Hierarchical Wireless Vehicular Fog Architecture: A Case Study of Scheduling Electric Vehicle Energy Demands

Chekired, Djabir Abdeldjalil; Togou, Mohammed Amine; Khoukhi, Lyes

doi:10.1109/mvt.2018.2866268

Cited by 21 publications

(23 citation statements)

References 14 publications

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“…However, their model suffers from proposing charging/discharging mechanism and the subsequent profits analysis. Chekired et al [27] Fig. 1.…”

Section: Information and Communication Technology (Ict)-based Schedulingmentioning

confidence: 97%

“…Advances in ICT has introduced innovative approaches for decentralized EVs charging/discharging solutions. Charging scheduling of EVs using MEC has been recently introduced [1], [2], [10], [26], [27] in which the roadside infrastructures, such as mobile buses or drones, act as MEC servers between the cloud and on-move EVs.…”

Section: Information and Communication Technology (Ict)-based Schedulingmentioning

confidence: 99%

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

Mehrabi

Siekkinen

Ylä-Jääski

et al. 2022

IEEE Systems Journal

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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 greedybased algorithm, which utilizes the internal updating heuristics, outperforms some baseline solutions in terms of social welfare and power grid ancillary services. Index Terms-Ancillary services, electric vehicles (EVs), greedy-based algorithms, mixed integer nonlinear programming (MINLP), mobile edge computing (MEC), renewable energy. Abbas Mehrabi (Member, IEEE) received the B.Sc. degree in computer engineering from Azad University, Tehran, Iran in 2008, the M.Sc. degree in computer engineering from the Shahid Bahonar University of Kerman, Kerman, Iran in 2010, and the Ph.D. degree from the School of Electrical Engineering and Computer Science,

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“…However, their model suffers from proposing charging/discharging mechanism and the subsequent profits analysis. Chekired et al [27] Fig. 1.…”

Section: Information and Communication Technology (Ict)-based Schedulingmentioning

confidence: 97%

Section: Information and Communication Technology (Ict)-based Schedulingmentioning

confidence: 99%

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

Mehrabi

Siekkinen

Ylä-Jääski

et al. 2022

IEEE Systems Journal

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“…• Computing: The complex structure of the EVN requires a hierarchical computing architecture to ensure that data stored/analyzed at different layers can meet computing demand correspondingly [49]. The management of the computing architecture in terms of data collection, storage, and computing requires a flexible and automated operation mechanism [46].…”

Section: Sdn Controlmentioning

confidence: 99%

“…The management of computing infrastructure becomes more flexible and efficient supported by SDN. [49] proposes a hierarchical SDN management framework to handle specific tasks and issues on different tiers. Another approach is to implement computing tasks through virtualization technology to partition the computing resource into various slides for different services [50].…”

Section: Sdn Controlmentioning

confidence: 99%

“…Google Cloud SQL provides a platform to perform energy scheduling and communication service as software as a service(SaaS) [128]; Amazon elastic compute cloud can provide data storage by virtual machine for the grid operator as infrastructure as a service (IaaS) [129]. The grid operator can also utilize servers such as CloudFusion Delay-sensitive tasks (e.g., real-time EV charging/discharging scheduling [30], [42], [49]) and tasks offloaded from EVs (e.g., charging navigation)…”

Section: F Computation Infrastructurementioning

confidence: 99%

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Energy and Information Management of Electric Vehicular Network: A Survey

Chen

Wang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

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The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

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Energy‐efficient mechanisms for sustainable operations in vehicular fog networks: Challenges, state‐of‐the‐art, and future directions

Jiang

2024

Int J Communication

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SummaryVehicular fog networks (VFNs) improve vehicular communications by incorporating fog computing into the existing vehicular infrastructure. The integration of fog computing and vehicular communication results in the development of a diverse range of novel applications and services, including intelligent transportation systems and real‐time data analytics. This paradigm has evolved as an innovative strategy that deals with the load on cloudlet nodes, improves service response time during high‐demand periods, and saves energy in battery‐powered devices. VFN is realized by offloading user workloads to vehicular devices, taking advantage of the underutilized computational power of nearby vehicles. While VFN holds great promise, its widespread adoption faces many challenges, including ineffective energy‐latency tradeoff mechanisms and optimal resource allocation strategies. This study provides a thorough analysis that explores the challenges, examines current approaches, and suggests ways to improve the tradeoffs between energy and latency, thereby optimizing network resources. The study offers constructive recommendations for enhancing the development and establishing more robust VFNs.

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Hierarchical Wireless Vehicular Fog Architecture: A Case Study of Scheduling Electric Vehicle Energy Demands

Cited by 21 publications

References 14 publications

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

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

Energy and Information Management of Electric Vehicular Network: A Survey

Energy‐efficient mechanisms for sustainable operations in vehicular fog networks: Challenges, state‐of‐the‐art, and future directions

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