Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics | EAI Endorsed Transactions on AI and Robotics

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Chen, “IoT, cloud, big data and AI in interdisciplinary domains,” Simul. Model. Pract. Theory, vol. 102, 2020, doi: 10.1016/j.simpat.2020.102070."/> <meta name="citation_reference" content="M. Salhaoui, A. Guerrero-González, M. Arioua, F. J. Ortiz, A. El Oualkadi, and C. L. Torregrosa, “Smart industrial iot monitoring and control system based on UAV and cloud computing applied to a concrete plant,” Sensors (Switzerland), vol. 19, no. 15, 2019, doi: 10.3390/s19153316."/> <meta name="citation_reference" content="S. Sarker, L. Jamal, S. F. Ahmed, and N. Irtisam, “Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review,” Rob. Auton. Syst., vol. 146, 2021, doi: 10.1016/j.robot.2021.103902."/> <meta name="citation_reference" content="S. K. Singh, S. Rathore, and J. H. Park, “BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence,” Futur. Gener. Comput. Syst., vol. 110, pp. 721–743, 2020, doi: 10.1016/j.future.2019.09.002."/> <meta name="citation_reference" content="M. Salhaoui, “Smart IoT monitoring and real-time control based on autonomous robots, visual recognition and cloud/edge computing services,” 2021."/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Kiran Deep Singh"/> <meta name="DC.Creator.PersonalName" content="Prabhdeep Singh"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-03-12"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-07-25"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-01-08"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-03-12"/> <meta name="DC.Description" xml:lang="en" content="Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.&nbsp;"/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="3617"/> <meta name="DC.Identifier.DOI" content="10.4108/airo.3617"/> <meta name="DC.Identifier.URI" content="https://publications.eai.eu/index.php/airo/article/view/3617"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Prabhdeep Singh, Kiran Deep Singh"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by-nc-sa/4.0"/> <meta name="DC.Source" content="EAI Endorsed Transactions on AI and Robotics"/> <meta name="DC.Source.ISSN" content="2790-7511"/> <meta name="DC.Source.Volume" content="3"/> <meta name="DC.Source.URI" content="https://publications.eai.eu/index.php/airo"/> <meta name="DC.Subject" xml:lang="en" content="Cloud Computing"/> <meta name="DC.Title" content="Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="Review article"/> <link rel="stylesheet" href="https://publications.eai.eu/index.php/airo/$$$call$$$/page/page/css?name=stylesheet" type="text/css" /><link rel="stylesheet" href="https://publications.eai.eu/index.php/airo/$$$call$$$/page/page/css?name=font" type="text/css" /><link rel="stylesheet" href="https://publications.eai.eu/lib/pkp/styles/fontawesome/fontawesome.css?v=3.3.0.18" type="text/css" /><style type="text/css">.pkp_structure_head { background: center / cover no-repeat url("https://publications.eai.eu/public/journals/1/homepageImage_en_US.png");}</style><link rel="stylesheet" href="https://publications.eai.eu/plugins/generic/citations/css/citations.css?v=3.3.0.18" type="text/css" /><link rel="stylesheet" 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class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Review article </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Kiran Deep Singh </span> <span class="affiliation"> Chitkara University <a href="https://ror.org/057d6z539"><?xml version="1.0" encoding="UTF-8" standalone="no"?>  <svg xmlns:i="&ns_ai;" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:svg="http://www.w3.org/2000/svg" xmlns="http://www.w3.org/2000/svg" xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd" xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape" 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1.33,5.02 1.33,7.83 0,2.87 -0.44,5.49 -1.33,7.87 z m 44.94,-16.04 c -0.72,0.92 -1.62,1.59 -2.71,2.02 -1.09,0.43 -2.31,0.69 -3.65,0.77 -1.35,0.09 -2.62,0.13 -3.83,0.13 h -7.91 V 39.89 h 8.77 c 1.2,0 2.44,0.09 3.7,0.26 1.26,0.17 2.38,0.5 3.35,0.99 0.97,0.49 1.78,1.19 2.41,2.11 0.63,0.92 0.95,2.15 0.95,3.7 0,1.65 -0.36,2.94 -1.08,3.86 