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class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="user-bio-container"><div class="profile-bio fake-truncate js-profile-about" style="margin: 0px;">Nastooh Taheri Javan is an Assistant Professor at Imam Khomeini International University (IKIU). He was post-doctoral fellow at Amirkabir University of Technology (Tehran Polytechnic) where he completed his PhD and MS in computer engineering. His research interests lie in the area of wireless computer networks and network coding theory, spanning from theory to design and implementation. He has actively collaborated with researchers in various disciplines of computer science, particularly resource management on problems at the network architecture area. Nastooh is currently an IEEE Senior Member (SMIEEE). He joined IKIU as an assistant professor in the department of computer engineering in September 2020.<br /><br />Dr. Taheri is the co-founder and CEO of Barbod, a knowledge-based holding company focused on hardware design and electronic products development. As the CEO of Barbod, Nastooh is overseeing all aspects of the business. He has a proven track record in executive management and possesses over 10 years of experience in leading R&D teams in the IT industry to develop solutions for complex technical problems. 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class="profile--tab_heading_container">Papers by Nastooh Taheri Javan, PhD, نستوه طاهری جوان</h3></div><div class="js-work-strip profile--work_container" data-work-id="126267115"><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/126267115/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection"><img alt="Research paper thumbnail of Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection" class="work-thumbnail" src="https://attachments.academia-assets.com/120170410/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/126267115/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection">Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram ...</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">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. Our experimental results show that this detection system significantly outperforms existing methods in virus detection accuracy. The system improves detection accuracy by 15% and reduces false positives by 20% compared to traditional n-gram techniques. Additionally, it cuts computational overhead by about 30%, making it suitable for real-time applications. These advancements demonstrate the effectiveness and practicality of our approach. Future research will focus on applying our methods to polymorphic viruses and other malware to further enhance their robustness and applicability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb1c23ab1a6c0960e95f0ebf959cd197" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120170410,"asset_id":126267115,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120170410/download_file?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="126267115"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126267115"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126267115; 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$(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="124601380"><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/124601380/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection"><img alt="Research paper thumbnail of Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection" class="work-thumbnail" src="https://attachments.academia-assets.com/118797276/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/124601380/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection">Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection</a></div><div class="wp-workCard_item"><span>IEEE Access</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram ...</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">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. Our experimental results show that this detection system significantly outperforms existing methods in virus detection accuracy. The system improves detection accuracy by 15% and reduces false positives by 20% compared to traditional n-gram techniques. Additionally, it cuts computational overhead by about 30%, making it suitable for real-time applications. These advancements demonstrate the effectiveness and practicality of our approach. Future research will focus on applying our methods to polymorphic viruses and other malware to further enhance their robustness and applicability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d98a8413c059234e5006342a653ae9db" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118797276,"asset_id":124601380,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118797276/download_file?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="124601380"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124601380"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124601380; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116215412"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/116215412/Q_learning_based_algorithms_for_dynamic_transmission_control_in_IoT_equipment"><img alt="Research paper thumbnail of Q-learning-based algorithms for dynamic transmission control in IoT equipment" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/116215412/Q_learning_based_algorithms_for_dynamic_transmission_control_in_IoT_equipment">Q-learning-based algorithms for dynamic transmission control in IoT equipment</a></div><div class="wp-workCard_item"><span>The Journal of Supercomputing</span><span>, Jul 3, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116215412"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215412"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215412; 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Control Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an ...</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">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an innovative consensus tracking controller to track the desired trajectory for a class of fractional-order multi-agent systems with non-linear dynamics. The study derives an algorithm by implementing graph theory and the DSC method. The main approaches in the control of fractional-order systems are the DSC and the adaptive DSC techniques to avoid the computational complexity of fractionalorder virtual control law. According to these techniques, a virtual control law is formulated and the proposed controller is passed through a fractional-order dynamic surface. By employing the DSC and adaptive DSC laws, we demonstrate that the desired consensus tracking between agents can be ensured. To verify the performance of the new approach, we simulate the desirable scenarios and evaluate the results against a popular adaptive sliding mode technique.</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="116215411"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215411"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215411; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116215411]").text(description); $(".js-view-count[data-work-id=116215411]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116215411; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116215411']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="116215410"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/116215410/Clustering_Alexa_Internet_Data_using_Auto_Encoder_Network_and_Affinity_Propagation"><img alt="Research paper thumbnail of Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation" 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" rel="nofollow" href="https://www.academia.edu/116215410/Clustering_Alexa_Internet_Data_using_Auto_Encoder_Network_and_Affinity_Propagation">Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation</a></div><div class="wp-workCard_item"><span>2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Non-linear mapping is one of the most popular solutions for complex data structures and distinct ...</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">Non-linear mapping is one of the most popular solutions for complex data structures and distinct patterns to cluster data. Auto encoder Networks (AENs) are widely used in clustering as they improve data representation. In this paper, we collect Alexa.com data by crawling popular websites profiles, where dataset has 84 columns with type number and array of words. Next, an AEN architecture is presented to identify specific websites with exceptional patterns and the encoded data expresses new feature space of our original data. (Our) Encoded data is clustered by Affinity Propagation which is a partitioning algorithm without the need for specifying the number of clusters. There are important results based on 194 clusters and exemplars which are filtered and analyzed. One remarkable fact about results is that the first 11 columns as raw data are not clustered by Affinity Propagation a which re considered all as outlier. The results are summarized by selecting the best option w.r.t. the statistics and charts. Some of the obtained results are useful for website owners and provide some suggestions and solutions for Search Engine Optimization (SEO). Finally, we propose our crawler application which crawls and records data over 4 days. It must be added that the proposed web crawler faces challenges and their solutions can be helpful in most commonly used web crawling algorithms and libraries.</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="116215410"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215410"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215410; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116215410]").text(description); $(".js-view-count[data-work-id=116215410]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116215410; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116215410']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=116215410]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116215410,"title":"Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation","internal_url":"https://www.academia.edu/116215410/Clustering_Alexa_Internet_Data_using_Auto_Encoder_Network_and_Affinity_Propagation","owner_id":123061,"coauthors_can_edit":true,"owner":{"id":123061,"first_name":"Nastooh Taheri Javan, PhD,","middle_initials":null,"last_name":"نستوه طاهری جوان","page_name":"NastoohTaheriJavanPhDنستوهطاهریجوان","domain_name":"amirkabir","created_at":"2010-01-27T18:59:36.235-08:00","display_name":"Nastooh Taheri Javan, PhD, نستوه طاهری جوان","url":"https://amirkabir.academia.edu/NastoohTaheriJavanPhD%D9%86%D8%B3%D8%AA%D9%88%D9%87%D8%B7%D8%A7%D9%87%D8%B1%DB%8C%D8%AC%D9%88%D8%A7%D9%86"},"attachments":[]}, 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="116215409"><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/116215409/An_optimal_policy_for_joint_compression_and_transmission_control_in_delay_constrained_energy_harvesting_IoT_devices"><img alt="Research paper thumbnail of An optimal policy for joint compression and transmission control in delay-constrained energy harvesting IoT devices" class="work-thumbnail" src="https://attachments.academia-assets.com/112408007/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/116215409/An_optimal_policy_for_joint_compression_and_transmission_control_in_delay_constrained_energy_harvesting_IoT_devices">An optimal policy for joint compression and transmission control in delay-constrained energy harvesting IoT devices</a></div><div class="wp-workCard_item"><span>Computer Communications</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Energy-efficient communication remains one of the key requirements of the Internet of Things (IoT...