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Cao, Yue, Song, Houbing, Kaiwartya, Omprakash, Zhou, Bingpeng, Zhuang, Yuan, Cao, Yang and Zhang, Xu 
ORCID: https://orcid.org/0000-0001-6557-6607
(2018)
Mobile edge computing for big-data-enabled electric vehicle charging.
IEEE Communications Magazine, 56 (3).
pp. 150-156.
ISSN 0163-6804
Abstract
As one of the key drivers of smart grid, EVs are environment-friendly to alleviate CO2 pollution. Big data analytics could enable the move from Internet of EVs, to optimized EV charging in smart transportation. In this article, we propose a MECbased system, in line with a big data-driven planning strategy, for CS charging. The GC as cloud server further facilitates analytics of big data, from CSs (service providers) and on-the-move EVs (mobile clients), to predict the charging availability of CSs. Mobility-aware MEC servers interact with opportunistically encountered EVs to disseminate CSs' predicted charging availability, collect EVs' driving big data, and implement decentralized computing on data mining and aggregation. The case study shows the benefits of the MEC-based system in terms of communication efficiency (with repeated monitoring of a traffic jam) concerning the long-term popularity of EVs.
| Item Type: | Article |
|---|---|
| Additional Information: | Publisher Copyright: © 2018 IEEE. |
| Uncontrolled Keywords: | computer science applications,computer networks and communications,electrical and electronic engineering,sdg 7 - affordable and clean energy ,/dk/atira/pure/subjectarea/asjc/1700/1706 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 26 Jan 2024 02:15 |
| Last Modified: | 10 Dec 2024 01:43 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94266 |
| DOI: | 10.1109/MCOM.2018.1700210 |
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