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Selective Offloading in Mobile Edge Computing for the Green Internet of Things
Beijing University of Posts and Telecommunications, Beijing, China.
Beijing University of Posts and Telecommunications, Beijing, China.
Guangdong University of Technology, Guangzhou, China.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).ORCID iD: 0000-0003-4894-4134
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2018 (English)In: IEEE Network, ISSN 0890-8044, E-ISSN 1558-156X, Vol. 32, no 1, p. 54-60Article in journal (Refereed) Published
Abstract [en]

Mobile edge computing provides the radio access networks with cloud computing capabilities to fulfill the requirements of the Internet of Things services such as high reliability and low latency. Offloading services to edge servers can alleviate the storage and computing limitations and prolong the lifetimes of the IoT devices. However, offloading in MEC faces scalability problems due to the massive number of IoT devices. In this article, we present a new integration architecture of the cloud, MEC, and IoT, and propose a lightweight request and admission framework to resolve the scalability problem. Without coordination among devices, the proposed framework can be operated at the IoT devices and computing servers separately, by encapsulating latency requirements in offloading requests. Then a selective offloading scheme is designed to minimize the energy consumption of devices, where the signaling overhead can be further reduced by enabling the devices to be self-nominated or self-denied for offloading. Simulation results show that our proposed selective offloading scheme can satisfy the latency requirements of different services and reduce the energy consumption of IoT devices. © 2018 IEEE 

Place, publisher, year, edition, pages
Piscataway: IEEE, 2018. Vol. 32, no 1, p. 54-60
National Category
Communication Systems Telecommunications
Identifiers
URN: urn:nbn:se:hh:diva-36194DOI: 10.1109/MNET.2018.1700101OAI: oai:DiVA.org:hh-36194DiVA, id: diva2:1178847
Note

This work was supported in part by the National Natural Science Foundation of China under Grant 61471060 and Grant 61421061, and in part by the National Key Research and Development Program of China under Grant 2017ZX03001003.

Available from: 2018-01-30 Created: 2018-01-30 Last updated: 2018-01-31Bibliographically approved

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Vinel, Alexey

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CiteExportLink to record
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  • apa
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