</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">Energy-efficient communication remains one of the key requirements of the Internet of Things (IoT) platforms. The concern on energy consumption can be mitigated by exploiting technical ploys to reduce the volume of data for transmission (e.g., via sensing data compression) as well as by resorting to technological advancements (e.g., energy harvesting). However, these mitigating measures carry their own cost, which is the additional complexity of control and optimization in the digital communication chain. In particular, compression ratio is another control knob that needs adjusting besides the usual transmission parameters. Also, with the random and sporadic nature of the harvested energy, the goal shifts from mere energy conservation to judicious consumption of the renewable energy in a foresighted manner. In this paper, we assume an energy-harvesting IoT device that is tasked with (loss-lessly) compressing and reporting delay-constrained sensing events to an IoT gateway over a time-varying wireless channel. We are interested in computing an optimal policy for joint compression and transmission control adaptive to the node's energy availability, transmission buffer length, as well as its wireless channel conditions. We cast the problem as a Constrained Markov Decision Process (CMDP), and propose a two-timescale model-free reinforcement learning (RL) algorithm that is able to shape the optimal control policy in the absence of the statistical knowledge of the underlying system dynamics. Exhaustive simulation experiments are conducted to investigate the convergence of the learning algorithm, to explore the impacts of different system parameters (such as: the rate of sensing events, the energy arrival rate, and battery capacity) on the performance of the proposed policy, as well as to compare against some baseline schemes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c2a0b2a859d5d2ef910f9d57bdfda60" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112408007,"asset_id":116215409,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112408007/download_file?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="116215409"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215409"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215409; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116215409]").text(description); $(".js-view-count[data-work-id=116215409]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116215409; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116215409']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "8c2a0b2a859d5d2ef910f9d57bdfda60" } } $('.js-work-strip[data-work-id=116215409]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116215409,"title":"An optimal policy for joint compression and transmission control in delay-constrained energy harvesting IoT devices","internal_url":"https://www.academia.edu/116215409/An_optimal_policy_for_joint_compression_and_transmission_control_in_delay_constrained_energy_harvesting_IoT_devices","owner_id":123061,"coauthors_can_edit":true,"owner":{"id":123061,"first_name":"Nastooh Taheri Javan, PhD,","middle_initials":null,"last_name":"نستوه طاهری جوان","page_name":"NastoohTaheriJavanPhDنستوهطاهریجوان","domain_name":"amirkabir","created_at":"2010-01-27T18:59:36.235-08:00","display_name":"Nastooh Taheri Javan, PhD, نستوه طاهری جوان","url":"https://amirkabir.academia.edu/NastoohTaheriJavanPhD%D9%86%D8%B3%D8%AA%D9%88%D9%87%D8%B7%D8%A7%D9%87%D8%B1%DB%8C%D8%AC%D9%88%D8%A7%D9%86"},"attachments":[{"id":112408007,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112408007/thumbnails/1.jpg","file_name":"Com_Com.pdf","download_url":"https://www.academia.edu/attachments/112408007/download_file","bulk_download_file_name":"An_optimal_policy_for_joint_compression.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112408007/Com_Com-libre.pdf?1710418353=\u0026response-content-disposition=attachment%3B+filename%3DAn_optimal_policy_for_joint_compression.pdf\u0026Expires=1740586549\u0026Signature=Bg42acIu~LFjCoomsy1sdnJyn1UCGHOH9xv6GoIyXNjdwCu6o58MEpe72dog3TcDF~77XulkghcprmF2ekSgg6Ru1UuS~HiXRIn1eG~0vicP4ceXy~RqPyBD22Kuo-t-DgNdkNciLB856HXhGb6Ri1gnOCl7rT2W-~09aBXLNFeI5tfJi674HOjMWr1AAF~CGE0BMgM6cSV3zspeKMzZTPoTS22Dg0FZ9wsHFS5SsZu7w4-RRb956FaEquFumKyoOXevepQmR35Z3r9563VRt1Zu-Kzmc5OdTy9px4vDvlq6t6iiC5ej3MmvbxMy2h4w-uY72KmHrZfblsElyNGdrQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="116215408"><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/116215408/To_overhear_or_not_to_overhear_a_dilemma_between_network_coding_gain_and_energy_consumption_in_multi_hop_wireless_networks"><img alt="Research paper thumbnail of To overhear or not to overhear: a dilemma between network coding gain and energy consumption in multi-hop wireless networks" class="work-thumbnail" src="https://attachments.academia-assets.com/112408009/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/116215408/To_overhear_or_not_to_overhear_a_dilemma_between_network_coding_gain_and_energy_consumption_in_multi_hop_wireless_networks">To overhear or not to overhear: a dilemma between network coding gain and energy consumption in multi-hop wireless networks</a></div><div class="wp-workCard_item"><span>Wireless Networks</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Any properly designed network coding technique can result in increased throughput and reliability...</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">Any properly designed network coding technique can result in increased throughput and reliability of multi-hop wireless networks by taking advantage of the broadcast nature of wireless medium. In many inter-flow network coding schemes nodes are encouraged to overhear neighbour's traffic in order to improve coding opportunities at the transmitter nodes. A study of these schemes reveal that some of the overheard packets are not useful for coding operation and thus this forced overhearing increases energy consumption dramatically. In this paper, we formulate network coding aware sleep/wakeup scheduling as a semi Markov decision process (SMDP) that leads to an optimal node operation. In the proposed solution for SMDP, the network nodes learn when to switch off their transceiver in order to conserve energy and when to stay awake to overhear some useful packets. One of the main challenges here is the delay in obtaining reward signals by nodes. We employ a modified Reinforcement Learning (RL) method based on continuous-time Q-learning to overcome this challenge in the learning process. Our simulation results confirm the optimality of the new methodology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="13bac35d44e808d5c517e654452355cd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112408009,"asset_id":116215408,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112408009/download_file?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="116215408"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215408"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215408; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116215408]").text(description); $(".js-view-count[data-work-id=116215408]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116215408; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116215408']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="116215407"><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/116215407/To_send_or_not_to_send_An_optimal_stopping_approach_to_network_coding_in_multi_hop_wireless_networks"><img alt="Research paper thumbnail of To‐send‐or‐not‐to‐send: An optimal stopping approach to network coding in multi‐hop wireless networks" class="work-thumbnail" src="https://attachments.academia-assets.com/112408008/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/116215407/To_send_or_not_to_send_An_optimal_stopping_approach_to_network_coding_in_multi_hop_wireless_networks">To‐send‐or‐not‐to‐send: An optimal stopping approach to network coding in multi‐hop wireless networks</a></div><div class="wp-workCard_item"><span>International Journal of Communication Systems</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">SummaryNetwork coding is all about combining a variety of packets and forwarding as much packets ...</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">SummaryNetwork coding is all about combining a variety of packets and forwarding as much packets as possible in each transmission operation. The network coding technique improves the throughput efficiency of multi‐hop wireless networks by taking advantage of the broadcast nature of wireless channels. However, there are some scenarios where the coding cannot be exploited due to the stochastic nature of the packet arrival process in the network. In these cases, the coding node faces 2 critical choices: forwarding the packet towards the destination without coding, thereby sacrificing the advantage of network coding, or waiting for a while until a coding opportunity arises for the packets. Current research works have addressed this challenge for the case of a simple and restricted scheme called reverse carpooling where it is assumed that 2 flows with opposite directions arrive at the coding node. In this paper, the issue is explored in a general sense based on the COPE architecture requ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c5dc110b3d9b8a3a898e0014eea7397e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112408008,"asset_id":116215407,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112408008/download_file?