z" id="path80" inkscape:connector-curvature="0" style="fill:#202826" /> </g> </g> </switch> </svg> </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.4108/airo.3617"> https://doi.org/10.4108/airo.3617 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> Artificial Intelligence, Fog Computing, Internet of Things, Robotics, Cloud Computing </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Fog Cloud Computing and the Internet of Things are transforming robotics by empowering AI-enabled autonomous systems. This study analyzes the benefits, drawbacks, and uses of this integration. AI-enabled autonomous robots can use edge computing and cloud resources for real-time data processing and decision-making, improving their performance and adaptability. Communication protocols, data management, security, and scalability are examined in the ecosystem. Case studies reveal how this confluence affects robotics applications. This research shows how FCC, IoT, and AI may improve robotic systems' efficiency, intelligence, and autonomy. The article covers AI-enabled autonomous systems in transportation, manufacturing, healthcare, agriculture, and smart cities. These technologies can improve productivity and safety in many fields, from self-driving automobiles to surgical robots. Integrating these technologies raises safety, ethical decision-making, data privacy, and security concerns. The report emphasizes transparent and ethical AI algorithms, unbiased decision-making, and regulatory frameworks to enable responsible integration and mitigate dangers. In the future, AI-enabled autonomous systems will be shaped by improved AI algorithms, multi-modal sensing, human-robot collaboration, and edge intelligence. It emphasizes the necessity of interdisciplinary collaboration and ethical considerations in responsible technology development. This study concludes with a detailed analysis of fog/cloud computing, IoT, and AI in robotics, revealing the immense promise and problems of AI-enabled autonomous systems. Responsible development and collaboration can help us negotiate this transformational frontier and create a safer, more efficient, and innovative society with AI-driven autonomous systems.</p><p> </p> </section> <div class="item downloads_chart"> <h3 class="label"> Downloads </h3> <div class="value"> <canvas class="usageStatsGraph" data-object-type="Submission" data-object-id="3617"></canvas> <div class="usageStatsUnavailable" data-object-type="Submission" data-object-id="3617"> Download data is not yet available. </div> </div> </div> <section class="item references"> <h2 class="label"> References </h2> <div class="value"> <p>E. Poornima et al., “Fog robotics-based intelligence transportation system using line-of-sight intelligent transportation,” Multimed. Tools Appl., 2023, doi: 10.1007/s11042-023-15086-6. </p> <p>P. Singh and K. D. Singh, “Fog-Centric Intelligent Surveillance System: A Novel Approach for Effective and Efficient Surveillance,” in 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), 2023, pp. 762–766. </p> <p>K. D. Singh, “Securing of Cloud Infrastructure using Enterprise Honeypot,” in Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, 2021, pp. 1388–1393. doi: 10.1109/ICAC3N53548.2021.9725389. </p> <p>P. Dhiman et al., “A novel deep learning model for detection of severity level of the disease in citrus fruits,” Electronics, vol. 11, no. 3, p. 495, 2022. </p> <p>S. Tiwari, S. Kumar, and K. Guleria, “Outbreak Trends of Coronavirus Disease-2019 in India: A Prediction,” Disaster Med. Public Health Prep., vol. 14, no. 5, pp. e33–e38, 2020, doi: 10.1017/dmp.2020.115. </p> <p>P. R. Kapula, B. Pant, B. Kanwer, D. Buddhi, K. V. D. Sagar, and S. Sinthu, “Integration of AI in implementation of Wire-less Webbing: A detailed Review,” in 2023 International Conference on Artificial Intelligence and Smart Communication (AISC), 2023, pp. 983–989. </p> <p>J. Venkatesh et al., “A Complex Brain Learning Skeleton Comprising Enriched Pattern Neural Network System for Next Era Internet of Things,” J. Healthc. Eng., vol. 2023, 2023. </p> <p>D. C. Nguyen et al., “6G Internet of Things: A Comprehensive Survey,” IEEE Internet Things J., vol. 9, no. 1, pp. 359–383, 2022, doi: 10.1109/JIOT.2021.3103320. </p> <p>N. El Menbawy, H. Arafat, M. Saraya, and A. M. T. Ali-Eldin, “Studying and analyzing the fog-based internet of robotic things,” Proc. - 2020 21st Int. Arab Conf. Inf. Technol. ACIT 2020, 