and resolutions for wireless medical devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356346/A_review_of_security_challenges_attacks_and_resolutions_for_wireless_medical_devices">A review of security challenges, attacks and resolutions for wireless medical devices</a></div><div class="wp-workCard_item"><span>2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Evolution of implantable medical devices for human beings has provided a radical new way for trea...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356346"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356346"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356346; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356346]").text(description); $(".js-view-count[data-work-id=78356346]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356346; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356346']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356346, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356346]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356346,"title":"A review of security challenges, attacks and resolutions for wireless medical devices","translated_title":"","metadata":{"abstract":"Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)"},"translated_abstract":"Evolution of implantable medical devices for human beings has provided a radical new way for treating chronic diseases such as diabetes, cardiac arrhythmia, cochlear, gastric diseases etc. Implantable medical devices have provided a breakthrough in network transformation by enabling and accessing the technology on demand. However, with the advancement of these devices with respect to wireless communication and ability for outside caregiver to communicate wirelessly have increased its potential to impact the security, and breach in privacy of human beings. There are several vulnerable threats in wireless medical devices such as information harvesting, tracking the patient, impersonation, relaying attacks and denial of service attack. These threats violate confidentiality, integrity, availability properties of these devices. For securing implantable medical devices diverse solutions have been proposed ranging from machine learning techniques to hardware technologies. The present survey paper focusses on the challenges, threats and solutions pertaining to the privacy and safety issues of medical devices.","internal_url":"https://www.academia.edu/78356346/A_review_of_security_challenges_attacks_and_resolutions_for_wireless_medical_devices","translated_internal_url":"","created_at":"2022-05-03T12:23:25.018-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_review_of_security_challenges_attacks_and_resolutions_for_wireless_medical_devices","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":2345,"name":"Wireless Communications","url":"https://www.academia.edu/Documents/in/Wireless_Communications"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20178629,"url":"http://xplorestaging.ieee.org/ielx7/7975134/7986245/07986505.pdf?arnumber=7986505"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356345"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356345/DLRT_Deep_Learning_Approach_for_Reliable_Diabetic_Treatment"><img alt="Research paper thumbnail of DLRT: Deep Learning Approach for Reliable Diabetic Treatment" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356345/DLRT_Deep_Learning_Approach_for_Reliable_Diabetic_Treatment">DLRT: Deep Learning Approach for Reliable Diabetic Treatment</a></div><div class="wp-workCard_item"><span>GLOBECOM 2017 - 2017 IEEE Global Communications Conference</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today,...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356345"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356345"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356345; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356345]").text(description); $(".js-view-count[data-work-id=78356345]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356345; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356345']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356345, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356345]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356345,"title":"DLRT: Deep Learning Approach for Reliable Diabetic Treatment","translated_title":"","metadata":{"abstract":"Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"GLOBECOM 2017 - 2017 IEEE Global Communications Conference"},"translated_abstract":"Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.","internal_url":"https://www.academia.edu/78356345/DLRT_Deep_Learning_Approach_for_Reliable_Diabetic_Treatment","translated_internal_url":"","created_at":"2022-05-03T12:23:24.841-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"DLRT_Deep_Learning_Approach_for_Reliable_Diabetic_Treatment","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20178628,"url":"http://xplorestaging.ieee.org/ielx7/8253768/8253909/08255028.pdf?arnumber=8255028"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356344"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356344/Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks"><img alt="Research paper thumbnail of Mathematical Evaluation of Human Immune Systems For Securing Software Defined Networks" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356344/Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks">Mathematical Evaluation of Human Immune Systems For Securing Software Defined Networks</a></div><div class="wp-workCard_item"><span>2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM)</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The immune system of the human body has massive potential in defending it against multiple harmfu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356344"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356344"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356344; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356344]").text(description); $(".js-view-count[data-work-id=78356344]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356344; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356344']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356344, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356344]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356344,"title":"Mathematical Evaluation of Human Immune Systems For Securing Software Defined Networks","translated_title":"","metadata":{"abstract":"The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM)"},"translated_abstract":"The immune system of the human body has massive potential in defending it against multiple harmful viruses and foreign bodies. All through their developmental history, human beings have been contaminated by micro-organisms. In order to restrict the nature, size, and intensity of these microbial invasions, human beings have inherent capabilities to deal with them. The human immune system is capable of protecting the body in the form of external barriers such