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="116215407"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215407"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215407; 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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="116215403"><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/116215403/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach"><img alt="Research paper thumbnail of IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/112407990/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/116215403/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach">IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach</a></div><div class="wp-workCard_item"><span>IEEE Sensors Journal</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction,...</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">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slotframe) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zeroknowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSIbased method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b66eac401dfa4155806ea770ae09c190" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112407990,"asset_id":116215403,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112407990/download_file?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="116215403"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215403"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215403; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b66eac401dfa4155806ea770ae09c190" } } $('.js-work-strip[data-work-id=116215403]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116215403,"title":"IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach","internal_url":"https://www.academia.edu/116215403/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach","owner_id":123061,"coauthors_can_edit":true,"owner":{"id":123061,"first_name":"Nastooh Taheri Javan, PhD,","middle_initials":null,"last_name":"نستوه طاهری جوان","page_name":"NastoohTaheriJavanPhDنستوهطاهریجوان","domain_name":"amirkabir","created_at":"2010-01-27T18:59:36.235-08:00","display_name":"Nastooh Taheri Javan, PhD, نستوه طاهری جوان","url":"https://amirkabir.academia.edu/NastoohTaheriJavanPhD%D9%86%D8%B3%D8%AA%D9%88%D9%87%D8%B7%D8%A7%D9%87%D8%B1%DB%8C%D8%AC%D9%88%D8%A7%D9%86"},"attachments":[{"id":112407990,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/112407990/thumbnails/1.jpg","file_name":"1909.pdf","download_url":"https://www.academia.edu/attachments/112407990/download_file","bulk_download_file_name":"IEEE_802_15_4_e_TSCH_Based_Scheduling_fo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/112407990/1909-libre.pdf?1710418354=\u0026response-content-disposition=attachment%3B+filename%3DIEEE_802_15_4_e_TSCH_Based_Scheduling_fo.pdf\u0026Expires=1740586549\u0026Signature=PrlsTuffb2fcBkcsTF01e4MTC6D5qxOrXD1Rl-O3Z50bIRSiMjLF3noYLnjrIT46tzDV9AAQM3zojD2k5-1vWDG76pwt5A7rvq2MWGO0b9tcj2KlwDM1iSYD5JabiHLA58fmRFcPFF8cmiqmO8-Lv-w2c3x3o4DdiByRQ~nqcHg9nVIUDzZu6QkZYvz1rlZgpzJGn57Eu7Xz0canIgdQlBVrPHGno3fHz-0AaGJYd3iaS-EU35-~tnxIrL-cIrLi6R5AbUInGrJcsg9rEW0hOG5plfzny4qu~et23JUntBitkEeUNNKWM5-l-SSn6GG35f2OZjoQvo58FqMJG87eoA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="115590419"><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/115590419/Consensus_tracking_for_a_class_of_fractional_order_non_linear_multi_agent_systems_via_an_adaptive_dynamic_surface_controller"><img alt="Research paper thumbnail of Consensus tracking for a class of fractional-order non-linear multi-agent systems via an adaptive dynamic surface controller" class="work-thumbnail" src="https://attachments.academia-assets.com/111953611/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/115590419/Consensus_tracking_for_a_class_of_fractional_order_non_linear_multi_agent_systems_via_an_adaptive_dynamic_surface_controller">Consensus tracking for a class of fractional-order non-linear multi-agent systems via an adaptive dynamic surface controller</a></div><div class="wp-workCard_item"><span>Systems Science & Control Engineering</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an ...</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">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an innovative consensus tracking controller to track the desired trajectory for a class of fractional-order multi-agent systems with non-linear dynamics. The study derives an algorithm by implementing graph theory and the DSC method. The main approaches in the control of fractional-order systems are the DSC and the adaptive DSC techniques to avoid the computational complexity of fractionalorder virtual control law. According to these techniques, a virtual control law is formulated and the proposed controller is passed through a fractional-order dynamic surface. By employing the DSC and adaptive DSC laws, we demonstrate that the desired consensus tracking between agents can be ensured. To verify the performance of the new approach, we simulate the desirable scenarios and evaluate the results against a popular adaptive sliding mode technique.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8b64bf3994797773cd7a09ddad268be3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111953611,"asset_id":115590419,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111953611/download_file?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="115590419"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115590419"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115590419; 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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="115590160"><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/115590160/To_Code_or_Not_to_Code_When_and_How_to_Use_Network_Coding_in_Energy_Harvesting_Wireless_Multi_Hop_Networks"><img alt="Research paper thumbnail of To Code or Not to Code: When and How to Use Network Coding in Energy Harvesting Wireless Multi-Hop Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/111953379/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/115590160/To_Code_or_Not_to_Code_When_and_How_to_Use_Network_Coding_in_Energy_Harvesting_Wireless_Multi_Hop_Networks">To Code or Not to Code: When and How to Use Network Coding in Energy Harvesting Wireless Multi-Hop Networks</a></div><div class="wp-workCard_item"><span>IEEE Access</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The broadcast nature of communication in transmission media has driven the rise of network coding...</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 broadcast nature of communication in transmission media has driven the rise of network coding's popularity in wireless networks. Numerous benefits arise from employing network coding in multi-hop wireless networks, including enhanced throughput, reduced energy consumption, and decreased end-to-end delay. These advantages are a direct outcome of the minimized transmission count. This paper introduces a comprehensive framework to employ network coding in these networks. It refines decisionmaking at coding and decoding nodes simultaneously. The coding-nodes employ optimal stopping theory to find optimal moments for packet transmission. Meanwhile, the decoding-nodes dynamically decide, through SMDP (Semi Markov Decision Process) problem formulation, whether to conserve energy by deactivating radio units or to stay active for improved coding by overhearing packets. The proposed framework, named ENCODE, enables nodes to learn how and when to use network coding over time. Simulation results compare its performance with existing approaches. Our simulation results shed new light on when and how to use network coding in wireless multi-hop networks more effectively. INDEX TERMS Wireless multi-hop networks, network coding theory, coding gain, optimal stopping theory, semi-Markov decision process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="881bc58d2467ed10c2f09dcfd8de6740" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111953379,"asset_id":115590160,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111953379/download_file?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="115590160"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115590160"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115590160; 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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="87545261"><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/87545261/Mobility_enhancement_of_patients_body_monitoring_based_on_WBAN_with_multipath_routing"><img alt="Research paper thumbnail of Mobility enhancement of patients body monitoring based on WBAN with multipath routing" class="work-thumbnail" src="https://attachments.academia-assets.com/91723922/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/87545261/Mobility_enhancement_of_patients_body_monitoring_based_on_WBAN_with_multipath_routing">Mobility enhancement of patients body monitoring based on WBAN with multipath routing</a></div><div class="wp-workCard_item"><span>2014 2nd International Conference on Information and Communication Technology (ICoICT)</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the promising applications of wireless sensor networks (WSNs) is monitoring of the human 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">One of the promising applications of wireless sensor networks (WSNs) is monitoring of the human body for health concerns. For this purpose, a large number of small sensors are implanted in the human body. These sensors altogether provide a network of wireless sensors (WBANs) and monitor the vital signs and signals of the human body; these sensors will then send this information to the doctor. The most important application of the WBAN is the implementation of the monitoring network for patient safety in the hospital environment. In this case, supporting patients' mobility is one of the basic needs, which has been underestimated in recent studies. The problem that involves providing the required energy for the units used in this type of network is challenging; for this reason, sent/ received units with very low power consumption and with a very small radius are used in order to save energy. The resulting small sending range, leads to the lack of support for patients' mobility. In this paper, the AD HOC mode is suggested for use to establish a network and a multi-path routing algorithm, for the purpose of importing patients' mobility in hospital setting. The results of the simulation show that in addition to supporting patients' mobility, the use of the proposed idea instead of previously presented protocols, reduces delays in data transmission and energy consumption; and it also increases the delivery rate depending on the destination and the lifetime of the network, while on the other hand, it increases routing overhead.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5e7e1e7a4ef23af28b66abda35b58b5c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723922,"asset_id":87545261,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723922/download_file?