2020, doi: 10.1109/ACIT50332.2020.9300093. </p> <p>S. S. Kang, K. D. Singh, and S. Kumari, “Smart antenna for emerging 5G and application,” in Printed Antennas, CRC Press, 2022, pp. 249–264. </p> <p>K. D. Singh and P. Singh, “A Novel Cloud-based Framework to Predict the Employability of Students,” in 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), 2023, pp. 528–532. </p> <p>K. D. Singh, “Particle Swarm Optimization assisted Support Vector Machine based Diagnostic System for Dengue prediction at the early stage,” in Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, 2021, pp. 844–848. doi: 10.1109/ICAC3N53548.2021.9725670. </p> <p>K. D. Singh, P. Singh, and S. S. Kang, “Ensembled-based Credit Card Fraud Detection in Online Transactions,” in AIP Conference Proceedings, 2022, vol. 2555, no. 1, p. 50009. doi: 10.1063/5.0108873. </p> <p>Z. Zou, Y. Jin, P. Nevalainen, Y. Huan, J. Heikkonen, and T. Westerlund, “Edge and Fog Computing Enabled AI for IoT-An Overview,” Proc. 2019 IEEE Int. Conf. Artif. Intell. Circuits Syst. AICAS 2019, pp. 51–56, 2019, doi: 10.1109/AICAS.2019.8771621. </p> <p>R. S. Ahmed, E. S. A. Ahmed, and R. A. Saeed, “Machine learning in cyber-physical systems in industry 4.0,” Artif. Intell. Paradig. Smart Cyber-Physical Syst., pp. 20–41, 2020, doi: 10.4018/978-1-7998-5101-1.ch002. </p> <p>O. Vermesan et al., “Internet of Robotic Things Intelligent Connectivity and Platforms,” Front. Robot. AI, vol. 7, 2020, doi: 10.3389/frobt.2020.00104. </p> <p>Y. Xianjia, J. P. Queralta, J. Heikkonen, and T. Westerlund, “Federated Learning in Robotic and Autonomous Systems,” Procedia Comput. Sci., vol. 191, pp. 135–142, 2021, doi: 10.1016/j.procs.2021.07.041. </p> <p>M. Andronie et al., “Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things,” Electron., vol. 12, no. 1, 2023, doi: 10.3390/electronics12010022. </p> <p>L. Hu, Y. Miao, G. Wu, M. M. Hassan, and I. Humar, “iRobot-Factory: An intelligent robot factory based on cognitive manufacturing and edge computing,” Futur. Gener. Comput. Syst., vol. 90, pp. 569–577, 2019, doi: 10.1016/j.future.2018.08.006. </p> <p>R. S. Batth, A. Nayyar, and A. Nagpal, “Internet of Robotic Things: Driving Intelligent Robotics of Future - Concept, Architecture, Applications and Technologies,” Proc. - 4th Int. Conf. Comput. Sci. ICCS 2018, pp. 151–160, 2019, doi: 10.1109/ICCS.2018.00033. </p> <p>G. Lăzăroiu, A. Androniceanu, I. Grecu, G. Grecu, and O. Neguriță, “Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing,” Oeconomia Copernicana, vol. 13, no. 4, pp. 1047–1080, 2022, doi: 10.24136/oc.2022.030. </p> <p>Y. Chen, “IoT, cloud, big data and AI in interdisciplinary domains,” Simul. Model. Pract. Theory, vol. 102, 2020, doi: 10.1016/j.simpat.2020.102070. </p> <p>M. Salhaoui, A. Guerrero-González, M. Arioua, F. J. Ortiz, A. El Oualkadi, and C. L. Torregrosa, “Smart industrial iot monitoring and control system based on UAV and cloud computing applied to a concrete plant,” Sensors (Switzerland), vol. 19, no. 15, 2019, doi: 10.3390/s19153316. </p> <p>S. Sarker, L. Jamal, S. F. Ahmed, and N. Irtisam, “Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review,” Rob. Auton. Syst., vol. 146, 2021, doi: 10.1016/j.robot.2021.103902. </p> <p>S. K. Singh, S. Rathore, and J. H. Park, “BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence,” Futur. Gener. Comput. Syst., vol. 110, pp. 721–743, 2020, doi: 10.1016/j.future.2019.09.002. </p> <p>M. Salhaoui, “Smart IoT monitoring and real-time control based on autonomous robots, visual recognition and cloud/edge computing services,” 2021. </p> </div> </section> </div> <div class="entry_details"> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://publications.eai.eu/index.php/airo/article/view/3617/2989"> PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>12-03-2024</span> </div> </section> </div> <div class="item citation"> <section class="sub_item citation_display"> <h2 class="label"> How to Cite </h2> <div class="value"> <div id="citationOutput" role="region" aria-live="polite"> <div class="csl-bib-body"> <div class="csl-entry"><div class="csl-left-margin">[1]</div><div class="csl-right-inline">Kiran Deep Singh and P. 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Fog Cloud Computing and IoT Integration for AI enabled Autonomous Systems in Robotics | EAI Endorsed Transactions on AI and Robotics