Resource Optimization of MAB-based Reputation Management for Data Trading in Vehicular Edge Computing

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Author(s)Xiao, Huizi
Author(s)Cai, Lin
Author(s)Feng, Jie
Author(s)Pei, Qingqi
Author(s)Shi, Weisong
Date Accessioned2023-03-24T19:02:33Z
Date Available2023-03-24T19:02:33Z
Publication Date2023-01-09
Description© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This article was originally published in IEEE Transactions on Wireless Communications. The version of record is available at: https://doi.org/10.1109/TWC.2022.3233035
AbstractVehicles are hesitant to upload data to edge servers in vehicle edge computing (VEC) as many vehicle data collected and perceived by various on-board sensors contain sensitive and personal information and lack economic incentive. Instead of free access to shared data, encrypted data trading will alleviate security and privacy concerns and provide an incentive for vehicle owners to share their data. The edge server needs to pay the price in data trading, and reputation management is a great method to help it trade with reliable and available vehicles. In this paper, we propose a multi-armed bandit (MAB)-based reputation management scheme, so the edge servers can select the high reputation vehicles for data trading, which can ensure the credibility and reliability of the data. The encryption scheme is applied to achieve the required transmission security level and defend the rights and interests of the edge server. On the other hand, implementing security measures will consume the computation and communication resources of the vehicles. We formulate an optimization problem that maximizes the revenue of vehicles in data trading under the constraints of time delay, energy consumption, and security level. Simulation results demonstrate that the proposed scheme is effective and efficient for vehicle reputation management, data trading selection, and resource allocation.
SponsorThis work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1807500, in part by the National Natural Science Foundation of China under Grants 62102297, 62001357, 62202005, and 62132013, in part by the Key Research and Development Program of Shaanxi under Grant 2022GY-437, in part by the Guangdong Basic and Applied Basic Research Foundation under Grants 2020A1515110496 and 2020A1515110079, in part by the China Postdoctoral Science Foundation under Grant 2021M692501, in part by the Open Project of Shaanxi Key Laboratory of Information Communication Network and Security under Grant ICNS202005, and in part by the China Scholarship Council.
CitationH. Xiao, L. Cai, J. Feng, Q. Pei and W. Shi, "Resource Optimization of MAB-based Reputation Management for Data Trading in Vehicular Edge Computing," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2022.3233035.
ISSN1558-2248
URLhttps://udspace.udel.edu/handle/19716/32571
Languageen_US
PublisherIEEE Transactions on Wireless Communications
KeywordsData trading
Keywordsmulti-armed bandit algorithm
Keywordsreputation management
Keywordsresource optimization
Keywordsvehicular edge computing
TitleResource Optimization of MAB-based Reputation Management for Data Trading in Vehicular Edge Computing
TypeArticle
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