as skin, cells, and tissues. Furthermore, it is capable of differentiating among the self and the non-self cells with the distinct properties and features that infiltrate the human body. This paper presents a case study of the human immune system in which we develop mathematical models of innate and adaptive immune system. Extensive simulations were carried out to study the effect of the foreign particles when the recovery mechanism occurs in the body. The results obtained, substantiate the reliability of the human immune mathematical model. Finally, we advocate that having a strong security and privacy around the human body can contribute in building a strong network system. For instance, the two layer immune inspired framework viz innate layer and adaptive layer can be instigated at the data layer and the control layer of Software Defined Networking respectively.","internal_url":"https://www.academia.edu/78356344/Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks","translated_internal_url":"","created_at":"2022-05-03T12:23:24.690-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Mathematical_Evaluation_of_Human_Immune_Systems_For_Securing_Software_Defined_Networks","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20178627,"url":"http://xplorestaging.ieee.org/ielx7/8622653/8629585/08629728.pdf?arnumber=8629728"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356341"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356341/Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework"><img alt="Research paper thumbnail of Invited Paper: mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using a Smart Edge Framework" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356341/Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework">Invited Paper: mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using a Smart Edge Framework</a></div><div class="wp-workCard_item"><span>2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases, it is practical and efficient to manage and process test configuration and data analysis as close to the testbed as possible (inline) instead of offline compute platforms. We present mmMoReEdge, a mmWave modular and reconfigurable testbed inspired by a smart edge networking and communication framework, typically found in IoT devices. In mmMoReEdge, complex signal processing is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. Using a mathematical model for processing time (the proposed figure of merit), we present results which show that mmMoReEdge is 50% to 70% faster as compared to an offline general-purpose processor based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356341"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356341"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356341; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356341]").text(description); $(".js-view-count[data-work-id=78356341]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356341; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356341']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356341, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356341]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356341,"title":"Invited Paper: mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using a Smart Edge Framework","translated_title":"","metadata":{"abstract":"As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases, it is practical and efficient to manage and process test configuration and data analysis as close to the testbed as possible (inline) instead of offline compute platforms. We present mmMoReEdge, a mmWave modular and reconfigurable testbed inspired by a smart edge networking and communication framework, typically found in IoT devices. In mmMoReEdge, complex signal processing is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. Using a mathematical model for processing time (the proposed figure of merit), we present results which show that mmMoReEdge is 50% to 70% faster as compared to an offline general-purpose processor based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)"},"translated_abstract":"As the complexity of hardware (sensors, components, antennas) and software (algorithms) increases, it is practical and efficient to manage and process test configuration and data analysis as close to the testbed as possible (inline) instead of offline compute platforms. We present mmMoReEdge, a mmWave modular and reconfigurable testbed inspired by a smart edge networking and communication framework, typically found in IoT devices. In mmMoReEdge, complex signal processing is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. Using a mathematical model for processing time (the proposed figure of merit), we present results which show that mmMoReEdge is 50% to 70% faster as compared to an offline general-purpose processor based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.","internal_url":"https://www.academia.edu/78356341/Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework","translated_internal_url":"","created_at":"2022-05-03T12:23:24.428-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Invited_Paper_mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_a_Smart_Edge_Framework","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE"}],"urls":[{"id":20178624,"url":"http://xplorestaging.ieee.org/ielx7/9145943/9156071/09156144.pdf?arnumber=9156144"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356340"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356340/Towards_a_Practical_Pedestrian_Distraction_Detection_Framework_using_Wearables"><img alt="Research paper thumbnail of Towards a Practical Pedestrian Distraction Detection Framework using Wearables" class="work-thumbnail" src="https://attachments.academia-assets.com/85433105/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356340/Towards_a_Practical_Pedestrian_Distraction_Detection_Framework_using_Wearables">Towards a Practical Pedestrian Distraction Detection Framework using Wearables</a></div><div class="wp-workCard_item"><span>2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e87b3c9de2c134f4fd67bfd63e61b7a0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433105,"asset_id":78356340,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433105/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356340"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356340"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356340; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356340]").text(description); $(".js-view-count[data-work-id=78356340]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356340; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356340']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356340, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356339"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356339/Blockchain_applications_for_healthcare"><img alt="Research paper thumbnail of Blockchain applications for healthcare" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356339/Blockchain_applications_for_healthcare">Blockchain applications for healthcare</a></div><div class="wp-workCard_item"><span>Energy Efficiency of Medical Devices and Healthcare Applications</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Healthcare systems control and monitor the health of patients with the help of advanced ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Healthcare systems control and monitor the health of patients with the help of advanced technologies. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchain and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a de-centralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient, and enables the provision of financial services, thereby giving users more freedom and power. This emerging technology provides internet users the capability to create value and authenticate the digital information. It has the capability to revolutionize a diverse set of business applications ranging from sharing economy to data management and prediction markets. In this paper, we present a survey of blockchain applications in healthcare. Healthcare systems play a very crucial role in people&#39;s life and there are diverse ways by which it can benefit from the blockchain technology and have been discussed in the chapter. The survey results demonstrate that Blockchain has distinct advantages for healthcare applications as compared to other applications. The benefits of blockchain can be further amplified by using a light-weight distributed ledger system like IOTA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356339"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356339"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356339; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356339]").text(description); $(".js-view-count[data-work-id=78356339]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356339; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356339']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356339, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356339]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356339,"title":"Blockchain applications for healthcare","translated_title":"","metadata":{"abstract":"Abstract Healthcare systems control and monitor the health of patients with the help of advanced technologies. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchain and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a de-centralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient, and enables the provision of financial services, thereby giving users more freedom and power. This emerging technology provides internet users the capability to create value and authenticate the digital information. It has the capability to revolutionize a diverse set of business applications ranging from sharing economy to data management and prediction markets. In this paper, we present a survey of blockchain applications in healthcare. Healthcare systems play a very crucial role in people\u0026#39;s life and there are diverse ways by which it can benefit from the blockchain technology and have been discussed in the chapter. The survey results demonstrate that Blockchain has distinct advantages for healthcare applications as compared to other applications. The benefits of blockchain can be further amplified by using a light-weight distributed ledger system like IOTA.","publisher":"Elsevier","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Energy Efficiency of Medical Devices and Healthcare Applications"},"translated_abstract":"Abstract Healthcare systems control and monitor the health of patients with the help of advanced technologies. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchain and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a de-centralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient, and enables the provision of financial services, thereby giving users more freedom and power. This emerging technology provides internet users the capability to create value and authenticate the digital information. It has the capability to revolutionize a diverse set of business applications ranging from sharing economy to data management and prediction markets. In this paper, we present a survey of blockchain applications in healthcare. Healthcare systems play a very crucial role in people\u0026#39;s life and there are diverse ways by which it can benefit from the blockchain technology and have been discussed in the chapter. The survey results demonstrate that Blockchain has distinct advantages for healthcare applications as compared to other applications. The benefits of blockchain can be further amplified by using a light-weight distributed ledger system like IOTA.","internal_url":"https://www.academia.edu/78356339/Blockchain_applications_for_healthcare","translated_internal_url":"","created_at":"2022-05-03T12:23:24.113-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Blockchain_applications_for_healthcare","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1131312,"name":"Academic","url":"https://www.academia.edu/Documents/in/Academic"}],"urls":[{"id":20178622,"url":"https://api.elsevier.com/content/article/PII:B978012819045600008X?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356338"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356338/mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_an_Edge_Inspired_Architecture"><img alt="Research paper thumbnail of mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using an Edge-Inspired Architecture" class="work-thumbnail" src="https://attachments.academia-assets.com/85433042/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356338/mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_an_Edge_Inspired_Architecture">mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using an Edge-Inspired Architecture</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing arc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing architecture found in IoT devices. In mmMoReEdge, complex signal processing, typically required for 5G testing, is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. We present simulation results that show that mmMoReEdge is 50% to 70% faster as compared to an offline CPU-based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5fa409609fcf9226ba729f821c44fccf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433042,"asset_id":78356338,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433042/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356338"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356338"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356338; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356338]").text(description); $(".js-view-count[data-work-id=78356338]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356338; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356338']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356338, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5fa409609fcf9226ba729f821c44fccf" } } $('.js-work-strip[data-work-id=78356338]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356338,"title":"mmMoReEdge: A mmWave Modular and Reconfigurable Testbed Design using an Edge-Inspired Architecture","translated_title":"","metadata":{"abstract":"We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing architecture found in IoT devices. In mmMoReEdge, complex signal processing, typically required for 5G testing, is performed on the edge (local servers in close proximity) of a group of testbed nodes. mmMoReEdge offers modularity via configuration of phased-array antennas, RF front ends, ADC, and DAC, while the edge processing provides reconfigurability via scalable inline processing. We present simulation results that show that mmMoReEdge is 50% to 70% faster as compared to an offline CPU-based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"We present mmMoReEdge, a modular and reconfigurable mmWave testbed inspired by edge computing architecture found in IoT devices. 