Mobile Edge Computing for Big Data-Enabled Electric Vehicle Charging

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

Research output: Contribution to journalArticle

104 Citations (Scopus)
191 Downloads (Pure)


As one of the key drivers of smart grid, Electric Vehicles (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 paper, we propose a Mobile Edge Computing (MEC) based system, inline with a big data-driven planning strategy on which Charging Station (CS) to charge. The Global Controller (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 benefits of MEC based system in terms of communication efficiency (with repeated monitoring the traffic jam), concerning the long term popularity of EVs.
Original languageEnglish
Pages (from-to)150-156
JournalIEEE Communications Magazine
Issue number3
Publication statusPublished - 15 Mar 2018


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