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

Cao, Y, Song, H, Kaiwartya, O ORCID: https://orcid.org/0000-0001-9669-8244, Zhou, B, Zhuang, Y, Cao, Y and Zhang, X, 2018. Mobile edge computing for big-data-enabled electric vehicle charging. IEEE Communications Magazine, 56 (3), pp. 150-156. ISSN 0163-6804

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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:	Journal article
Publication Title:	IEEE Communications Magazine
Creators:	Cao, Y., Song, H., Kaiwartya, O., Zhou, B., Zhuang, Y., Cao, Y. and Zhang, X.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	15 March 2018
Volume:	56
Number:	3
ISSN:	0163-6804
Identifiers:	Number Type 10.1109/mcom.2018.1700210 DOI 17617417 Publisher Item Identifier
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	13 Aug 2018 15:57
Last Modified:	13 Aug 2018 15:57
URI:	https://irep.ntu.ac.uk/id/eprint/34327

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