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We present simulation results that show that mmMoReEdge is 50% to 70% faster as compared to an offline CPU-based architecture and is 30% to 40% faster as compared to a node-based architecture with one FPGA.","internal_url":"https://www.academia.edu/78356338/mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_an_Edge_Inspired_Architecture","translated_internal_url":"","created_at":"2022-05-03T12:23:23.980-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":85433042,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433042/thumbnails/1.jpg","file_name":"mmMoReEdge_PerCom_SmartEdge_2020.pdf","download_url":"https://www.academia.edu/attachments/85433042/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"mmMoReEdge_A_mmWave_Modular_and_Reconfig.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433042/mmMoReEdge_PerCom_SmartEdge_2020-libre.pdf?1651606128=\u0026response-content-disposition=attachment%3B+filename%3DmmMoReEdge_A_mmWave_Modular_and_Reconfig.pdf\u0026Expires=1732471281\u0026Signature=XC8C4jjrv~sTfeJCAYwSIpa~twR1gyyLMx9miS3Zh6uZiyRjhDBrWMMH5xoOSzqQqxsi1NG~Gt43GUkPmWmMq1L4F1xuv6VXtnVjQ0dpx8TtOMQpvX11qsYxoBxj-UjJzd3C3Vtif~TpHOmLAUf~wuZikVKIGuMiq79PSr13LRCG5rrrhVGM7Y-TzSer0jZaWDCMN1~QtEceNBgFzxD1YwnNa-cDxPp~ecuUiIOJTiiztScn4WJdn7DivgmDHWH-17p1ho7eK4Dukf7duMy3YCtMWMMmHSYb1ZgHadezGgMWyOs-6y6jLOkJkty25goghtH5umuOkVnjMVK~R9lIFg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"mmMoReEdge_A_mmWave_Modular_and_Reconfigurable_Testbed_Design_using_an_Edge_Inspired_Architecture","translated_slug":"","page_count":7,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[{"id":85433042,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433042/thumbnails/1.jpg","file_name":"mmMoReEdge_PerCom_SmartEdge_2020.pdf","download_url":"https://www.academia.edu/attachments/85433042/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"mmMoReEdge_A_mmWave_Modular_and_Reconfig.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433042/mmMoReEdge_PerCom_SmartEdge_2020-libre.pdf?1651606128=\u0026response-content-disposition=attachment%3B+filename%3DmmMoReEdge_A_mmWave_Modular_and_Reconfig.pdf\u0026Expires=1732471281\u0026Signature=XC8C4jjrv~sTfeJCAYwSIpa~twR1gyyLMx9miS3Zh6uZiyRjhDBrWMMH5xoOSzqQqxsi1NG~Gt43GUkPmWmMq1L4F1xuv6VXtnVjQ0dpx8TtOMQpvX11qsYxoBxj-UjJzd3C3Vtif~TpHOmLAUf~wuZikVKIGuMiq79PSr13LRCG5rrrhVGM7Y-TzSer0jZaWDCMN1~QtEceNBgFzxD1YwnNa-cDxPp~ecuUiIOJTiiztScn4WJdn7DivgmDHWH-17p1ho7eK4Dukf7duMy3YCtMWMMmHSYb1ZgHadezGgMWyOs-6y6jLOkJkty25goghtH5umuOkVnjMVK~R9lIFg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":85433041,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/85433041/thumbnails/1.jpg","file_name":"mmMoReEdge_PerCom_SmartEdge_2020.pdf","download_url":"https://www.academia.edu/attachments/85433041/download_file","bulk_download_file_name":"mmMoReEdge_A_mmWave_Modular_and_Reconfig.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/85433041/mmMoReEdge_PerCom_SmartEdge_2020-libre.pdf?1651606128=\u0026response-content-disposition=attachment%3B+filename%3DmmMoReEdge_A_mmWave_Modular_and_Reconfig.pdf\u0026Expires=1732471281\u0026Signature=ZU5ajJK92JQn1pCaUfEKtWpqb9h7Fd10q7vJoLdMjuwWYXeDk7dNSHL4030L5V4RdctvbKhQfbC2XvxG9vV2eZ~fLmQhAVH0mNmzgjMkGffozwA5~rB5o9bySWlu61wPCEPBe9ie~P3z0a5yDeu53Pr1YVLnTk~fwsxsP9vdxu7MK5uKj~QAKpZRPtOVdS5vdKH2um6cgZdGeI-FELbo9NS8od277R8lJ~VIal1o7MG7I4O8HmxpTE1SEDIX84g9NK5jWZJ4bnfg894088DcOsO0Myg9SzGl-WxaW71pZNJ-T4rN6izN2yT3RV5OfZLpb9uLicIyxOFs83Dtz0rDFA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[{"id":20178621,"url":"https://s2.smu.edu/~camp/pubs/mmMoReEdge_PerCom_SmartEdge_2020.pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356336"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356336/A_Bio_Inspired_Framework_to_Mitigate_DoS_Attacks_in_Software_Defined_Networking"><img alt="Research paper thumbnail of A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking" class="work-thumbnail" src="https://attachments.academia-assets.com/85433102/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356336/A_Bio_Inspired_Framework_to_Mitigate_DoS_Attacks_in_Software_Defined_Networking">A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking</a></div><div class="wp-workCard_item"><span>2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Software Defined Networking (SDN) is an emerging architecture providing services on a priority ba...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer an...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="85cc92cd02c60942fcbfc520b3c4b216" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433102,"asset_id":78356336,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433102/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NzY4MSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356336"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356336"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356336; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356336]").text(description); $(".js-view-count[data-work-id=78356336]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356336; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356336']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356336, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "85cc92cd02c60942fcbfc520b3c4b216" } } $('.js-work-strip[data-work-id=78356336]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356336,"title":"A Bio-Inspired Framework to Mitigate DoS Attacks in Software Defined Networking","translated_title":"","metadata":{"abstract":"Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. 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The two layer immune inspired framework, viz innate layer an...","publisher":"2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS)"},"translated_abstract":"Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. 