MEGEE: Mobile Edge computer Geared v2x for E-mobility Ecosystem

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.

[1]  Zhili Sun,et al.  Routing in Delay/Disruption Tolerant Networks: A Taxonomy, Survey and Challenges , 2013, IEEE Communications Surveys & Tutorials.

[2]  Joan Triay,et al.  From Delay-Tolerant Networks to Vehicular Delay-Tolerant Networks , 2012, IEEE Communications Surveys & Tutorials.

[3]  Arobinda Gupta,et al.  A Review of Charge Scheduling of Electric Vehicles in Smart Grid , 2015, IEEE Systems Journal.

[4]  Chin-Teng Lin,et al.  Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects , 2016, IEEE Access.

[5]  Sarvapali D. Ramchurn,et al.  Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.

[6]  P. Castoldi,et al.  An advanced smart management system for electric vehicle recharge , 2012, 2012 IEEE International Electric Vehicle Conference.

[7]  Yue Cao,et al.  Toward Efficient Electric-Vehicle Charging Using VANET-Based Information Dissemination , 2017, IEEE Transactions on Vehicular Technology.

[8]  Haitham S. Cruickshank,et al.  A Reliable and Efficient Geographic Routing Scheme for Delay/Disruption Tolerant Networks , 2013, IEEE Wireless Communications Letters.

[9]  Jörg Ott,et al.  The ONE simulator for DTN protocol evaluation , 2009, SIMUTools 2009.

[10]  Gaoxi Xiao,et al.  Power demand and supply management in microgrids with uncertainties of renewable energies , 2014 .

[11]  Michael Till Beck,et al.  Mobile Edge Computing: A Taxonomy , 2014 .

[12]  Yi Pan,et al.  Stochastic Load Balancing for Virtual Resource Management in Datacenters , 2020, IEEE Transactions on Cloud Computing.