Mobile edge computing for big-data-enabled electric vehicle charging

Yue Cao, Houbing Song, Omprakash Kaiwartya, Bingpeng Zhou, Yuan Zhuang, Yang Cao, Xu Zhang

Research output: Contribution to journalArticlepeer-review

101 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)150-156
Number of pages7
JournalIEEE Communications Magazine
Issue number3
Publication statusPublished - Mar 2018

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