Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16575
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dc.contributor.authorThingom, Chintureena-
dc.contributor.authorSen, Arijeet Chandra-
dc.contributor.authorJain, Amit-
dc.contributor.authorDutt, Krishan-
dc.date.accessioned2024-08-29T05:42:11Z-
dc.date.available2024-08-29T05:42:11Z-
dc.date.issued2024-
dc.identifier.citationChapter 9; pp. 164-182en_US
dc.identifier.isbn9781040016480-
dc.identifier.isbn9781032530185-
dc.identifier.urihttps://doi.org/10.1201/9781003470281-9-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16575-
dc.description.abstractElectric vehicles offer a promising solution for greener transportation due to their minimal greenhouse gas emissions. Nevertheless, the challenges of extended charging times and charging congestion continue to impede the seamless experience of electric vehicle owners in urban settings. Optimizing various aspects of electric vehicle charging, including charging services, station selection, scheduling, and security against potential network attacks, is imperative to address these issues. The incorporation of a smart grid further elevates the potential for optimization. In the context of future 5G-based Internet of Things (IoT) applications for traffic management, we introduce the charging priority (CP) preemptive charging scheduling strategy. This approach, founded on queuing theory, prioritizes the charging of electric vehicles with the highest charging priority, determined by factors such as charging demand and remaining parking time, to maximize overall charging efficiency. Building upon the CP charging scheduling strategy, we propose a reservation-based charging station selection scheme that accounts for reservation information. It selects the charging station with the shortest charging journey time, considering a single charging session, thus minimizing overall travel time for electric vehicles. To implement this scheme effectively, electric vehicles must upload their charging reservation information, enabling precise prediction of the service congestion status at charging stations and efficient allocation of charging resources. The proposed optimized charging priority (CP) charge scheduling strategy and reservation-based charging station selection scheme are rigorously validated through urban traffic data simulations. Results demonstrate their efficacy in significantly reducing the average charging journey time for electric vehicles and ensuring comprehensive charging services for a larger number of electric vehicles within the constraints of limited parking time, all the while considering the potential challenges posed by network attacks. © 2024 selection and editorial matter, Sagar Dhanraj Pande and Aditya Khamparia; individual chapters, the contributors.en_US
dc.language.isoenen_US
dc.publisherNetworks Attack Detection on 5G Networks using Data Mining Techniquesen_US
dc.publisherCRC Pressen_US
dc.subjectElectric Vehicle Chargingen_US
dc.subjectUrban Areasen_US
dc.subject5G Network Integrationen_US
dc.subjectNetwork Attack Mitigationen_US
dc.titleEnhancing Electric Vehicle Charging Efficiency In Urban Areas with 5G Network Integration and Network Attack Mitigationen_US
dc.typeBook Chapteren_US
Appears in Collections:Book/ Book Chapters

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