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Cao, Yue, Wu, Celimuge, Zhang, Xu 
ORCID: https://orcid.org/0000-0001-6557-6607, Liu, William, Peng, Linyu and Khalid, Muhammad
(2019)
MEGEE:Mobile Edge computer Geared v2x for E-mobility Ecosystem.
In:
2019 IEEE Wireless Communications and Networking Conference, WCNC 2019.
IEEE Wireless Communications and Networking Conference, WCNC
.
The Institute of Electrical and Electronics Engineers (IEEE), MAR.
ISBN 9781538676462
Abstract
The introduction of Electric Vehicles (EVs) leads to new concern on the E-Mobility. Making charging reservation, by considering the EV's arrival time and its expected charging time at Charging Stations (CSs) has been studied to predict the dynamic status of CSs. In this paper, we propose a Mobile Edge computer Geared v2x for E-mobility Ecosystem (MEGEE), as a decentralized alternative to the conventional centralized cloud based architecture. MEGEE enables the Vehicular Delay/Disruption Tolerant Networking (VDTN)-driven anycasting for information delivery, and Mobile Edge Computing (MEC) functioned CSs for information mining and aggregation. MEGEE efficiently and timely processes essential charging reservations and charging control information, through the Internet of EVs and MEC servers. Our studies show that MEGEE can achieve the close charging performance as performed by the centralized system, while offers a significant saving in communications cost.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Funding Information: Y.Cao is with the School of Computing and Communications, Lancaster University, UK. Email: yue.cao@lancaster.ac.uk; C.Wu is with the Department of Computer and Network Engineering, Japan.; X.Zhang is with the Department of Computer Science and Engineering, Xi’an University of Technology, China; W.Liu is with the School of Engineering, Computer&Mathematical Sciences, Auckland University of Technology, New Zealand; L.Peng is with the Waseda Institute for Advanced Study, Waseda University, Japan; M.Khalid is with the Department of Computer and Information Sciences, Northumbria University, UK. This work was supported in part by JSPS KAKENHI No 18KK0279; Horizon 2020 research and innovation programme (ICT) under grant agreement No 687794. Publisher Copyright: © 2019 IEEE. |
| Uncontrolled Keywords: | engineering(all) ,/dk/atira/pure/subjectarea/asjc/2200 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 26 Jan 2024 02:20 |
| Last Modified: | 10 Dec 2024 01:13 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94270 |
| DOI: | 10.1109/WCNC.2019.8885676 |
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