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However, attacks directed towards on-board sensors and network attacks on the wireless communication channels can adversely impact the correctness and integrity of this information and present a grave security and safety challenge to connected vehicles. Current centralized security solutions are not a good fit as they do not scale well and are unable to validate the correctness of the shared data. Decentralizing security provisioning in connected vehicles by means of the upcoming blockchain technology is an interesting alternative for overcoming these limitations. However, current permission-less linear hash-chain based blockchain solutions have low transaction throughput, high computational cost and are resource intensive, thereby making their adoption for designing a security solution for resource constrained connected cars difficult. In this paper, we present TangleCV, a decentralized technique for secure message sharing and recording for connected vehicles using an approach like Tangle, a directed acyclic graph based blockchain architecture. We introduce an initial design of TangleCV and describe how it provides improved efficiency and scalability against information correctness and information integrity attacks in connected vehicle networks.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356335"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356335"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356335; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356335]").text(description); $(".js-view-count[data-work-id=78356335]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356335; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356335']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356335, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356335]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356335,"title":"TangleCV","translated_title":"","metadata":{"abstract":"Connected vehicles are designed to make informed safety-related decisions based on data/information they receive from various on-board sensors and other vehicles in the vicinity. However, attacks directed towards on-board sensors and network attacks on the wireless communication channels can adversely impact the correctness and integrity of this information and present a grave security and safety challenge to connected vehicles. Current centralized security solutions are not a good fit as they do not scale well and are unable to validate the correctness of the shared data. Decentralizing security provisioning in connected vehicles by means of the upcoming blockchain technology is an interesting alternative for overcoming these limitations. However, current permission-less linear hash-chain based blockchain solutions have low transaction throughput, high computational cost and are resource intensive, thereby making their adoption for designing a security solution for resource constrained connected cars difficult. In this paper, we present TangleCV, a decentralized technique for secure message sharing and recording for connected vehicles using an approach like Tangle, a directed acyclic graph based blockchain architecture. We introduce an initial design of TangleCV and describe how it provides improved efficiency and scalability against information correctness and information integrity attacks in connected vehicle networks.","publisher":"ACM","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of the ACM Workshop on Automotive Cybersecurity"},"translated_abstract":"Connected vehicles are designed to make informed safety-related decisions based on data/information they receive from various on-board sensors and other vehicles in the vicinity. However, attacks directed towards on-board sensors and network attacks on the wireless communication channels can adversely impact the correctness and integrity of this information and present a grave security and safety challenge to connected vehicles. Current centralized security solutions are not a good fit as they do not scale well and are unable to validate the correctness of the shared data. Decentralizing security provisioning in connected vehicles by means of the upcoming blockchain technology is an interesting alternative for overcoming these limitations. However, current permission-less linear hash-chain based blockchain solutions have low transaction throughput, high computational cost and are resource intensive, thereby making their adoption for designing a security solution for resource constrained connected cars difficult. In this paper, we present TangleCV, a decentralized technique for secure message sharing and recording for connected vehicles using an approach like Tangle, a directed acyclic graph based blockchain architecture. We introduce an initial design of TangleCV and describe how it provides improved efficiency and scalability against information correctness and information integrity attacks in connected vehicle networks.","internal_url":"https://www.academia.edu/78356335/TangleCV","translated_internal_url":"","created_at":"2022-05-03T12:23:23.600-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"TangleCV","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":20178619,"url":"https://dl.acm.org/doi/pdf/10.1145/3309171.3309177"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356334"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356334/Deep_learning_based_security_schemes_for_implantable_medical_devices"><img alt="Research paper thumbnail of Deep learning-based security schemes for implantable medical devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356334/Deep_learning_based_security_schemes_for_implantable_medical_devices">Deep learning-based security schemes for implantable medical devices</a></div><div class="wp-workCard_item"><span>Energy Efficiency of Medical Devices and Healthcare Applications</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns in the data for solving a diverse set of problems such as recognition, classification, and segmentation. It is a neural network-based, biologically inspired model, which has benefitted health, transport, energy, and public safety sectors in diverse ways. It has enabled new potential innovations in these domains, including data analytics, security, treatment, and diagnostics. Intelligent healthcare enables medical specialists to remotely monitor patients, thereby leading to an increase in the popularity of this field in recent years. Doctors are able to provide a better quality of treatment to their patients through a variety of implanted medical devices. The addition of communication ability enables such devices to talk with one another and to the Internet, which leads to the concept of the Internet of Things applied for medical devices. Such devices now have 802.11x or LTE chips on, with the goal that they can converse with one another, in addition to the conventional jobs of sensing and actuating. However, on the other end, the addition of wireless connectivity now makes these devices too prone to be hacked, leading sometimes to lethal events for patients if they are not mitigated. This chapter focuses on how deep learning can be utilized to make these devices more secure while addressing the tradeoffs related to constrained