Machine learning based computa tional offloading in the Fog Cloud Internet of Things ecosystems

Alli, Adam A.

dc.contributor.author	Alli, Adam A.
dc.date.accessioned	2024-09-11T05:09:29Z
dc.date.available	2024-09-11T05:09:29Z
dc.date.issued	2023-05-30
dc.identifier.citation	[1] ETSI. ETSI GS MEC 002. Mobile Edge Computing (MEC); Technical Re quirements. ETSI White Paper, 1:1–40, 2016. URL http://www.etsi.org/ standards-search. [2] Keith Dyer. The Mobile Network ≫ MEC architecture, 2016. URL http://the-mobile-network.com/2016/04/ etsi-establishes-the-foundations-of-mobile-edge-computing/ mec-architecture/. [3] Joshua E Siegel, Sumeet Kumar, and Sanjay E Sarma. The future internet of things: secure, efficient, and model-based. IEEE Internet of Things Journal, 5(4):2386–2398, 2018. [4] Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B Letaief. A survey on mobile edge computing: The communication perspec tive. IEEE Communications Surveys & Tutorials, 19(4):2322–2358, 2017. [5] Massimo Ficco, Christian Esposito, Yang Xiang, and Francesco Palmieri. Pseudo-dynamic testing of realistic edge-fog cloud ecosystems. IEEE Com munications Magazine, 55(11):98–104, 2017. [6] Shanhe Yi, Zhengrui Qin, and Qun Li. Security and privacy issues of fog computing: A survey. In International conference on wireless algorithms, systems, and applications, pages 685–695. Springer, 2015. [7] Cisco. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021 White Paper - Cisco, 2016. URL https: //www.cisco.com/c/en/us/solutions/collateral/service-provider/ visual-networking-index-vni/mobile-white-paper-c11-520862. html. 119 Bibliography 120 [8] Habib Ur Rehman, Muhammad Asif, and Mudassar Ahmad. Future appli cations and research challenges of iot. In 2017 International Conference on Information and Communication Technologies (ICICT), pages 68–74. IEEE, 2017. [9] Ahmad-Reza Sadeghi, Christian Wachsmann, and Michael Waidner. Se curity and privacy challenges in industrial internet of things. In Design Automation Conference (DAC), 2015 52nd ACM/EDAC/IEEE, pages 1–6. IEEE, 2015. [10] Peter Mell, Tim Grance, et al. The nist definition of cloud computing. National institute of standards and technology, 53(6):50, 2009. [11] Karthik Kumar and Yung-Hsiang Lu. Cloud computing for mobile users: Can offloading computation save energy? Computer, 43(4):51–56, 2010. [12] Yuan Ai, Mugen Peng, and Kecheng Zhang. Edge computing technologies for internet of things: a primer. Digital Communications and Networks, 4 (2):77–86, 2018. [13] Hany Atlam, Robert Walters, and Gary Wills. Fog computing and the internet of things: A review. Big Data and Cognitive Computing, 2(2):10, 2018. [14] Ashkan Yousefpour, Genya Ishigaki, Riti Gour, and Jason P Jue. On reduc ing iot service delay via fog offloading. IEEE Internet of Things Journal, 2018. [15] Pedro Garcia Lopez, Alberto Montresor, Dick Epema, Anwitaman Datta, Teruo Higashino, Adriana Iamnitchi, Marinho Barcellos, Pascal Felber, and Etienne Riviere. Edge-centric computing: Vision and challenges. ACM SIGCOMM Computer Communication Review, 45(5):37–42, 2015. [16] Minghui Min, Dongjin Xu, Liang Xiao, Yuliang Tang, and Di Wu. Learning based computation offloading for iot devices with energy harvesting. arXiv preprint arXiv:1712.08768, 2017. [17] Mohammad Aazam and Eui-Nam Huh. Fog computing micro datacenter based dynamic resource estimation and pricing model for iot. In Advanced Information Networking and Applications (AINA), 2015 IEEE 29th Inter national Conference on, pages 687–694. IEEE, 2015. Bibliography 121 [18] Subhadeep Sarkar, Subarna Chatterjee, and Sudip Misra. Assessment of the suitability of fog computing in the context of internet of things. IEEE Transactions on Cloud Computing, 6(1):46–59, 2018. [19] sdxcentral. Mobile edge computing vs. multi-access edge computing. sdx central.com, 2018. URL https://www.sdxcentral.com/mec/definitions/ mobile-edge-computing-vs-multi-access-edge-computing. [20] Afnan Fahim, Abderrahmen Mtibaa, and Khaled A Harras. Making the case for computational offloading in mobile device clouds. In Proceedings of the 19th annual international conference on Mobile computing & networking, pages 203–205. ACM, 2013. [21] Clinton Dsouza, Gail-Joon Ahn, and Marthony Taguinod. Policy-driven se curity management for fog computing: Preliminary framework and a case study. In Information Reuse and Integration (IRI), 2014 IEEE 15th Inter national Conference on, pages 16–23. IEEE, 2014. [22] Sotiris B Kotsiantis, I Zaharakis, and P Pintelas. Supervised machine learn ing: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160:3–24, 2007. [23] Real Carbonneau, Kevin Laframboise, and Rustam Vahidov. Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3):1140–1154, 2008. [24] Konstantina Kourou, Themis P Exarchos, Konstantinos P Exarchos, Michalis V Karamouzis, and Dimitrios I Fotiadis. Machine learning ap plications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13:8–17, 2015. [25] Geoffrey I Webb, Michael J Pazzani, and Daniel Billsus. Machine learning for user modeling. User modeling and user-adapted interaction, 11(1-2):19–29, 2001. [26] Thuy TT Nguyen and Grenville J Armitage. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys and Tutorials, 10(1-4):56–76, 2008. [27] John M Mulvey. Machine learning and financial planning. IEEE Potentials, 36(6):8–13, 2017. Bibliography 122 [28] Rahul C Deo. Machine learning in medicine. Circulation, 132(20):1920–1930, 2015. [29] Gianni D’Angelo and Salvatore Rampone. Diagnosis of aerospace structure defects by a hpc implemented soft computing algorithm. In 2014 IEEE Metrology for Aerospace (MetroAeroSpace), pages 408–412. IEEE, 2014. [30] Gianni D’Angelo and Salvatore Rampone. Feature extraction and soft com puting methods for aerospace structure defect classification. Measurement, 85:192–209, 2016. [31] Tara A Estlin and Raymond J Mooney. Learning to improve both efficiency and quality of planning. In IJCAI, pages 1227–1233, 1997. [32] Knud Lasse Lueth. State of the IoT 2018: Number of IoT devices now at 7B – Market accelerating, 2018. URL https://iot-analytics.com/ state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/. [33] Adam A Alli and Muhammad Mahbub Alam. Secoff-fciot: Machine learning based