Mobile Edge Computing for Big Data-Enabled Electric Vehicle Charging

Cao, Yue, Hong, Houbing, Kaiwartya, Omprakash, Zhou, Bingpeng, Zhuang, Yuan, Cao, Yang and Zhang, Xu (2018) Mobile Edge Computing for Big Data-Enabled Electric Vehicle Charging. IEEE Communications Magazine, 56 (3). pp. 150-156. ISSN 0163-6804

Preview

Text Enabling Mobile Edge Computing for Data-Driven Electric Vehicle Charging.pdf - Accepted Version Download (901kB) | Preview

Abstract

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.

Actions (login required)

View Item

Downloads