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="87545261"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545261"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545261; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5e7e1e7a4ef23af28b66abda35b58b5c" } } $('.js-work-strip[data-work-id=87545261]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":87545261,"title":"Mobility enhancement of patients body monitoring based on WBAN with multipath routing","internal_url":"https://www.academia.edu/87545261/Mobility_enhancement_of_patients_body_monitoring_based_on_WBAN_with_multipath_routing","owner_id":123061,"coauthors_can_edit":true,"owner":{"id":123061,"first_name":"Nastooh Taheri Javan, PhD,","middle_initials":null,"last_name":"نستوه طاهری جوان","page_name":"NastoohTaheriJavanPhDنستوهطاهریجوان","domain_name":"amirkabir","created_at":"2010-01-27T18:59:36.235-08:00","display_name":"Nastooh Taheri Javan, PhD, نستوه طاهری جوان","url":"https://amirkabir.academia.edu/NastoohTaheriJavanPhD%D9%86%D8%B3%D8%AA%D9%88%D9%87%D8%B7%D8%A7%D9%87%D8%B1%DB%8C%D8%AC%D9%88%D8%A7%D9%86"},"attachments":[{"id":91723922,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/91723922/thumbnails/1.jpg","file_name":"ICoICT_2014.pdf","download_url":"https://www.academia.edu/attachments/91723922/download_file","bulk_download_file_name":"Mobility_enhancement_of_patients_body_mo.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/91723922/ICoICT_2014-libre.pdf?1664447694=\u0026response-content-disposition=attachment%3B+filename%3DMobility_enhancement_of_patients_body_mo.pdf\u0026Expires=1740586549\u0026Signature=c9X4jwc8kC5udRIE6bHls7IYK7qyuN6VtOkRKV3DHpkEqvfJ39-G-RzgOTsgO3X7~Ce-LAUUqAzVVLhWhFh5yHtFL4bWpZnhvBoMwVmZWrOsorRBSLgMORTroGp7ZOAfC0mqBzPhjRHaBGAArVlBP7bKsJ745e0W79IHE-UBKOwfpnaDUELzSJ~Ug41XWHizdc1Wh45NnlEl5uwMNciOyHMDbDzV6~0P~UwetCxUmib4gwOod1jVe7h9T0hkswT7~yXgLPwjm1ryFHa~zlygwA8AZq0q4opFAg2UrKH43Nun6YjH6gl25bMhhTsye1cq55-1uOIRFJtvl7uqrvuAmQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="87545260"><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/87545260/Increasing_coding_opportunities_using_maximum_weight_clique"><img alt="Research paper thumbnail of Increasing coding opportunities using maximum-weight clique" class="work-thumbnail" src="https://attachments.academia-assets.com/91723921/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/87545260/Increasing_coding_opportunities_using_maximum_weight_clique">Increasing coding opportunities using maximum-weight clique</a></div><div class="wp-workCard_item"><span>2013 5th Computer Science and Electronic Engineering Conference (CEEC)</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Network coding is used to improve the throughput of communication networks. In this technique, th...</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">Network coding is used to improve the throughput of communication networks. In this technique, the intermediate nodes mix packets to increase the information content of each transmission. For each flow, a coding pattern is defined as a set of flows that can be coded together. Finding a suitable coding pattern is a challenge due to much complexity. In this paper, we propose an algorithm to find a suitable coding pattern in intermediate nodes by mapping this problem onto maximumweight clique. Also, we described time complexity of our algorithm in details. Simulation results show that our proposed method can achieve better performance in terms of throughput and end-to-end delay by increasing coding opportunities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6be68ab4e821b9395ea7b471d60f3eb2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723921,"asset_id":87545260,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723921/download_file?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="87545260"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545260"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545260; 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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="87545259"><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/87545259/Improving_Energy_Efficiency_in_MANETs_by_Multi_Path_Routing"><img alt="Research paper thumbnail of Improving Energy Efficiency in MANETs by Multi-Path Routing" class="work-thumbnail" src="https://attachments.academia-assets.com/91723924/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/87545259/Improving_Energy_Efficiency_in_MANETs_by_Multi_Path_Routing">Improving Energy Efficiency in MANETs by Multi-Path Routing</a></div><div class="wp-workCard_item"><span>International Journal of Wireless &amp; Mobile Networks</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Some multi-path routing algorithm in MANET, simultaneously send information to the destination th...</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">Some multi-path routing algorithm in MANET, simultaneously send information to the destination through several directions to reduce end-to-end delay. In all these algorithms, the sent traffic through a path affects the adjacent path and unintentionally increases the delay due to the use of adjacent paths. Because, there are repetitive competitions among neighboring nodes, in order to obtain the joint channel in adjacent paths. The represented algorithm in this study tries to discover the distinct paths between source and destination nodes with using Omni directional antennas, to send information through these simultaneously. For this purpose, the number of active neighbors is counted in each direction with using a strategy. These criterions are effectively used to select routes. Proposed algorithm is based on AODV routing algorithm, and in the end it is compared with AOMDV, AODVM, and IZM-DSR algorithms which are multi-path routing algorithms based on AODV and DSR. Simulation results show that using the proposed algorithm creates a significant improvement in energy efficiency and reducing end-to-end delay.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d50acb7ad318445dae46bc71e55310c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723924,"asset_id":87545259,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723924/download_file?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="87545259"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545259"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545259; 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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="87545140"><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/87545140/Energy_and_amp_throughput_tradeoff_in_WSN_with_network_coding"><img alt="Research paper thumbnail of Energy &amp; throughput tradeoff in WSN with network coding" class="work-thumbnail" src="https://attachments.academia-assets.com/91723860/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/87545140/Energy_and_amp_throughput_tradeoff_in_WSN_with_network_coding">Energy &amp; throughput tradeoff in WSN with network coding</a></div><div class="wp-workCard_item"><span>2013 International Conference on ICT Convergence (ICTC)</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0759391f2ff89a8a64e301600b6b7f9c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723860,"asset_id":87545140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723860/download_file?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="87545140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545140; 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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="75410866"><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/75410866/Reducing_End_to_End_Delay_in_Multi_path_Routing_Algorithms_for_Mobile_Ad_Hoc_Networks"><img alt="Research paper thumbnail of Reducing End-to-End Delay in Multi-path Routing Algorithms for Mobile Ad Hoc Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/83190241/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/75410866/Reducing_End_to_End_Delay_in_Multi_path_Routing_Algorithms_for_Mobile_Ad_Hoc_Networks">Reducing End-to-End Delay in Multi-path Routing Algorithms for Mobile Ad Hoc Networks</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Some of the routing algorithms in mobile ad hoc networks use multiple paths simultaneously. These...</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">Some of the routing algorithms in mobile ad hoc networks use multiple paths simultaneously. These algorithms can attempt to find nodedisjoint paths to achieve higher fault tolerance capability. By using nodedisjoint paths, it is expected that the end-to-end delay in each path should be independent of each other. However, because of natural properties of wireless media and medium access mechanisms in ad hoc networks, the end-to-end delay between any source and destination depends on the pattern of communication in the neighborhood region. In this case some of the intermediate nodes should be silent to reverence their neighbors and this matter increases the average of end-to-end delay. To avoid this problem, multi-path routing algorithms can use zone-disjoint paths instead of node-disjoint paths. Two routes with no pair of neighbor nodes are called zone-disjoint paths. In this paper we propose a new multi-path routing algorithm that selects zone-disjoint paths, using omni-directional antenna. We evaluate our algorithm in several different scenarios. The simulation results show that the proposed approach is very effective in decreasing delay and packet loss.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65f61876ea5abed7b47f57050890d55c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83190241,"asset_id":75410866,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83190241/download_file?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="75410866"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75410866"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75410866; 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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="44849132"><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/44849132/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach"><img alt="Research paper thumbnail of IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/65357199/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/44849132/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach">IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction,...</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">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slot-frame) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zero-knowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSI-based method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8289e16ddecb027db8fd992d0381f41d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":65357199,"asset_id":44849132,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/65357199/download_file?