computations, and energy available on such devices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356334"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356334"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356334; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356334]").text(description); $(".js-view-count[data-work-id=78356334]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356334; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356334']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356334, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356334]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356334,"title":"Deep learning-based security schemes for implantable medical devices","translated_title":"","metadata":{"abstract":"Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns in the data for solving a diverse set of problems such as recognition, classification, and segmentation. It is a neural network-based, biologically inspired model, which has benefitted health, transport, energy, and public safety sectors in diverse ways. It has enabled new potential innovations in these domains, including data analytics, security, treatment, and diagnostics. Intelligent healthcare enables medical specialists to remotely monitor patients, thereby leading to an increase in the popularity of this field in recent years. Doctors are able to provide a better quality of treatment to their patients through a variety of implanted medical devices. The addition of communication ability enables such devices to talk with one another and to the Internet, which leads to the concept of the Internet of Things applied for medical devices. Such devices now have 802.11x or LTE chips on, with the goal that they can converse with one another, in addition to the conventional jobs of sensing and actuating. However, on the other end, the addition of wireless connectivity now makes these devices too prone to be hacked, leading sometimes to lethal events for patients if they are not mitigated. This chapter focuses on how deep learning can be utilized to make these devices more secure while addressing the tradeoffs related to constrained computations, and energy available on such devices.","publisher":"Elsevier","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Energy Efficiency of Medical Devices and Healthcare Applications"},"translated_abstract":"Abstract Deep learning is a subset of machine learning, which learns from the inherent patterns in the data for solving a diverse set of problems such as recognition, classification, and segmentation. It is a neural network-based, biologically inspired model, which has benefitted health, transport, energy, and public safety sectors in diverse ways. It has enabled new potential innovations in these domains, including data analytics, security, treatment, and diagnostics. Intelligent healthcare enables medical specialists to remotely monitor patients, thereby leading to an increase in the popularity of this field in recent years. Doctors are able to provide a better quality of treatment to their patients through a variety of implanted medical devices. The addition of communication ability enables such devices to talk with one another and to the Internet, which leads to the concept of the Internet of Things applied for medical devices. Such devices now have 802.11x or LTE chips on, with the goal that they can converse with one another, in addition to the conventional jobs of sensing and actuating. However, on the other end, the addition of wireless connectivity now makes these devices too prone to be hacked, leading sometimes to lethal events for patients if they are not mitigated. This chapter focuses on how deep learning can be utilized to make these devices more secure while addressing the tradeoffs related to constrained computations, and energy available on such devices.","internal_url":"https://www.academia.edu/78356334/Deep_learning_based_security_schemes_for_implantable_medical_devices","translated_internal_url":"","created_at":"2022-05-03T12:23:23.416-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Deep_learning_based_security_schemes_for_implantable_medical_devices","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1131312,"name":"Academic","url":"https://www.academia.edu/Documents/in/Academic"}],"urls":[{"id":20178618,"url":"https://api.elsevier.com/content/article/PII:B9780128190456000066?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356332"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356332/Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations"><img alt="Research paper thumbnail of Using Control Theory and Bayesian Reinforcement Learning for Policy Management in Pandemic Situations" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356332/Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations">Using Control Theory and Bayesian Reinforcement Learning for Policy Management in Pandemic Situations</a></div><div class="wp-workCard_item"><span>2021 IEEE International Conference on Communications Workshops (ICC Workshops)</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic o...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic outbreak and see how public health policies can be effectively managed to reduce the severe loss of lives and minimize the impact on people’s livelihood. Non-pharmaceutical interventions, such as in-place sheltering and social distancing, are typically introduced to slow the spread (flatten the curve) and reverse the growth of the virus. However, such approaches have the unintended consequences of causing economic activities to plummet and bringing local businesses to a standstill, thereby putting millions of jobs at risk. City administrators have generally resorted to an open loop, belief-based decision-making process, thereby struggling to manage (identify and enforce) timely and optimal policies. To overcome this challenge, this position paper explores a systematically designed, feedback-based strategy, to modulate parameters that control suppression and mitigation. Our work leverages advances in Bayesian Reinforcement Learning algorithms and known techniques in control theory, to stabilize and diminish the rate of propagation in pandemic situations. This paper discusses how offline exploitation using pre-trigger data, online exploration using observations from the environment, and a careful orchestration between the two using granular control of multiple on-off control signals can be used to modulate policy enforcement based on established metrics, such as reproduction number.