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Mobile Edge Computing Assisted Green Scheduling of On-Move Electric Vehicles

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Mehrabi, Abbas
dc.contributor.author Siekkinen, Matti
dc.contributor.author Yla-Jaaski, Antti
dc.contributor.author Aggarwal, Geetika
dc.date.accessioned 2021-09-02T08:46:41Z
dc.date.available 2021-09-02T08:46:41Z
dc.date.issued 2021
dc.identifier.citation Mehrabi , A , Siekkinen , M , Yla-Jaaski , A & Aggarwal , G 2021 , ' Mobile Edge Computing Assisted Green Scheduling of On-Move Electric Vehicles ' , IEEE Systems Journal . https://doi.org/10.1109/JSYST.2021.3084746 en
dc.identifier.issn 1932-8184
dc.identifier.issn 1937-9234
dc.identifier.other PURE UUID: a4585d17-42b3-4591-8c41-28acbcea5c21
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/a4585d17-42b3-4591-8c41-28acbcea5c21
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85112595308&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/67025805/Mobile_Edge_Computing_Assisted_Green_Scheduling_of_On_Move_Electric_Vehicles.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/109605
dc.description Publisher Copyright: CCBY
dc.description.abstract Mobile edge computing (MEC) has been proposed as a promising solution, which enables the content processing at the edges of the network helping to significantly improve the quality of experience (QoE) of end users. In this article, we aim to utilize the MEC facilities integrated with time-varying renewable energy resources for charging/discharging scheduling known as green scheduling of on-move electric vehicles (EVs) in a geographical wide area comprising of multiple charging stations (CSs). In the proposed system, the charging/discharging demands and the contextual information of EVs are first transmitted to nearby edge servers. With instantaneous electricity load/pricing and the availability of renewable energy at nearby CSs collected by aggregators, a weighted social-welfare maximization problem is then solved at the edges using greedy-based algorithms to choose the best CS for the EV's service. From the system point of view, our results reveal that compared to cloud-based scheme, the proposed MEC-assisted EVs scheduling system significantly improves the complexity burden, boosts the satisfaction (QoE) of EVs' drivers by localizing the traffic at nearby CSs, and further helps to efficiently utilize the renewable energy across CSs. Furthermore, our greedy-based algorithm, which utilizes the internal updating heuristics, outperforms some baseline solutions in terms of social welfare and power grid ancillary services. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries IEEE Systems Journal en
dc.rights openAccess en
dc.title Mobile Edge Computing Assisted Green Scheduling of On-Move Electric Vehicles en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Helsinki Institute for Information Technology (HIIT)
dc.contributor.department Nottingham Trent University
dc.subject.keyword Ancillary services
dc.subject.keyword Base stations
dc.subject.keyword Batteries
dc.subject.keyword Cascading style sheets
dc.subject.keyword electric vehicles (EVs)
dc.subject.keyword greedy-based algorithms
dc.subject.keyword Job shop scheduling
dc.subject.keyword mixed integer nonlinear programming (MINLP)
dc.subject.keyword mobile edge computing (MEC)
dc.subject.keyword Optimization
dc.subject.keyword renewable energy
dc.subject.keyword Renewable energy sources
dc.subject.keyword Servers
dc.identifier.urn URN:NBN:fi:aalto-202109028837
dc.identifier.doi 10.1109/JSYST.2021.3084746
dc.type.version publishedVersion

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