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="44849132"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44849132"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44849132; 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Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slot-frame) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zero-knowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSI-based method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d92f443446089593163ccd887e3d8716" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":63994862,"asset_id":43691327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/63994862/download_file?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="43691327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="43691327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43691327; 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$(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="61277" id="papers"><div class="js-work-strip profile--work_container" data-work-id="126267115"><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/126267115/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection"><img alt="Research paper thumbnail of Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection" class="work-thumbnail" src="https://attachments.academia-assets.com/120170410/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/126267115/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection">Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram ...</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">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. Our experimental results show that this detection system significantly outperforms existing methods in virus detection accuracy. The system improves detection accuracy by 15% and reduces false positives by 20% compared to traditional n-gram techniques. Additionally, it cuts computational overhead by about 30%, making it suitable for real-time applications. These advancements demonstrate the effectiveness and practicality of our approach. Future research will focus on applying our methods to polymorphic viruses and other malware to further enhance their robustness and applicability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb1c23ab1a6c0960e95f0ebf959cd197" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":120170410,"asset_id":126267115,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/120170410/download_file?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="126267115"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="126267115"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 126267115; 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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="124601380"><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/124601380/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection"><img alt="Research paper thumbnail of Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection" class="work-thumbnail" src="https://attachments.academia-assets.com/118797276/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/124601380/Enhancing_Malicious_Code_Detection_With_Boosted_N_Gram_Analysis_and_Efficient_Feature_Selection">Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection</a></div><div class="wp-workCard_item"><span>IEEE Access</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram ...</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">A fundamental challenge in virology research lies in effectively detecting malicious code. Ngram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. Our experimental results show that this detection system significantly outperforms existing methods in virus detection accuracy. The system improves detection accuracy by 15% and reduces false positives by 20% compared to traditional n-gram techniques. Additionally, it cuts computational overhead by about 30%, making it suitable for real-time applications. These advancements demonstrate the effectiveness and practicality of our approach. Future research will focus on applying our methods to polymorphic viruses and other malware to further enhance their robustness and applicability.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d98a8413c059234e5006342a653ae9db" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118797276,"asset_id":124601380,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118797276/download_file?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="124601380"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124601380"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124601380; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116215412"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/116215412/Q_learning_based_algorithms_for_dynamic_transmission_control_in_IoT_equipment"><img alt="Research paper thumbnail of Q-learning-based algorithms for dynamic transmission control in IoT equipment" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/116215412/Q_learning_based_algorithms_for_dynamic_transmission_control_in_IoT_equipment">Q-learning-based algorithms for dynamic transmission control in IoT equipment</a></div><div class="wp-workCard_item"><span>The Journal of Supercomputing</span><span>, Jul 3, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116215412"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215412"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215412; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116215412]").text(description); $(".js-view-count[data-work-id=116215412]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116215412; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116215412']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="116215411"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/116215411/Consensus_tracking_for_a_class_of_fractional_order_non_linear_multi_agent_systems_via_an_adaptive_dynamic_surface_controller"><img alt="Research paper thumbnail of Consensus tracking for a class of fractional-order non-linear multi-agent systems via an adaptive dynamic surface controller" 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" rel="nofollow" href="https://www.academia.edu/116215411/Consensus_tracking_for_a_class_of_fractional_order_non_linear_multi_agent_systems_via_an_adaptive_dynamic_surface_controller">Consensus tracking for a class of fractional-order non-linear multi-agent systems via an adaptive dynamic surface controller</a></div><div class="wp-workCard_item"><span>Systems Science &amp; Control Engineering</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an ...</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">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an innovative consensus tracking controller to track the desired trajectory for a class of fractional-order multi-agent systems with non-linear dynamics. The study derives an algorithm by implementing graph theory and the DSC method. The main approaches in the control of fractional-order systems are the DSC and the adaptive DSC techniques to avoid the computational complexity of fractionalorder virtual control law. According to these techniques, a virtual control law is formulated and the proposed controller is passed through a fractional-order dynamic surface. By employing the DSC and adaptive DSC laws, we demonstrate that the desired consensus tracking between agents can be ensured. To verify the performance of the new approach, we simulate the desirable scenarios and evaluate the results against a popular adaptive sliding mode technique.</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="116215411"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215411"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215411; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116215411]").text(description); $(".js-view-count[data-work-id=116215411]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116215411; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116215411']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=116215411]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116215411,"title":"Consensus tracking for a class of fractional-order non-linear multi-agent systems via an adaptive dynamic surface controller","internal_url":"https://www.academia.edu/116215411/Consensus_tracking_for_a_class_of_fractional_order_non_linear_multi_agent_systems_via_an_adaptive_dynamic_surface_controller","owner_id":123061,"coauthors_can_edit":true,"owner":{"id":123061,"first_name":"Nastooh Taheri Javan, PhD,","middle_initials":null,"last_name":"نستوه طاهری جوان","page_name":"NastoohTaheriJavanPhDنستوهطاهریجوان","domain_name":"amirkabir","created_at":"2010-01-27T18:59:36.235-08:00","display_name":"Nastooh Taheri Javan, PhD, نستوه طاهری جوان","url":"https://amirkabir.academia.edu/NastoohTaheriJavanPhD%D9%86%D8%B3%D8%AA%D9%88%D9%87%D8%B7%D8%A7%D9%87%D8%B1%DB%8C%D8%AC%D9%88%D8%A7%D9%86"},"attachments":[]}, 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="116215410"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/116215410/Clustering_Alexa_Internet_Data_using_Auto_Encoder_Network_and_Affinity_Propagation"><img alt="Research paper thumbnail of Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation" 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" rel="nofollow" href="https://www.academia.edu/116215410/Clustering_Alexa_Internet_Data_using_Auto_Encoder_Network_and_Affinity_Propagation">Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation</a></div><div class="wp-workCard_item"><span>2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Non-linear mapping is one of the most popular solutions for complex data structures and distinct ...</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">Non-linear mapping is one of the most popular solutions for complex data structures and distinct patterns to cluster data. Auto encoder Networks (AENs) are widely used in clustering as they improve data representation. In this paper, we collect Alexa.com data by crawling popular websites profiles, where dataset has 84 columns with type number and array of words. Next, an AEN architecture is presented to identify specific websites with exceptional patterns and the encoded data expresses new feature space of our original data. (Our) Encoded data is clustered by Affinity Propagation which is a partitioning algorithm without the need for specifying the number of clusters. There are important results based on 194 clusters and exemplars which are filtered and analyzed. One remarkable fact about results is that the first 11 columns as raw data are not clustered by Affinity Propagation a which re considered all as outlier. The results are summarized by selecting the best option w.r.t. the statistics and charts. Some of the obtained results are useful for website owners and provide some suggestions and solutions for Search Engine Optimization (SEO). Finally, we propose our crawler application which crawls and records data over 4 days. It must be added that the proposed web crawler faces challenges and their solutions can be helpful in most commonly used web crawling algorithms and libraries.