TY - GEN
T1 - MEGEE
T2 - 2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
AU - Cao, Yue
AU - Wu, Celimuge
AU - Zhang, Xu
AU - Liu, William
AU - Peng, Linyu
AU - Khalid, Muhammad
N1 - 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.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85074770784&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074770784&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2019.8885676
DO - 10.1109/WCNC.2019.8885676
M3 - Conference contribution
AN - SCOPUS:85074770784
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 April 2019 through 19 April 2019
ER -