Reference : Emerging Edge Computing Technologies for Distributed IoT Systems
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/39347
Emerging Edge Computing Technologies for Distributed IoT Systems
English
Alnoman, Ali [Ryeson Univesity, Canada]
Sharma, Shree Krishna mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ejaz, Waleed [Thomson River University, Canada]
Anpalagan, Alagan [Ryerson University, Canada > Department of Electrical and Computer Engineering]
2019
IEEE Network
Institute of Electrical and Electronics Engineers
Yes (verified by ORBilu)
International
0890-8044
1558-156X
New York
NY
[en] IoT ; Machine Learning ; Edge Computing ; Reinforcement Learning
[en] The ever-increasing growth of connected smart devices and Internet of Things (IoT) verticals is leading to the crucial challenges of handling the massive amount of raw data generated by distributed IoT systems and providing timely feedback to the end-users. Although existing cloud computing paradigm has an enormous amount of virtual computing power and storage capacity, it might not be able to satisfy delaysensitive applications since computing tasks are usually processed at the distant cloud-servers. To this end, edge/fog computing has recently emerged as a new computing paradigm that helps to extend cloud functionalities to the network edge. Despite several benefits of edge computing including geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed for future IoT systems. In this regard, this paper provides a comprehensive view on the current issues encountered in distributed IoT systems and effective solutions by classifying them into three main categories, namely, radio and computing resource management, intelligent edge-IoT systems, and flexible infrastructure management. Furthermore, an optimization framework for edge-IoT systems is proposed by considering the key performance metrics including throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of ML in edge-IoT computing.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/39347
https://arxiv.org/pdf/1811.11268.pdf

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