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

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

doi:10.1109/MCOM.2018.1700210

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 journal › Article › peer-review

137 Citations (Scopus)

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.

Original language	English
Pages (from-to)	150-156
Number of pages	7
Journal	IEEE Communications Magazine
Volume	56
Issue number	3
DOIs	https://doi.org/10.1109/MCOM.2018.1700210
Publication status	Published - Mar 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.1109/MCOM.2018.1700210

https://researchportal.northumbria.ac.uk/en/publications/mobile-edge-computing-for-big-data-enabled-electric-vehicle-chargLicence: Unspecified

Cite this

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title = "Mobile edge computing for big-data-enabled electric vehicle charging",

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.",

author = "Yue Cao and Houbing Song and Omprakash Kaiwartya and Bingpeng Zhou and Yuan Zhuang and Yang Cao and Xu Zhang",

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AU - Cao, Yue

AU - Song, Houbing

AU - Kaiwartya, Omprakash

AU - Zhou, Bingpeng

AU - Zhuang, Yuan

AU - Cao, Yang

AU - Zhang, Xu

PY - 2018/3

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N2 - 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.

AB - 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.

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

Abstract

UN SDGs

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