</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="116215410"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215410"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215410; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116215410]").text(description); $(".js-view-count[data-work-id=116215410]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116215410; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116215410']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=116215410]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116215410,"title":"Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation","internal_url":"https://www.academia.edu/116215410/Clustering_Alexa_Internet_Data_using_Auto_Encoder_Network_and_Affinity_Propagation","owner_id":123061,"coauthors_can_edit":true,"owner":{"id":123061,"first_name":"Nastooh Taheri Javan, PhD,","middle_initials":null,"last_name":"نستوه طاهری جوان","page_name":"NastoohTaheriJavanPhDنستوهطاهریجوان","domain_name":"amirkabir","created_at":"2010-01-27T18:59:36.235-08:00","display_name":"Nastooh Taheri Javan, PhD, نستوه طاهری جوان","url":"https://amirkabir.academia.edu/NastoohTaheriJavanPhD%D9%86%D8%B3%D8%AA%D9%88%D9%87%D8%B7%D8%A7%D9%87%D8%B1%DB%8C%D8%AC%D9%88%D8%A7%D9%86"},"attachments":[]}, 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="116215409"><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/116215409/An_optimal_policy_for_joint_compression_and_transmission_control_in_delay_constrained_energy_harvesting_IoT_devices"><img alt="Research paper thumbnail of An optimal policy for joint compression and transmission control in delay-constrained energy harvesting IoT devices" class="work-thumbnail" src="https://attachments.academia-assets.com/112408007/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/116215409/An_optimal_policy_for_joint_compression_and_transmission_control_in_delay_constrained_energy_harvesting_IoT_devices">An optimal policy for joint compression and transmission control in delay-constrained energy harvesting IoT devices</a></div><div class="wp-workCard_item"><span>Computer Communications</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Energy-efficient communication remains one of the key requirements of the Internet of Things (IoT...</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">Energy-efficient communication remains one of the key requirements of the Internet of Things (IoT) platforms. The concern on energy consumption can be mitigated by exploiting technical ploys to reduce the volume of data for transmission (e.g., via sensing data compression) as well as by resorting to technological advancements (e.g., energy harvesting). However, these mitigating measures carry their own cost, which is the additional complexity of control and optimization in the digital communication chain. In particular, compression ratio is another control knob that needs adjusting besides the usual transmission parameters. Also, with the random and sporadic nature of the harvested energy, the goal shifts from mere energy conservation to judicious consumption of the renewable energy in a foresighted manner. In this paper, we assume an energy-harvesting IoT device that is tasked with (loss-lessly) compressing and reporting delay-constrained sensing events to an IoT gateway over a time-varying wireless channel. We are interested in computing an optimal policy for joint compression and transmission control adaptive to the node's energy availability, transmission buffer length, as well as its wireless channel conditions. We cast the problem as a Constrained Markov Decision Process (CMDP), and propose a two-timescale model-free reinforcement learning (RL) algorithm that is able to shape the optimal control policy in the absence of the statistical knowledge of the underlying system dynamics. Exhaustive simulation experiments are conducted to investigate the convergence of the learning algorithm, to explore the impacts of different system parameters (such as: the rate of sensing events, the energy arrival rate, and battery capacity) on the performance of the proposed policy, as well as to compare against some baseline schemes.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8c2a0b2a859d5d2ef910f9d57bdfda60" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112408007,"asset_id":116215409,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112408007/download_file?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="116215409"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215409"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215409; 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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="116215408"><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/116215408/To_overhear_or_not_to_overhear_a_dilemma_between_network_coding_gain_and_energy_consumption_in_multi_hop_wireless_networks"><img alt="Research paper thumbnail of To overhear or not to overhear: a dilemma between network coding gain and energy consumption in multi-hop wireless networks" class="work-thumbnail" src="https://attachments.academia-assets.com/112408009/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/116215408/To_overhear_or_not_to_overhear_a_dilemma_between_network_coding_gain_and_energy_consumption_in_multi_hop_wireless_networks">To overhear or not to overhear: a dilemma between network coding gain and energy consumption in multi-hop wireless networks</a></div><div class="wp-workCard_item"><span>Wireless Networks</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Any properly designed network coding technique can result in increased throughput and reliability...</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">Any properly designed network coding technique can result in increased throughput and reliability of multi-hop wireless networks by taking advantage of the broadcast nature of wireless medium. In many inter-flow network coding schemes nodes are encouraged to overhear neighbour's traffic in order to improve coding opportunities at the transmitter nodes. A study of these schemes reveal that some of the overheard packets are not useful for coding operation and thus this forced overhearing increases energy consumption dramatically. In this paper, we formulate network coding aware sleep/wakeup scheduling as a semi Markov decision process (SMDP) that leads to an optimal node operation. In the proposed solution for SMDP, the network nodes learn when to switch off their transceiver in order to conserve energy and when to stay awake to overhear some useful packets. One of the main challenges here is the delay in obtaining reward signals by nodes. We employ a modified Reinforcement Learning (RL) method based on continuous-time Q-learning to overcome this challenge in the learning process. Our simulation results confirm the optimality of the new methodology.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="13bac35d44e808d5c517e654452355cd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112408009,"asset_id":116215408,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112408009/download_file?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="116215408"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215408"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215408; 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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="116215407"><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/116215407/To_send_or_not_to_send_An_optimal_stopping_approach_to_network_coding_in_multi_hop_wireless_networks"><img alt="Research paper thumbnail of To‐send‐or‐not‐to‐send: An optimal stopping approach to network coding in multi‐hop wireless networks" class="work-thumbnail" src="https://attachments.academia-assets.com/112408008/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/116215407/To_send_or_not_to_send_An_optimal_stopping_approach_to_network_coding_in_multi_hop_wireless_networks">To‐send‐or‐not‐to‐send: An optimal stopping approach to network coding in multi‐hop wireless networks</a></div><div class="wp-workCard_item"><span>International Journal of Communication Systems</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">SummaryNetwork coding is all about combining a variety of packets and forwarding as much packets ...</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">SummaryNetwork coding is all about combining a variety of packets and forwarding as much packets as possible in each transmission operation. The network coding technique improves the throughput efficiency of multi‐hop wireless networks by taking advantage of the broadcast nature of wireless channels. However, there are some scenarios where the coding cannot be exploited due to the stochastic nature of the packet arrival process in the network. In these cases, the coding node faces 2 critical choices: forwarding the packet towards the destination without coding, thereby sacrificing the advantage of network coding, or waiting for a while until a coding opportunity arises for the packets. Current research works have addressed this challenge for the case of a simple and restricted scheme called reverse carpooling where it is assumed that 2 flows with opposite directions arrive at the coding node. In this paper, the issue is explored in a general sense based on the COPE architecture requ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c5dc110b3d9b8a3a898e0014eea7397e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112408008,"asset_id":116215407,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112408008/download_file?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="116215407"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215407"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215407; 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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="116215403"><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/116215403/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach"><img alt="Research paper thumbnail of IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/112407990/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/116215403/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach">IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach</a></div><div class="wp-workCard_item"><span>IEEE Sensors Journal</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction,...