secure offloading in fog-cloud of things for smart city applications. Internet of Things, 7:100070, 2019. [34] Yang Yang. Multi-tier computing networks for intelligent iot. Nature Elec tronics, 2(1):4, 2019. [35] Syed Noorulhassan Shirazi, Antonios Gouglidis, Arsham Farshad, and David Hutchison. The extended cloud: Review and analysis of mobile edge com puting and fog from a security and resilience perspective. IEEE Journal on Selected Areas in Communications, 35(11):2586–2595, 2017. [36] Mithun Mukherjee, Lei Shu, and Di Wang. Survey of fog computing: Fun damental, network applications, and research challenges. IEEE Communi cations Surveys & Tutorials, 20(3):1826–1857, 2018. [37] Carlo Puliafito, Enzo Mingozzi, Francesco Longo, Antonio Puliafito, and Omer Rana. Fog computing for the internet of things: A survey. ACM Transactions on Internet Technology (TOIT), 19(2):18, 2019. [38] Ranesh Kumar Naha, Saurabh Garg, Dimitrios Georgakopoulos, Prem Prakash Jayaraman, Longxiang Gao, Yong Xiang, and Rajiv Ranjan. Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE access, 6:47980–48009, 2018. Bibliography 123 [39] Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami. Internet of things (iot): A vision, architectural elements, and future directions. Future generation computer systems, 29(7):1645–1660, 2013. [40] Orlando Arias, Jacob Wurm, Khoa Hoang, and Yier Jin. Privacy and se curity in internet of things and wearable devices. IEEE Transactions on Multi-Scale Computing Systems, 1(2):99–109, 2015. [41] Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pages 13–16. ACM, 2012. [42] Daniele Catteddu. Cloud computing: benefits, risks and recommendations for information security. In Web application security, pages 17–17. Springer, 2010. [43] IEEE Standard Association et al. Ieee 1934-2018-ieee standard for adoption of openfog reference architecture for fog computing, 2018. [44] Pengfei Hu, Sahraoui Dhelim, Huansheng Ning, and Tie Qiu. Survey on fog computing: architecture, key technologies, applications and open issues. Journal of Network and Computer Applications, 98:27–42, 2017. [45] Mung Chiang and Tao Zhang. Fog and iot: An overview of research oppor tunities. IEEE Internet of Things Journal, 3(6):854–864, 2016. [46] Dongxin Lu and Tao Liu. The application of iot in medical system. In 2011 IEEE International Symposium on IT in Medicine and Education, volume 1, pages 272–275. IEEE, 2011. [47] K Natarajan, B Prasath, and P Kokila. Smart health care system using internet of things. Journal of Network Communications and Emerging Tech nologies (JNCET) www. jncet. org, 6(3), 2016. [48] Prasanth Lade, Rumi Ghosh, and Soundar Srinivasan. Manufacturing an alytics and industrial internet of things. IEEE Intelligent Systems, 32(3): 74–79, 2017. Bibliography 124 [49] Khadija Akherfi, Micheal Gerndt, and Hamid Harroud. Mobile cloud com puting for computation offloading: Issues and challenges. Applied computing and informatics, 14(1):1–16, 2018. [50] Manas Kumar Yogi, K Chandrasekhar, and G Vijay Kumar. Mist computing: Principles, trends and future direction. arXiv preprint arXiv:1709.06927, 2017. [51] Ambrose McNevin. Edge computing in broadcasting — Industry Trends — IBC, 2017. URL https://www.ibc.org/tech-advances/ edge-computing-in-broadcasting/1881.article. [52] OpenfogConsortium. OpenFog Reference Architecture for Fog Computing . Reference Architecture, 1(February):1–162, 2017. ISSN 2047-4954. doi: 10.1049/iet-net.2015.0034. [53] Dario Sabella, Alessandro Vaillant, Pekka Kuure, Uwe Rauschenbach, and Fabio Giust. Mobile-edge computing architecture: The role of mec in the internet of things. IEEE Consumer Electronics Magazine, 5:84–91, 10 2016. doi: 10.1109/MCE.2016.2590118. [54] By Dario Sabella, Alessandro Vaillant, Pekka Kuure, and Uwe Rauschen bach. Mobile-Edge Computing Architecture: The role of MEC in the Inter net of Things. IEEE Consumer ElEctronics magazine, 1(September):84–91, 2016. doi: 10.1109/MCE.2016.2590118. [55] Andrea Giordano, Giandomenico Spezzano, and Andrea Vinci. Smart agents and fog computing for smart city applications. In International Conference on Smart Cities, pages 137–146. Springer, 2016. [56] Behailu Negash, Tuan Nguyen Gia, Arman Anzanpour, Iman Azimi, Mingzhe Jiang, Tomi Westerlund, Amir M Rahmani, Pasi Liljeberg, and Hannu Tenhunen. Leveraging fog computing for healthcare iot. In Fog Computing in the Internet of Things, pages 145–169. Springer, 2018. [57] Chao Zhu, Giancarlo Pastor, Yu Xiao, and Antti Ylajaaski. Vehicular fog computing for video crowdsourcing: Applications, feasibility, and challenges. IEEE Communications Magazine, 56(10):58–63, 2018. [58] Ashish Rauniyar, Paal Engelstad, Boning Feng, et al. Crowdsourcing-based disaster management using fog computing in internet of things paradigm. Bibliography 125 In 2016 IEEE 2nd international conference on collaboration and internet computing (CIC), pages 490–494. IEEE, 2016. [59] Jianhua He, Jian Wei, Kai Chen, Zuoyin Tang, Yi Zhou, and Yan Zhang. Multitier fog computing with large-scale iot data analytics for smart cities. IEEE Internet of Things Journal, 5(2):677–686, 2017. [60] Nanxi Chen, Yang Yang, Tao Zhang, Ming-Tuo Zhou, Xiliang Luo, and John K Zao. Fog as a service technology. IEEE Communications Magazine, 56(11):95–101, 2018. [61] Martin Strohbach, Holger Ziekow, Vangelis Gazis, and Navot Akiva. To wards a big data analytics framework for iot and smart city applications. In Modeling and processing for next-generation big-data technologies, pages 257–282. Springer, 2015. [62] Juma Kasadha, Adam A Alli, Aisha Kasujja Basuuta, and Abdulhamid Mpoza. Social media taxation and its impact on africa’s economic growth. Journal of Public Affairs, page e2004, 2019. [63] Yin Zhang, Xiao Ma, Jing Zhang, M Shamim Hossain, Ghulam Muhammad, and Syed Umar Amin. Edge intelligence in the cognitive internet of things: Improving sensitivity and interactivity. IEEE Network, 33(3):58–64, 2019. [64] Mehdi Mohammadi and Ala Al-Fuqaha. Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. IEEE Communications Magazine, 56(2):94–101, 2018. [65] Amir M Rahmani, Tuan Nguyen Gia, Behailu Negash, Arman Anzanpour, Iman Azimi, Mingzhe Jiang, and Pasi Liljeberg. Exploiting smart e-health gateways at the edge of healthcare internet-of-things: A fog computing ap proach. Future Generation Computer Systems, 78:641–658, 2018. [66] Rabindra Kumar Barik, Harishchandra Dubey, Kunal Mankodiya, Sapana Ashok Sasane, and Chinmaya Misra. Geofog4health: a fog-based sdi framework for geospatial health big data analysis. Journal of Ambient Intelligence and Humanized Computing, 10(2):551–567, 2019. [67] Hasan Ali Khattak, Hafsa Arshad, Saif ul Islam, Ghufran Ahmed, Sohail Jabbar, Abdullahi Mohamud Sharif, and Shehzad Khalid. Utilization and Bibliography 126 load balancing in fog servers for health applications. EURASIP Journal on Wireless Communications and Networking, 2019(1):91, 2019. [68] Ida Syafiza M Isa, Mohamed OI Musa, Taisir EH El-Gorashi, Ahmed Q Lawey, and Jaafar MH Elmirghani. Energy efficiency of fog computing health monitoring applications. In 2018 20th International Conference on Trans parent Optical Networks (ICTON), pages 1–5. IEEE, 2018. [69] Pedro H Vilela, Joel JPC Rodrigues, Petar Solic, Kashif Saleem, and Vasco Furtado. Performance evaluation of a fog-assisted iot solution for e-health applications. Future Generation Computer Systems, 97:379–386, 2019. [70] Gunasekaran Raja and Anil Thomas. Safer: Crowdsourcing based disaster monitoring system using software defined fog computing. Mobile Networks and Applications, pages 1–11, 2019. [71] Tiago M Fern´andez-Caram´es, Iv´an Froiz-M´ıguez, Oscar Blanco-Novoa, and Paula Fraga-Lamas. Enabling the internet of mobile crowdsourcing health things: A mobile fog computing, blockchain and iot based continuous glucose monitoring system for diabetes mellitus research and care. Sensors, 19(15): 3319, 2019. [72] Fatimah Alghamdi, Saoucene Mahfoudh, and Ahmed Barnawi. A novel fog computing based architecture to improve the performance in content delivery networks. Wireless Communications and Mobile Computing, 2019, 2019. [73] Aparna Kumari, Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar, Reza M Parizi, and Kim-Kwang Raymond Choo. Fog data analytics: a taxonomy and process model. Journal of Network and Computer Applications, 128: 90–104, 2019. [74] Cheol-Ho Hong and Blesson Varghese. Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Computing Surveys (CSUR), 52(5):1–37, 2019. [75] Dimitri Belli, Stefano Chessa, Burak Kantarci, and Luca Foschini. Toward fog-based mobile crowdsensing systems: State of the art and opportunities. IEEE Communications Magazine, 57(12):78–83, 2019. Bibliography 127 [76] Yaohua Sun, Mugen Peng, and Shiwen Mao. Deep reinforcement learning based mode selection and resource management for green fog radio access networks. IEEE Internet of Things Journal, 6(2):1960–1971, 2018. [77] Tian Wang, Yuzhu Liang, Weijia Jia, Muhammad Arif, Anfeng Liu, and Mande Xie. Coupling resource management based on fog computing in smart city systems. Journal of Network and Computer Applications, 135:11–19, 2019. [78] Mohit Taneja and Alan Davy. Resource aware placement of data analytics platform in fog computing. Procedia Computer Science, 97:153–156, 2016. [79] Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. Edge affinity-based management of applications in fog computing environments. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing, pages 61–70, 2019. [80] Abebe Abeshu and Naveen Chilamkurti. Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communica tions Magazine, 56(2):169–175, 2018. [81] Tian Wang, Guangxue Zhang, Md Zakirul Alam Bhuiyan, Anfeng Liu, Wei jia Jia, and Mande Xie. A novel trust mechanism based on fog computing in sensor–cloud system. Future Generation Computer Systems, 2018. [82] Adam A Alli, Mugigayi Fahadi, and Cherotwo Atebeni. Blockchain and fog computing: Fog-blockchain concept, opportunities and challenges. Blockchain in Data Analytics, page 75, 2020. [83] Esubalew Alemneh, Sidi-Mohammed Senouci, Philippe Brunet, and Tesfa Tegegne. A two-way trust management system for fog computing. Future Generation Computer Systems, 106:206–220, 2020. [84] Wassim Masri, Ismaeel Al Ridhawi, Nour Mostafa, and Pardis Pourghomi. Minimizing delay in iot systems through collaborative fog-to-fog (f2f) com munication. In 2017 Ninth International Conference on Ubiquitous and Fu ture Networks (ICUFN), pages 1005–1010. IEEE, 2017. [85] Dinh Nguyen, Zhishu Shen, Jiong Jin, and Atsushi Tagami. Icn-fog: An information-centric fog-to-fog architecture for data communications. In Bibliography 128 GLOBECOM 2017-2017 IEEE Global Communications Conference, pages 1–6. IEEE, 2017. [86] Bhawna Suri, Shweta Taneja, Hemankur Bhardwaj, Prateek Gupta, and Udit Ahuja. Peering through the fog: an inter-fog communication approach for computing environment. In International conference on innovative com puting and communications, pages 73–81. Springer, 2019. [87] Paola G Vinueza Naranjo, Zahra Pooranian, Mohammad Shojafar, Mauro Conti, and Rajkumar Buyya. Focan: A fog-supported smart city network architecture for management of applications in the internet of everything environments. Journal of Parallel and Distributed Computing, 132:274–283, 2019. [88] Simar Preet Singh, Anand Nayyar, Rajesh Kumar, and Anju Sharma. Fog computing: from architecture to edge computing and big data processing. The Journal of Supercomputing, 75(4):2070–2105, 2019. [89] Elena Hernandez-Nieves, Guillermo Hern´andez, Ana-Bel´en Gil-Gonz´alez, Sara Rodr´ıguez-Gonz´alez, and Juan M Corchado. Fog computing architec ture for personalized recommendation of banking products. Expert Systems with Applications, 140:112900, 2020. [90] Neda Maleki, Mohammad Loni, Masoud Daneshtalab, Mauro Conti, and Hossein Fotouhi. Sofa: A spark-oriented fog architecture. In IECON 2019- 45th Annual Conference of the IEEE Industrial Electronics Society, vol ume 1, pages 2792–2799. IEEE, 2019. [91] Pradip Kumar Sharma, Mu-Yen Chen, and Jong Hyuk Park. A software defined fog node based distributed blockchain cloud architecture for iot. Ieee Access, 6:115–124, 2017. [92] Rabindra K Barik, Harishchandra Dubey, Chinmaya Misra, Debanjan Borthakur, Nicholas Constant, Sapana Ashok Sasane, Rakesh K Lenka, Bha bani Shankar Prasad Mishra, Himansu Das, and Kunal Mankodiya. Fog assisted cloud computing in era of big data and internet-of-things: systems, architectures, and applications. In Cloud