Reference : IoV-based Deployment and Scheduling of Charging Infrastructure in Intelligent Transpo...

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/44824

Title :	IoV-based Deployment and Scheduling of Charging Infrastructure in Intelligent Transportation Systems
Language :	English
Author, co-author :	Ejaz, Waleed []
	Naeem, Mohammed []
	Sharma, Shree Krishna [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Khattak, Asad K. []
	Ramzan, M. R. []
	Ali, Amjad []
	Anpalagan, Alagan []
Publication date :	Jun-2020
Journal title :	IEEE Sensors Journal
Publisher :	Institute of Electrical and Electronics Engineers
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	1530-437X
e-ISSN :	1558-1748
City :	New York
Country :	NY
Keywords :	[en] IoT ; IoV ; Electric Vehicles ; Charging Infrastructure ; Scheduling Optimization
Abstract :	[en] Internet of vehicles (IoV) is an emerging paradigm to exchange and analyze information collected from sensors using wireless technologies between vehicles and people, vehicles and infrastructure, and vehicles-to-vehicles. With the recent increase in the number of electric vehicles (EVs), the seamless integration of IoV in EVs and charging infrastructure can offer environmentally sustainable and budget-friendly transportation. In this paper, we propose an IoV-based framework that consists of deployment and scheduling of a mobile charging infrastructure. For the deployment, we formulate an optimization problem to minimize the total cost of mobile charging infrastructure placement while considering constraints on the number of EVs that can be charged simultaneously. The formulated problem is mixedinteger programming and solved by using the branch and bound algorithm. We then propose an IoV-based scheduling scheme for EVs charging to minimize travel distance and charging costs while satisfying the constraints of charging time requirement of EVs and resources of a charging station.We consider passive road sensors and traffic sensors in the proposed IoV-based scheduling scheme to enable EV users for finding a charging station that can fulfill their requirements, as well as to enable service providers to know about the demand in the area. Simulation results illustrate the significant impact of the optimal deployment of charging infrastructure and scheduling optimization on the efficiency of EVs charging.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/44824
DOI :	10.1109/JSEN.2020.3006706
Other URL :	https://ieeexplore-ieee-org.proxy.bnl.lu/document/9131780

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