</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">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slotframe) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zeroknowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSIbased method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b66eac401dfa4155806ea770ae09c190" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112407990,"asset_id":116215403,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112407990/download_file?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="116215403"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116215403"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116215403; 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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="115590419"><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/115590419/Consensus_tracking_for_a_class_of_fractional_order_non_linear_multi_agent_systems_via_an_adaptive_dynamic_surface_controller"><img alt="Research paper thumbnail of Consensus tracking for a class of fractional-order non-linear multi-agent systems via an adaptive dynamic surface controller" class="work-thumbnail" src="https://attachments.academia-assets.com/111953611/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/115590419/Consensus_tracking_for_a_class_of_fractional_order_non_linear_multi_agent_systems_via_an_adaptive_dynamic_surface_controller">Consensus tracking for a class of fractional-order non-linear multi-agent systems via an adaptive dynamic surface controller</a></div><div class="wp-workCard_item"><span>Systems Science & Control Engineering</span><span>, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an ...</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">In this paper we investigate bottlenecks in adaptive dynamic surface control (DSC) and unveil an innovative consensus tracking controller to track the desired trajectory for a class of fractional-order multi-agent systems with non-linear dynamics. The study derives an algorithm by implementing graph theory and the DSC method. The main approaches in the control of fractional-order systems are the DSC and the adaptive DSC techniques to avoid the computational complexity of fractionalorder virtual control law. According to these techniques, a virtual control law is formulated and the proposed controller is passed through a fractional-order dynamic surface. By employing the DSC and adaptive DSC laws, we demonstrate that the desired consensus tracking between agents can be ensured. To verify the performance of the new approach, we simulate the desirable scenarios and evaluate the results against a popular adaptive sliding mode technique.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8b64bf3994797773cd7a09ddad268be3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111953611,"asset_id":115590419,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111953611/download_file?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="115590419"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115590419"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115590419; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=115590419]").text(description); $(".js-view-count[data-work-id=115590419]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 115590419; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='115590419']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="115590160"><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/115590160/To_Code_or_Not_to_Code_When_and_How_to_Use_Network_Coding_in_Energy_Harvesting_Wireless_Multi_Hop_Networks"><img alt="Research paper thumbnail of To Code or Not to Code: When and How to Use Network Coding in Energy Harvesting Wireless Multi-Hop Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/111953379/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/115590160/To_Code_or_Not_to_Code_When_and_How_to_Use_Network_Coding_in_Energy_Harvesting_Wireless_Multi_Hop_Networks">To Code or Not to Code: When and How to Use Network Coding in Energy Harvesting Wireless Multi-Hop Networks</a></div><div class="wp-workCard_item"><span>IEEE Access</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The broadcast nature of communication in transmission media has driven the rise of network coding...</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 broadcast nature of communication in transmission media has driven the rise of network coding's popularity in wireless networks. Numerous benefits arise from employing network coding in multi-hop wireless networks, including enhanced throughput, reduced energy consumption, and decreased end-to-end delay. These advantages are a direct outcome of the minimized transmission count. This paper introduces a comprehensive framework to employ network coding in these networks. It refines decisionmaking at coding and decoding nodes simultaneously. The coding-nodes employ optimal stopping theory to find optimal moments for packet transmission. Meanwhile, the decoding-nodes dynamically decide, through SMDP (Semi Markov Decision Process) problem formulation, whether to conserve energy by deactivating radio units or to stay active for improved coding by overhearing packets. The proposed framework, named ENCODE, enables nodes to learn how and when to use network coding over time. Simulation results compare its performance with existing approaches. Our simulation results shed new light on when and how to use network coding in wireless multi-hop networks more effectively. INDEX TERMS Wireless multi-hop networks, network coding theory, coding gain, optimal stopping theory, semi-Markov decision process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="881bc58d2467ed10c2f09dcfd8de6740" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111953379,"asset_id":115590160,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111953379/download_file?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="115590160"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115590160"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115590160; 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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="87545261"><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/87545261/Mobility_enhancement_of_patients_body_monitoring_based_on_WBAN_with_multipath_routing"><img alt="Research paper thumbnail of Mobility enhancement of patients body monitoring based on WBAN with multipath routing" class="work-thumbnail" src="https://attachments.academia-assets.com/91723922/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/87545261/Mobility_enhancement_of_patients_body_monitoring_based_on_WBAN_with_multipath_routing">Mobility enhancement of patients body monitoring based on WBAN with multipath routing</a></div><div class="wp-workCard_item"><span>2014 2nd International Conference on Information and Communication Technology (ICoICT)</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the promising applications of wireless sensor networks (WSNs) is monitoring of the human 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">One of the promising applications of wireless sensor networks (WSNs) is monitoring of the human body for health concerns. For this purpose, a large number of small sensors are implanted in the human body. These sensors altogether provide a network of wireless sensors (WBANs) and monitor the vital signs and signals of the human body; these sensors will then send this information to the doctor. The most important application of the WBAN is the implementation of the monitoring network for patient safety in the hospital environment. In this case, supporting patients' mobility is one of the basic needs, which has been underestimated in recent studies. The problem that involves providing the required energy for the units used in this type of network is challenging; for this reason, sent/ received units with very low power consumption and with a very small radius are used in order to save energy. The resulting small sending range, leads to the lack of support for patients' mobility. In this paper, the AD HOC mode is suggested for use to establish a network and a multi-path routing algorithm, for the purpose of importing patients' mobility in hospital setting. The results of the simulation show that in addition to supporting patients' mobility, the use of the proposed idea instead of previously presented protocols, reduces delays in data transmission and energy consumption; and it also increases the delivery rate depending on the destination and the lifetime of the network, while on the other hand, it increases routing overhead.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5e7e1e7a4ef23af28b66abda35b58b5c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723922,"asset_id":87545261,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723922/download_file?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="87545261"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545261"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545261; 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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="87545260"><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/87545260/Increasing_coding_opportunities_using_maximum_weight_clique"><img alt="Research paper thumbnail of Increasing coding opportunities using maximum-weight clique" class="work-thumbnail" src="https://attachments.academia-assets.com/91723921/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/87545260/Increasing_coding_opportunities_using_maximum_weight_clique">Increasing coding opportunities using maximum-weight clique</a></div><div class="wp-workCard_item"><span>2013 5th Computer Science and Electronic Engineering Conference (CEEC)</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Network coding is used to improve the throughput of communication networks. In this technique, th...</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">Network coding is used to improve the throughput of communication networks. In this technique, the intermediate nodes mix packets to increase the information content of each transmission. For each flow, a coding pattern is defined as a set of flows that can be coded together. Finding a suitable coding pattern is a challenge due to much complexity. In this paper, we propose an algorithm to find a suitable coding pattern in intermediate nodes by mapping this problem onto maximumweight clique. Also, we described time complexity of our algorithm in details. Simulation results show that our proposed method can achieve better performance in terms of throughput and end-to-end delay by increasing coding opportunities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6be68ab4e821b9395ea7b471d60f3eb2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723921,"asset_id":87545260,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723921/download_file?