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356332"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356332"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356332; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356332]").text(description); $(".js-view-count[data-work-id=78356332]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356332; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356332']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356332, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356332]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356332,"title":"Using Control Theory and Bayesian Reinforcement Learning for Policy Management in Pandemic Situations","translated_title":"","metadata":{"abstract":"As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic outbreak and see how public health policies can be effectively managed to reduce the severe loss of lives and minimize the impact on people’s livelihood. Non-pharmaceutical interventions, such as in-place sheltering and social distancing, are typically introduced to slow the spread (flatten the curve) and reverse the growth of the virus. However, such approaches have the unintended consequences of causing economic activities to plummet and bringing local businesses to a standstill, thereby putting millions of jobs at risk. City administrators have generally resorted to an open loop, belief-based decision-making process, thereby struggling to manage (identify and enforce) timely and optimal policies. To overcome this challenge, this position paper explores a systematically designed, feedback-based strategy, to modulate parameters that control suppression and mitigation. Our work leverages advances in Bayesian Reinforcement Learning algorithms and known techniques in control theory, to stabilize and diminish the rate of propagation in pandemic situations. This paper discusses how offline exploitation using pre-trigger data, online exploration using observations from the environment, and a careful orchestration between the two using granular control of multiple on-off control signals can be used to modulate policy enforcement based on established metrics, such as reproduction number.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)"},"translated_abstract":"As engineers and scientists, it is our responsibility to learn lessons from the recent pandemic outbreak and see how public health policies can be effectively managed to reduce the severe loss of lives and minimize the impact on people’s livelihood. Non-pharmaceutical interventions, such as in-place sheltering and social distancing, are typically introduced to slow the spread (flatten the curve) and reverse the growth of the virus. However, such approaches have the unintended consequences of causing economic activities to plummet and bringing local businesses to a standstill, thereby putting millions of jobs at risk. City administrators have generally resorted to an open loop, belief-based decision-making process, thereby struggling to manage (identify and enforce) timely and optimal policies. To overcome this challenge, this position paper explores a systematically designed, feedback-based strategy, to modulate parameters that control suppression and mitigation. Our work leverages advances in Bayesian Reinforcement Learning algorithms and known techniques in control theory, to stabilize and diminish the rate of propagation in pandemic situations. This paper discusses how offline exploitation using pre-trigger data, online exploration using observations from the environment, and a careful orchestration between the two using granular control of multiple on-off control signals can be used to modulate policy enforcement based on established metrics, such as reproduction number.","internal_url":"https://www.academia.edu/78356332/Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations","translated_internal_url":"","created_at":"2022-05-03T12:23:23.274-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Using_Control_Theory_and_Bayesian_Reinforcement_Learning_for_Policy_Management_in_Pandemic_Situations","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1688,"name":"Reinforcement Learning","url":"https://www.academia.edu/Documents/in/Reinforcement_Learning"}],"urls":[{"id":20178617,"url":"http://xplorestaging.ieee.org/ielx7/9473476/9473480/09473604.pdf?arnumber=9473604"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356331"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356331/Neuro_fuzzy_analytics_in_athlete_development_NueroFATH_a_machine_learning_approach"><img alt="Research paper thumbnail of Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356331/Neuro_fuzzy_analytics_in_athlete_development_NueroFATH_a_machine_learning_approach">Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Athletes represent the apex of physical capacity filling in a social picture of performance and b...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356331"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356331"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356331; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356331]").text(description); $(".js-view-count[data-work-id=78356331]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356331; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356331']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356331, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356331]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356331,"title":"Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach","translated_title":"","metadata":{"abstract":"Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Neural Computing and Applications"},"translated_abstract":"Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356329"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356329/A_Survey_of_Blockchain_Enabled_Cyber_Physical_Systems"><img alt="Research paper thumbnail of A Survey of Blockchain Enabled Cyber-Physical Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/85433035/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356329/A_Survey_of_Blockchain_Enabled_Cyber_Physical_Systems">A Survey of Blockchain Enabled Cyber-Physical Systems</a></div><div class="wp-workCard_item"><span>Sensors</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchains and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a decentralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. It has...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="daabaf3dda25d66dea04f6bed71068cb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433035,"asset_id":78356329,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433035/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356329"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356329"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356329; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356329]").text(description); $(".js-view-count[data-work-id=78356329]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356329; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356329']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356329, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "daabaf3dda25d66dea04f6bed71068cb" } } $('.js-work-strip[data-work-id=78356329]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356329,"title":"A Survey of Blockchain Enabled Cyber-Physical Systems","translated_title":"","metadata":{"abstract":"Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchains and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a decentralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. 