computing for optimization: foun dations, applications, and challenges, pages 367–394. Springer, 2018. Bibliography 129 [93] Karthik Kumar, Jibang Liu, Yung-Hsiang Lu, and Bharat Bhargava. A survey of computation offloading for mobile systems. Mobile Networks and Applications, 18(1):129–140, 2013. [94] Ting-Yi Lin, Ting-An Lin, Cheng-Hsin Hsu, and Chung-Ta King. Context aware decision engine for mobile cloud offloading. In Wireless Communica tions and Networking Conference Workshops (WCNCW), 2013 IEEE, pages 111–116. IEEE, 2013. [95] Ivan Stojmenovic. Fog computing: A cloud to the ground support for smart things and machine-to-machine networks. In Telecommunication Networks and Applications Conference (ATNAC), 2014 Australasian, pages 117–122. IEEE, 2014. [96] Daniel E O’Leary. Big data’, the ‘internet of things’and the ‘internet of signs. Intelligent Systems in Accounting, Finance and Management, 20(1): 53–65, 2013. [97] Tim Stack. Internet of things (iot) data continues to explode ex ponentially. who is using that data and how? — cisco blog — cisco systems, 2018. URL https://blogs.cisco.com/datacenter/ internet-of-things-iot-data-continues-to-explode-exponentially-who-is-using-that-data-and-how. [98] Shanhe Yi, Cheng Li, and Qun Li. A survey of fog computing: concepts, applications and issues. In Proceedings of the 2015 workshop on mobile big data, pages 37–42. ACM, 2015. [99] matt Watson. What Is Function-as-a-Service? Serverless Ar chitectures Are Here!, 2017. URL https://stackify.com/ function-as-a-service-serverless-architecture/. [100] Noah Slater. Backend as a Service, 2014. URL https://www.engineyard. com/blog/backend-as-a-service. [101] Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, and Sue Moon. I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pages 1–14. ACM, 2007. [102] Tuyen X Tran, Parul Pandey, Abolfazl Hajisami, and Dario Pompili. Collab orative multi-bitrate video caching and processing in mobile-edge computing Bibliography 130 networks. In Wireless On-demand Network Systems and Services (WONS), 2017 13th Annual Conference on, pages 165–172. IEEE, 2017. [103] Shan Zhang, Peter He, Katsuya Suto, Peng Yang, Lian Zhao, and Xuemin Shen. Cooperative edge caching in user-centric clustered mobile networks. IEEE Transactions on Mobile Computing, 17(8):1791–1805, 2018. [104] Engin Zeydan, Ejder Bastug, Mehdi Bennis, Manhal Abdel Kader, Ilyas Alper Karatepe, Ahmet Salih Er, and M´erouane Debbah. Big data caching for networking: Moving from cloud to edge. IEEE Communications Magazine, 54(9):36–42, 2016. [105] David Applegate, Aaron Archer, Vijay Gopalakrishnan, Seungjoon Lee, and Kadangode K Ramakrishnan. Optimal content placement for a large-scale vod system. In Proceedings of the 6th International COnference, page 4. ACM, 2010. [106] Tarik Taleb, Konstantinos Samdanis, Badr Mada, Hannu Flinck, Sunny Dutta, and Dario Sabella. On Multi-Access Edge Computing: A Sur vey of the Emerging 5G Network Edge Cloud Architecture and Orches tration. IEEE Communications Surveys and Tutorials, 19(3):1657–1681, 2017. ISSN 1553877X. doi: 10.1109/COMST.2017.2705720. URL http: //ieeexplore.ieee.org/document/7931566/. [107] Peter Graffagnino, Dave Hyatt, Richard Blanchard, Kevin Calhoun, Gilles Drieu, Maciej Stachowiak, Don Melton, and Darin Adler. Methods and apparatuses for providing a hardware accelerated web engine, July 31 2012. US Patent 8,234,392. [108] Noriyuki Takahashi, Hiroyuki Tanaka, and Ryutaro Kawamura. Analysis of process assignment in multi-tier mobile cloud computing and application to edge accelerated web browsing. In Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2015 3rd IEEE International Conference on, pages 233–234. IEEE, 2015. [109] Azarias Reda, Edward Cutrell, and Brian Noble. Towards improved web acceleration: leveraging the personal web. In Proceedings of the 5th ACM workshop on Networked systems for developing regions, pages 57–62. ACM, 2011. Bibliography 131 [110] Radovan Novotn`y, Radek Kuchta, and Jaroslav Kadlec. Smart city con cept, applications and services. Journal of Telecommunications System & Management, 3(2):1, 2014. [111] Oran Chieochan, Anukit Saokaew, and Ekkarat Boonchieng. Iot for smart farm: A case study of the lingzhi mushroom farm at maejo university. In 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), pages 1–6. IEEE, 2017. [112] Maxim Chernyshev, Zubair Baig, Oladayo Bello, and Sherali Zeadally. In ternet of things (iot): Research, simulators, and testbeds. IEEE Internet of Things Journal, 5(3):1637–1647, 2017. [113] Harshit Gupta, Amir Vahid Dastjerdi, Soumya K Ghosh, and Rajkumar Buyya. ifogsim: A toolkit for modeling and simulation of resource manage ment techniques in the internet of things, edge and fog computing environ ments. Software: Practice and Experience, 47(9):1275–1296, 2017. [114] A Kertesz, T Pflanzner, and T Gyimothy. A mobile iot device simulator for iot-fog-cloud systems. Journal of Grid Computing, pages 1–23, 2018. [115] George F Riley and Thomas R Henderson. The ns-3 network simulator. In Modeling and tools for network simulation, pages 15–34. Springer, 2010. [116] Antonio Coutinho, Fabiola Greve, Cassio Prazeres, and Joao Cardoso. Fogbed: A rapid-prototyping emulation environment for fog computing. In 2018 IEEE International Conference on Communications (ICC), pages 1–7. IEEE, 2018. [117] Yukun Zeng, Mengyuan Chao, and Radu Stoleru. Emuedge: A hybrid em ulator for reproducible and realistic edge computing experiments. In 2019 IEEE International Conference on Fog Computing (ICFC), pages 153–164. IEEE, 2019. [118] ChingYao Huang, John Zao, Chih-Yuan Huang, Kha Tho Nguyen, Yang Yang, Ming Tou Zhou, Xiliang Luo, Jen-Shun Yang, Vince Hsu, Yi-Chieh Pe ter Chang, et al. Trusted worthy fog computing testbed developed in great china region. In 2017 IEEE Fog World Congress (FWC), pages 1–6. IEEE, 2017. Bibliography 132 [119] Antonio Brogi and Stefano Forti. Qos-aware deployment of iot applications through the fog. IEEE Internet of Things Journal, 4(5):1185–1192, 2017. [120] Jasenka Dizdarevi´c, Francisco Carpio, Admela Jukan, and Xavi Masip Bruin. A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration. ACM Computing Surveys (CSUR), 51(6):116, 2019. [121] Redowan Mahmud, Fernando Luiz