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="87545260"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545260"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545260; 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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="87545259"><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/87545259/Improving_Energy_Efficiency_in_MANETs_by_Multi_Path_Routing"><img alt="Research paper thumbnail of Improving Energy Efficiency in MANETs by Multi-Path Routing" class="work-thumbnail" src="https://attachments.academia-assets.com/91723924/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/87545259/Improving_Energy_Efficiency_in_MANETs_by_Multi_Path_Routing">Improving Energy Efficiency in MANETs by Multi-Path Routing</a></div><div class="wp-workCard_item"><span>International Journal of Wireless &amp; Mobile Networks</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Some multi-path routing algorithm in MANET, simultaneously send information to the destination th...</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">Some multi-path routing algorithm in MANET, simultaneously send information to the destination through several directions to reduce end-to-end delay. In all these algorithms, the sent traffic through a path affects the adjacent path and unintentionally increases the delay due to the use of adjacent paths. Because, there are repetitive competitions among neighboring nodes, in order to obtain the joint channel in adjacent paths. The represented algorithm in this study tries to discover the distinct paths between source and destination nodes with using Omni directional antennas, to send information through these simultaneously. For this purpose, the number of active neighbors is counted in each direction with using a strategy. These criterions are effectively used to select routes. Proposed algorithm is based on AODV routing algorithm, and in the end it is compared with AOMDV, AODVM, and IZM-DSR algorithms which are multi-path routing algorithms based on AODV and DSR. Simulation results show that using the proposed algorithm creates a significant improvement in energy efficiency and reducing end-to-end delay.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d50acb7ad318445dae46bc71e55310c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723924,"asset_id":87545259,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723924/download_file?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="87545259"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545259"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545259; 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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="87545140"><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/87545140/Energy_and_amp_throughput_tradeoff_in_WSN_with_network_coding"><img alt="Research paper thumbnail of Energy &amp; throughput tradeoff in WSN with network coding" class="work-thumbnail" src="https://attachments.academia-assets.com/91723860/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/87545140/Energy_and_amp_throughput_tradeoff_in_WSN_with_network_coding">Energy &amp; throughput tradeoff in WSN with network coding</a></div><div class="wp-workCard_item"><span>2013 International Conference on ICT Convergence (ICTC)</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0759391f2ff89a8a64e301600b6b7f9c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":91723860,"asset_id":87545140,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/91723860/download_file?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="87545140"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="87545140"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87545140; 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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="75410866"><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/75410866/Reducing_End_to_End_Delay_in_Multi_path_Routing_Algorithms_for_Mobile_Ad_Hoc_Networks"><img alt="Research paper thumbnail of Reducing End-to-End Delay in Multi-path Routing Algorithms for Mobile Ad Hoc Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/83190241/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/75410866/Reducing_End_to_End_Delay_in_Multi_path_Routing_Algorithms_for_Mobile_Ad_Hoc_Networks">Reducing End-to-End Delay in Multi-path Routing Algorithms for Mobile Ad Hoc Networks</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Some of the routing algorithms in mobile ad hoc networks use multiple paths simultaneously. These...</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">Some of the routing algorithms in mobile ad hoc networks use multiple paths simultaneously. These algorithms can attempt to find nodedisjoint paths to achieve higher fault tolerance capability. By using nodedisjoint paths, it is expected that the end-to-end delay in each path should be independent of each other. However, because of natural properties of wireless media and medium access mechanisms in ad hoc networks, the end-to-end delay between any source and destination depends on the pattern of communication in the neighborhood region. In this case some of the intermediate nodes should be silent to reverence their neighbors and this matter increases the average of end-to-end delay. To avoid this problem, multi-path routing algorithms can use zone-disjoint paths instead of node-disjoint paths. Two routes with no pair of neighbor nodes are called zone-disjoint paths. In this paper we propose a new multi-path routing algorithm that selects zone-disjoint paths, using omni-directional antenna. We evaluate our algorithm in several different scenarios. The simulation results show that the proposed approach is very effective in decreasing delay and packet loss.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65f61876ea5abed7b47f57050890d55c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83190241,"asset_id":75410866,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83190241/download_file?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="75410866"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75410866"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75410866; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60482721"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/60482721/Adaptive_Channel_Hopping_for_IEEE_802_15_4_TSCH_Based_Networks_A_Dynamic_Bernoulli_Bandit_Approach"><img alt="Research paper thumbnail of Adaptive Channel Hopping for IEEE 802.15.4 TSCH-Based Networks: A Dynamic Bernoulli Bandit Approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/60482721/Adaptive_Channel_Hopping_for_IEEE_802_15_4_TSCH_Based_Networks_A_Dynamic_Bernoulli_Bandit_Approach">Adaptive Channel Hopping for IEEE 802.15.4 TSCH-Based Networks: A Dynamic Bernoulli Bandit Approach</a></div><div class="wp-workCard_item"><span>IEEE Sensors Journal</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60482721"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60482721"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60482721; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=60482721]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":60482721,"title":"Adaptive Channel Hopping for IEEE 802.15.4 TSCH-Based Networks: A Dynamic Bernoulli Bandit Approach","internal_url":"https://www.academia.edu/60482721/Adaptive_Channel_Hopping_for_IEEE_802_15_4_TSCH_Based_Networks_A_Dynamic_Bernoulli_Bandit_Approach","owner_id":123061,"coauthors_can_edit":true,"owner":{"id":123061,"first_name":"Nastooh Taheri Javan, PhD,","middle_initials":null,"last_name":"نستوه طاهری جوان","page_name":"NastoohTaheriJavanPhDنستوهطاهریجوان","domain_name":"amirkabir","created_at":"2010-01-27T18:59:36.235-08:00","display_name":"Nastooh Taheri Javan, PhD, نستوه طاهری جوان","url":"https://amirkabir.academia.edu/NastoohTaheriJavanPhD%D9%86%D8%B3%D8%AA%D9%88%D9%87%D8%B7%D8%A7%D9%87%D8%B1%DB%8C%D8%AC%D9%88%D8%A7%D9%86"},"attachments":[]}, 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="44849132"><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/44849132/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach"><img alt="Research paper thumbnail of IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/65357199/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/44849132/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach">IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction,...</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">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slot-frame) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zero-knowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSI-based method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8289e16ddecb027db8fd992d0381f41d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":65357199,"asset_id":44849132,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/65357199/download_file?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="44849132"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="44849132"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 44849132; 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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="43691327"><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/43691327/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach"><img alt="Research paper thumbnail of IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach" class="work-thumbnail" src="https://attachments.academia-assets.com/63994862/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/43691327/IEEE_802_15_4_e_TSCH_Based_Scheduling_for_Throughput_Optimization_A_Combinatorial_Multi_Armed_Bandit_Approach">IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach</a></div><div class="wp-workCard_item"><span>IEEE sensors journal</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction,...</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">In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slot-frame) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zero-knowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSI-based method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d92f443446089593163ccd887e3d8716" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":63994862,"asset_id":43691327,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/63994862/download_file?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="43691327"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="43691327"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43691327; 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