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It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356328"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356328/Multi_layer_security_scheme_for_implantable_medical_devices"><img alt="Research paper thumbnail of Multi-layer security scheme for implantable medical devices" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356328/Multi_layer_security_scheme_for_implantable_medical_devices">Multi-layer security scheme for implantable medical devices</a></div><div class="wp-workCard_item"><span>Neural Computing and Applications</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the area...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356328"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356328"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356328; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78356328]").text(description); $(".js-view-count[data-work-id=78356328]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78356328; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78356328']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 78356328, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=78356328]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356328,"title":"Multi-layer security scheme for implantable medical devices","translated_title":"","metadata":{"abstract":"Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.","publisher":"Springer Science and Business Media LLC","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Neural Computing and Applications"},"translated_abstract":"Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.","internal_url":"https://www.academia.edu/78356328/Multi_layer_security_scheme_for_implantable_medical_devices","translated_internal_url":"","created_at":"2022-05-03T12:23:22.646-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":26678070,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Multi_layer_security_scheme_for_implantable_medical_devices","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":26678070,"first_name":"Heena","middle_initials":"","last_name":"Rathore","page_name":"HeenaRathore","domain_name":"iitj","created_at":"2015-02-23T13:40:17.279-08:00","display_name":"Heena Rathore","url":"https://iitj.academia.edu/HeenaRathore"},"attachments":[],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":20178613,"url":"http://link.springer.com/content/pdf/10.1007/s00521-018-3819-0.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356327"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356327/Multi_Layer_Perceptron_Model_on_Chip_for_Secure_Diabetic_Treatment"><img alt="Research paper thumbnail of Multi-Layer Perceptron Model on Chip for Secure Diabetic Treatment" class="work-thumbnail" src="https://attachments.academia-assets.com/85433037/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356327/Multi_Layer_Perceptron_Model_on_Chip_for_Secure_Diabetic_Treatment">Multi-Layer Perceptron Model on Chip for Secure Diabetic Treatment</a></div><div class="wp-workCard_item"><span>IEEE Access</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9fd68528b76da12122de5efe797a0437" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85433037,"asset_id":78356327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85433037/download_file?st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&st=MTczMjQ2NzY4Miw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78356327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78356327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78356327; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9fd68528b76da12122de5efe797a0437" } } $('.js-work-strip[data-work-id=78356327]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78356327,"title":"Multi-Layer Perceptron Model on Chip for Secure Diabetic Treatment","translated_title":"","metadata":{"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","grobid_abstract":"Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for the infusion of insulin to control blood glucose level. While these devices offer many clinical benefits, there has been a recent increase in the number of cases, wherein, the wireless communication channel of such devices has been compromised. This not only causes the device to malfunction but also potentially threatens the patient's life. In this paper, a neural networks-based multi-layer perceptron model was designed for real-time medical device security. Machine learning algorithms are among the most effective and broadly utilized systems for classification, identification, and segmentation. Although they are effective, they are both computationally and memory intensive, making them hard to be deployed on low-power embedded frameworks. In this paper, we present an on-chip neural system network for securing diabetic treatment. The model achieved 98.1% accuracy in classifying fake versus genuine glucose measurements. The proposed model was comparatively evaluated with a linear support vector machine which achieved only 90.17% accuracy with negligible precision and recall. Moreover, the proposal estimates the reliability of the framework through the use of the Bayesian network. The proposed approach enhances the reliability of the overall framework by 18% when only one device is secured, and over 90% when all devices are secured. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="78356323"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/78356323/TangleCV"><img alt="Research paper thumbnail of TangleCV" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/78356323/TangleCV">TangleCV</a></div><div class="wp-workCard_item"><span>ACM Transactions on Cyber-Physical Systems</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Connected vehicles are set to define the future of transportation; however, this upcoming technol...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Connected vehicles are set to define the future of transportation; however, this upcoming technology continues to be plagued with serious security risks. If these risks are not addressed in a timely fashion, then they could threaten the adoption and success of this promising technology. This article deals with a specific class of attacks in connected vehicles, namely tampering attacks caused due to compromise of on-board sensors. Current centralized solutions that employ trusted infrastructure to protect against adversarial manipulation of information cannot validate the correctness of the shared data and do not scale well. To overcome these issues, decentralized protection mechanisms by means of blockchain technology have emerged as a promising research direction. However, current permission-less, linear blockchain-based solutions have low transaction performance and high computational cost, thereby making it difficult to adopt them for security in connected vehicles. 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