Koch, and Rajkumar Buyya. Cloud fog interoperability in iot-enabled healthcare solutions. In Proceedings of the 19th international conference on distributed computing and networking, page 32. ACM, 2018. [122] Cindy-Pamela Lopez, Marco Sant´orum, and Jose Aguilar. Autonomous cy cles of collaborative processes for integration based on industry 4.0. In Inter national Conference on Information Technology & Systems, pages 177–186. Springer, 2019. [123] Arslan Munir, Prasanna Kansakar, and Samee U Khan. Ifciot: Integrated fog cloud iot: A novel architectural paradigm for the future internet of things. IEEE Consumer Electronics Magazine, 6(3):74–82, 2017. [124] Vittorio Cozzolino, Aaron Yi Ding, Jorg Ott, and Dirk Kutscher. Enabling fine-grained edge offloading for iot. In Proceedings of the SIGCOMM Posters and Demos, pages 124–126. ACM, 2017. [125] B Di Martino, M Rak, M Ficco, A Esposito, SA Maisto, and S Nacchia. Internet of things reference architectures, security and interoperability: A survey. Internet of Things, 1:99–112, 2018. [126] Mohammad Aazam, Imran Khan, Aymen Abdullah Alsaffar, and Eui-Nam Huh. Cloud of things: Integrating internet of things and cloud computing and the issues involved. In Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad, Pak istan, 14th-18th January, 2014, pages 414–419. IEEE, 2014. [127] Jiehan Zhou, Teemu Leppanen, Erkki Harjula, Mika Ylianttila, Timo Ojala, Chen Yu, Hai Jin, and Laurence Tianruo Yang. Cloudthings: A common architecture for integrating the internet of things with cloud computing. In Proceedings of the 2013 IEEE 17th international conference on computer supported cooperative work in design (CSCWD), pages 651–657. IEEE, 2013. Bibliography 133 [128] Shweta Kaushik and Charu Gandhi. Fog vs. cloud computing architecture. In Advancing Consumer-Centric Fog Computing Architectures, pages 87–110. IGI Global, 2019. [129] Dai H Tran, Nguyen H Tran, Chuan Pham, SM Ahsan Kazmi, Eui-Nam Huh, and Choong Seon Hong. Oaas: offload as a service in fog networks. Computing, 99(11):1081–1104, 2017. [130] Said El Kafhali and Khaled Salah. Efficient and dynamic scaling of fog nodes for iot devices. The Journal of Supercomputing, 73(12):5261–5284, 2017. [131] Rodrigo Roman, Javier Lopez, and Masahiro Mambo. Mobile edge com puting, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78:680–698, 2018. [132] Yunsik Son, Junho Jeong, and YangSun Lee. An adaptive offloading method for an iot-cloud converged virtual machine system using a hybrid deep neural network. Sustainability, 10(11):3955, 2018. [133] Liang Xiao, Xiaoyue Wan, Xiaozhen Lu, Yanyong Zhang, and Di Wu. Iot security techniques based on machine learning: How do iot devices use ai to enhance security? IEEE Signal Processing Magazine, 35(5):41–49, 2018. [134] Duc Van Le and Chen-Khong Tham. A deep reinforcement learning based offloading scheme in ad-hoc mobile clouds. In IEEE INFOCOM 2018- IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 760–765. IEEE, 2018. [135] Shuai Yu, Xin Wang, and Rami Langar. Computation offloading for mobile edge computing: a deep learning approach. In Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017 IEEE 28th Annual International Symposium on, pages 1–6. IEEE, 2017. [136] Zhikai Kuang, Songtao Guo, Jiadi Liu, and Yuanyuan Yang. A quick response framework for multi-user computation offloading in mobile cloud computing. Future Generation Computer Systems, 81:166–176, 2018. [137] F Zhang, J Ge, Z Li, C Li, Z Huang, L Kong, and B Luo. Task offloading for scientific workflow application in mobile cloud. In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), pages 136–148, 2017. Bibliography 134 [138] Dmitry Kozyrev, Aleksandr Ometov, Dmitri Moltchanov, Vladimir Rykov, Dmitry Efrosinin, Tatiana Milovanova, Sergey Andreev, and Yevgeni Kouch eryavy. Mobility-centric analysis of communication offloading for hetero geneous internet of things devices. Wireless Communications and Mobile Computing, 2018, 2018. [139] Huber Flores, Xiang Su, Vassilis Kostakos, Aaron Yi Ding, Petteri Nurmi, Sasu Tarkoma, Pan Hui, and Yong Li. Large-scale offloading in the internet of things. In Pervasive Computing and Communications Workshops (Per Com Workshops), 2017 IEEE International Conference on, pages 479–484. IEEE, 2017. [140] Xiao Ma, Chuang Lin, Han Zhang, and Jianwei Liu. Energy-aware com putation offloading of iot sensors in cloudlet-based mobile edge computing. Sensors, 18(6):1945, 2018. [141] Sungwook Kim. Nested game-based computation offloading scheme for mo bile cloud iot systems. EURASIP Journal on Wireless Communications and Networking, 2015(1):229, 2015. [142] Jie Zhang, Zhili Zhou, Shu Li, Leilei Gan, Xuyun Zhang, Lianyong Qi, Xiao long Xu, and Wanchun Dou. Hybrid computation offloading for smart home automation in mobile cloud computing. Personal and Ubiquitous Computing, 22(1):121–134, 2018. [143] AN Gnana Jeevan and MA Maluk Mohamed. Dyto: Dynamic task offloading strategy for mobile cloud computing using surrogate object model. Interna tional Journal of Parallel Programming, pages 1–17, 2018. [144] Gianni D’Angelo and Salvatore Rampone. Cognitive distributed applica tion area networks. In Security and Resilience in Intelligent Data-Centric Systems and Communication Networks, pages 193–214. Elsevier, 2018. [145] Jean Caminha, Angelo Perkusich, and Mirko Perkusich. A smart trust man agement method to detect on-off attacks in the internet of things. Security and Communication Networks, 2018, 2018. [146] Yinghui Zhang, Jiangfan Zhao, Dong Zheng, Kaixin Deng, Fangyuan Ren, Xiaokun Zheng, and Jiangang Shu. Privacy-preserving data aggregation against false data injection attacks in fog computing. Sensors, 18(8):2659, 2018. Bibliography 135 [147] Manuel Su´arez-Albela, Tiago M Fern´andez-Caram´es, Paula Fraga-Lamas, and Luis Castedo. A practical evaluation of a high-security energy-efficient gateway for iot fog computing applications. Sensors, 17(9):1978, 2017. [148] Luis Belem Pacheco, Eduardo Pelinson Alchieri, and Priscila Mendez Bar reto. Device-based security to improve user privacy in the internet of things. Sensors, 18(8):2664, 2018. [149] Roohie Naaz Mir et al. Resource management in pervasive internet of things: A survey. Journal of King Saud University-Computer and Information Sci ences, 2018. [150] Bhaskar Prasad Rimal, Eunmi Choi, and Ian Lumb. A taxonomy and survey of cloud computing systems. In 2009 Fifth International Joint Conference on INC, IMS and IDC, pages 44–51. Ieee, 2009. [151] Yuhui Shi and Russell C Eberhart. Empirical study of particle swarm opti mization. In Proceedings of the 1999 Congress on Evolutionary Computation CEC99 (Cat. No. 99TH8406), volume 3, pages 1945–1950. IEEE, 1999. [152] Yuhui Shi et al. Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 Congress on Evolutionary Com putation (IEEE Cat. No. 01TH8546), volume 1, pages 81–86. IEEE, 2001. [153] Abdulwahab A Alnaqi, Hossein Moayedi, Amin Shahsavar, and Truong Khang Nguyen. Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Conversion and Management, 183:137–148, 2019. [154] Xudong Ye, Bing Chen, Liang Jing, Baiyu Zhang, and Yong Liu. Multi-agent hybrid particle swarm optimization (mahpso) for wastewater treatment net work planning. Journal of environmental management, 234:525–536, 2019. [155] Hao Pu, Taoran Song, Paul Schonfeld, Wei Li, Hong Zhang, Jianping Hu, Xianbao Peng, and Jie Wang. Mountain railway alignment optimization using stepwise & hybrid particle swarm optimization incorporating genetic operators. Applied Soft Computing, 2019. Bibliography 136 [156] K Thangaramya, K Kulothungan, R Logambigai, M Selvi, S Ganapathy, and A Kannan. Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in iot. Computer Networks, 2019. [157] George S Atsalakis and Kimon P Valavanis. Surveying stock market fore casting techniques–part ii: Soft computing methods. Expert Systems with Applications, 36(3):5932–5941, 2009. [158] Navneet Walia, Harsukhpreet Singh, and Anurag Sharma. Anfis: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications, 123(13), 2015. [159] Jose Vieira, F Morgado Dias, and Alexandre Mota. Neuro-fuzzy systems: a survey. In 5th WSEAS NNA international conference on neural networks and applications, Udine, Italia, pages 87–92, 2004. [160] Christopher JCH Watkins and Peter Dayan. Q-learning. Machine learning, 8(3-4):279–292, 1992. [161] Hado Van Hasselt, Arthur Guez, and David Silver. Deep reinforcement learning with double q-learning. Thirtieth AAAI Conference on Artificial Intelligence, 13(7):2094–2100, 2016. [162] Sascha Lange, Martin Riedmiller, and Arne Voigtl¨ander. Autonomous re inforcement learning on raw visual input data in a real world application. In The 2012 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2012. [163] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. [164] Robert H Crites and Andrew G Barto. Improving elevator performance using reinforcement learning. In Advances in neural information processing systems, pages 1017–1023, 1996. [165] Petar Kormushev, Sylvain Calinon, and Darwin Caldwell. Reinforcement learning in robotics: Applications and real-world challenges. Robotics, 2(3): 122–148, 2013. Bibliography 137 [166] Kehua Su, Jie Li, and Hongbo Fu. Smart city and the applications. In 2011 international conference on electronics, communications and control (ICECC), pages 1028–1031. IEEE, 2011. [167] Jiong Jin, Jayavardhana Gubbi, Slaven Marusic, and Marimuthu Palaniswami. An information framework for creating a smart city through internet of things. IEEE Internet of Things journal, 1(2):112–121, 2014. [168] Tongxiang Wang, Xianglin Wei, Chaogang Tang, and Jianhua Fan. Effi cient multi-tasks scheduling algorithm in mobile cloud computing with time constraints. Peer-to-Peer Networking and Applications, 11(4):793–807, 2018. [169] Yucen Nan, Wei Li, Wei Bao, Flavia C Delicato, Paulo F Pires, Yong Dou, and Albert Y Zomaya. Adaptive energy-aware computation offloading for cloud of things systems. IEEE Access, 5:23947–23957, 2017. [170] Chaogang Tang, Shixiong Xia, Qing Li, Wei Chen, and Weidong Fang. Re source pooling in vehicular fog computing. Journal of Cloud Computing, 10 (1):1–14, 2021. [171] Adam A Alli and Muhammad Mahbub Alam. The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications. Internet of Things, page 100177, 2020. [172] Pedro Garc´ıa L´opez, Marc S´anchez-Artigas, Gerard Par´ıs, Daniel Barcelona Pons, Alvaro Ruiz Ollobarren, and David Arroyo Pinto. Comparison of ´ faas orchestration systems. In 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), pages 148– 153. IEEE, 2018. [173] Ayed Salman, Imtiaz Ahmad, and Sabah Al-Madani. Particle swarm opti mization for task assignment problem. Microprocessors and Microsystems, 26(8):363–371, 2002. [174] Kenneth A De Jong and William M Spears. Using genetic algorithms to solve np-complete problems. In ICGA, pages 124–132, 1989. [175] Thinh Quang Dinh, Jianhua Tang, Quang Duy La, and Tony QS Quek. Offloading in mobile edge computing: Task allocation and computational frequency scaling. IEEE Transactions on Communications, 65(8):3571–3584, 2017. Bibliography 138 [176] Zhenyu Zhou, Pengju Liu, Junhao Feng, Yan Zhang, Shahid Mumtaz, and Jonathan Rodriguez. Computation resource allocation and task assignment optimization in vehicular fog computing: A contract-matching approach. IEEE Transactions on Vehicular Technology, 68(4):3113–3125, 2019. [177] Yi-Hsuan Kao and Bhaskar Krishnamachari. Optimizing mobile computa tional offloading with delay constraints. In 2014 IEEE Global Communica tions Conference, pages 2289–2294. IEEE, 2014. [178] Adam A Alli, Kalinaki Kassim, Nambobi Mutwalibi, Habiba Hamid, and Lwembawo Ibrahim. Secure fog-cloud of things: Architectures, opportunities and challenges. In Secure Edge Computing, pages 3–20. CRC Press, 2021. [179] Sanjay P Ahuja and Nathan Wheeler. Architecture of fog-enabled and cloud enhanced internet of things applications. International Journal of Cloud Applications and Computing (IJCAC), 10(1):1–10, 2020. [180] Carlo Puliafito, Diogo M Gon¸calves, M´arcio M Lopes, Leonardo L Martins, Edmundo Madeira, Enzo Mingozzi, Omer Rana, and Luiz F Bittencourt. Mobfogsim: Simulation of mobility and migration for fog computing. Sim ulation Modelling Practice and Theory, 101:102062, 2020. [181] Haoxin Wang, Tingting Liu, BaekGyu Kim, Chung-Wei Lin, Shinichi Shi raishi, Jiang Xie, and Zhu Han. Architectural design alternatives based on cloud/edge/fog computing for connected vehicles. IEEE Communications Surveys & Tutorials, 22(4):2349–2377, 2020. [182] Zainab Javed and Waqas Mahmood. A survey based study on fog computing awareness. International Journal of Information Technology and Computer Science (IJITCS), 13(2):49–62, 2021. [183] Gon¸calo Carvalho, Bruno Cabral, Vasco Pereira, and Jorge Bernardino. Computation offloading in edge computing environments using artificial in telligence techniques. Engineering Applications of Artificial Intelligence, 95: 103840, 2020. [184] Fei Gu, Jianwei Niu, Zhiping Qi, and Mohammed Atiquzzaman. Parti tioning and offloading in smart mobile devices for mobile cloud computing: State of the art and future directions. Journal of Network and Computer Applications, 119:83–96, 2018. Bibliography 139 [185] Zhiyuan Li, Cheng Wang, and Rong Xu. Computation offloading to save energy on handheld devices: a partition scheme. In Proceedings of the 2001 international conference on Compilers, architecture, and synthesis for em bedded systems, pages 238–246, 2001. [186] Mingjin Gao, Rujing Shen, Long Shi, Wen Qi, Jun Li, and Yonghui Li. Task partitioning and offloading in dnn-task enabled mobile edge computing networks. IEEE Transactions on Mobile Computing, 2021. [187] Jianhui Liu and Qi Zhang. Adaptive task partitioning at local device or remote edge server for offloading in mec. In 2020 IEEE Wireless Communi cations and Networking Conference (WCNC), pages 1–6. IEEE, 2020. [188] Sachchidanand Singh. Optimize cloud computations using edge computing. In 2017 International Conference on Big Data, IoT and Data Science (BID), pages 49–53. IEEE, 2017. [189] Giorgos Mitsis, Pavlos Athanasios Apostolopoulos, Eirini Eleni Tsiropoulou, and Symeon Papavassiliou. Intelligent dynamic data offloading in a compet itive mobile edge computing market. Future Internet, 11(5):118, 2019. [190] Xin-She Yang. Nature-inspired optimization algorithms: challenges and open problems. Journal of Computational Science, page 101104, 2020. [191] Kinjal Chaudhari and Ankit Thakkar. Travelling salesman problem: An empirical comparison between aco, pso, abc, fa and ga. In Emerging Research in Computing, Information, Communication and Applications, pages 397– 405. Springer, 2019. [192] James Kennedy and Russell Eberhart. Particle swarm optimization. In Proceedings of ICNN’95-International Conference on Neural Networks, vol ume 4, pages 1942–1948. IEEE, 1995. [193] Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay, and Sankar Kumar Pal. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Information sciences, 177(22):5033–5049, 2007. [194] Wei Bin, Peng Qinke, Zhao Jing, and Chen Xiao. A binary particle swarm optimization algorithm inspired by multi-level organizational learning be havior. European Journal of Operational Research, 219(2):224–233, 2012. Bibliography 140 [195] Amira S Ashour and Yanhui Guo. Optimization-based neutrosophic set in computer-aided diagnosis. In Optimization Theory Based on Neutrosophic and Plithogenic Sets, pages 405–421. Elsevier, 2020. [196] Matheus Rosendo and Aurora Pozo. Applying a discrete particle swarm op timization algorithm to combinatorial problems. In 2010 Eleventh Brazilian Symposium on Neural Networks, pages 235–240. IEEE, 2010. [197] Indresh Kumar Gupta, Samiya Shakil, and Sadiya Shakil. A hybrid ga-pso algorithm to solve traveling salesman problem. In Computational Intelli gence: Theories, Applications and Future Directions-Volume I, pages 453– 462. Springer, 2019. [198] Ammar W Mohemmed, Nirod Chandra Sahoo, and Tan Kim Geok. Solv ing shortest path problem using particle swarm optimization. Applied Soft Computing, 8(4):1643–1653, 2008. [199] Han Huang and Zhifeng Hao. Particle swarm optimization algorithm for transportation problems. Particle swarm optimization, ed. Aleksandar Lazinica, InTech, pages 275–290, 2009. [200] Jean L. Pierobom, Myriam Regattieri Delgado, and Celso A. A. Kaestner. Particle swarm optimization applied to task assignment problem. Chem BioChem, pages 1–8, 2016. [201] Hina Rafique, Munam Ali Shah, Saif Ul Islam, Tahir Maqsood, Suleman Khan, and Carsten Maple. A novel bio-inspired hybrid algorithm (nbiha) for efficient resource management in fog computing. IEEE Access, 7:115760– 115773, 2019. [202] Arash Heidari, Mohammad Ali Jabraeil Jamali, Nima Jafari Navimipour, and Shahin Akbarpour. Deep q-learning technique for offloading offline/on line computation in blockchain-enabled green iot-edge scenarios. Applied Sciences, 12(16):8232, 2022. [203] Siyuan Sun, Junhua Zhou, Jiuxing Wen, Yifei Wei, and Xiaojun Wang. A dqn-based cache strategy for mobile edge networks. Computers, Materials & Continua, 71(2):3277–3291, 2022. Bibliography 141 [204] Umber Saleem, Yu Liu, Sobia Jangsher, and Yong Li. Performance guar anteed partial offloading for mobile edge computing. In 2018 IEEE Global Communications Conference (GLOBECOM), pages 1–6. IEEE, 2018. [205] Lan Li, Xiaoyong Zhang, Kaiyang Liu, Fu Jiang, and Jun Peng. An energy aware task offloading mechanism in multiuser mobile-edge cloud computing. Mobile Information Systems, 2018, 2018. [206] Pengtao Zhao, Hui Tian, Cheng Qin, and Gaofeng Nie. Energy-saving of floading by jointly allocating radio and computational resources for mobile edge computing. IEEE Access, 5:11255–11268, 2017. [207] Sudip Misra and Niloy Saha. Detour: Dynamic task offloading in software defined fog for iot applications. IEEE Journal on Selected Areas in Commu nications, 37(5):1159–1166, 2019. [208] Bowen Zhou, Amir Vahid Dastjerdi, Rodrigo N Calheiros, and Rajkumar Buyya. An online algorithm for task offloading in heterogeneous mobile clouds. ACM Transactions on Internet Technology (TOIT), 18(2):1–25, 2018. [209] Amit Kishor and Chinmay Chakarbarty. Task offloading in fog computing for using smart ant colony optimization. Wireless Personal Communications, pages 1–22, 2021. [210] Maryam Keshavarznejad, Mohammad Hossein Rezvani, and Sepideh Adabi. Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms. Cluster Computing, pages 1– 29, 2021. [211] Jin Wang, Jia Hu, Geyong Min, Albert Y Zomaya, and Nektarios Georgalas. Fast adaptive task offloading in edge computing based on meta reinforcement learning. IEEE Transactions on Parallel and Distributed Systems, 32(1): 242–253, 2020. [212] Liang Huang, Xu Feng, Cheng Zhang, Liping Qian, and Yuan Wu. Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing. Digital Communications and Net works, 5(1):10–17, 2019. Bibliography 142 [213] D Shobha Rani and M Pounambal. Deep learning based dynamic task of floading in mobile cloudlet environments. Evolutionary Intelligence, pages 1–9, 2019. [214] Jianhua Liu, Xin Wang, Shigen Shen, Guangxue Yue, Shui Yu, and Minglu Li. A bayesian q-learning game for dependable task offloading against ddos attacks in sensor edge cloud. IEEE Internet of Things Journal, 8(9):7546– 7561, 2020. [215] Jin Wang, Wenbing Wu, Zhuofan Liao, R Simon Sherratt, Gwang-Jun Kim, Osama Alfarraj, Ahmad Alzubi, and Amr Tolba. A probability preferred priori offloading mechanism in mobile edge computing. IEEE Access, 8: 39758–39767, 2020. [216] Ni Zhang, Songtao Guo, Yifan Dong, and Defang Liu. Joint task offloading and data caching in mobile edge computing networks. Computer Networks, 182:107446, 2020. [217] Xueying Zhang, Ruiting Zhou, Zhi Zhou, John CSCS Lui, and Zongpeng Li. An online learning-based task offloading framework for 5g small cell networks. In 49th International Conference on Parallel Processing-ICPP, pages 1–11, 2020. [218] Zhen Wang, Jihui Zhang, and Shengxiang Yang. An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with ran dom job arrivals. Swarm and Evolutionary Computation, 51:100594, 2019. [219] Mohd Azri Abdul Aziz, Mohd Nasir Taib, and Naimah Mohd Hussin. An improved event selection technique in a modified pso algorithm to solve class scheduling problems. In 2009 IEEE Symposium on Industrial Electronics & Applications, volume 1, pages 203–208. IEEE, 2009. [220] N Delgarm, B Sajadi, F Kowsary, and S Delgarm. Multi-objective optimiza tion of the building energy performance: A simulation-based approach by means of particle swarm optimization (pso). Applied energy, 170:293–303, 2016. [221] Li-biao Zhang, Chun-guang Zhou, Ming Ma, and Xiao-hua Liu. Solutions of multi-objective optimization problems based on particle swarm optimization. Journal of computer research and development, 7(41):7, 2004. Bibliography 143 [222] Hemlata S Urade and Rahila Patel. Dynamic particle swarm optimization to solve multi-objective optimization problem. Procedia Technology, 6:283–290, 2012. [223] Spiridoula V Margariti, Vassilios V Dimakopoulos, and Georgios Tsoumanis. Modeling and simulation tools for fog computing—a comprehensive survey from a cost perspective. Future Internet, 12(5):89, 2020	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2190
dc.description	Supervised by Prof. Dr. Muhammad Mahbub Alam, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Internet of Things (IoT) is the new prototype that is shaping Information and Communication Technologies (ICT) in a new direction. The rate of adaptability, ingenuity, and expansion is promising to bring connectivity to all things in homes, offices, vehicles, etc. This paradigm is causing researchers to find new ways to connect things efficiently, improve processing power of devices, create value for data collected from IoTs, and ensure the security of the data. Although numer ous data-intensive applications (self-parking cars, trackers, domestic appliances, smartphones, etc.) have been developed to employ the use of smart devices they are small in size, battery-powered, and limited in processing, storage, and memory supplies. Their size and nature of applications act as a bottleneck to implement powerful applications on them. Moreover, the amount of data generated by IoT de vices collectively surge the network. Therefore, it is necessary to utilize the Cloud of Things (CoT) infrastructure. A CoT provides extensive processing power and unlimited storage that permits fast processing, and bulk storage of data produced by the new cloud infrastructure. It works well in situations that are not delay sensitive, and require no immediate responsiveness. However, the use of CoTs exclusively is not effective in applications that require immediate processing, high responsiveness, and real-time analysis of client’s requests because of the distance between the CoTs and IoTs. To this purpose, fog computing has been designed. Fogging enables computational offloading, data aggregation, and storage. The offloading process enables smart objects to realize the full potential of the IoT Fog-cloud infrastructure by sending part of its computation routines to remote sites for processing. This permit saving computational resources such as proces sors, storage, memory, and energy. Nonetheless, the question of when, what, and how to offload has not been fully resolved. Moreover, new challenges keep emerg ing such as finding the better offloading point given mobility, heterogeneity in IoT devices, and dynamic communication environment. Further, dealing with offload ing that is constrained by location and latency-sensitive submissions remain hard to solve. This study is intended to solve an IoT-Fog infrastructure performance problem in which each of the IoT devices may contain computation-intensive, or security-sensitive, or delay-sensitive tasks to offload. If the IoT device finds that it cannot execute the tasks, an offload to an optimal Fog is initiated through a Smart gateway (SG). The Fog either performs the tasks or sends it to the cloud. The in tension of the study is to perform dynamic offloading while maintaining the user’s sensitive tasks in the Fog during offloading. The study is expected to achieve high performance in terms of throughput, delay, energy consumption, resource utiliza tion rate and response time. For this purpose, a computation offloading framework is proposed with the engagement of a four-tier cloud infrastructure using a pipeline of machine learning (ML) strategies. This pipeline consists of a set of ML models connected in series to facilitate the offloading efficiently. The study is intended to design a fast, efficient and robust algorithm that enables the selection of optimal fog and cloud when the need arises, while providing mobility when network con ditions fluctuate.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Machine learning based computa tional offloading in the Fog Cloud Internet of Things ecosystems	en_US
dc.type	Thesis	en_US