{"title":"Joint timeliness and security provisioning for enhancement of dependability in Internet of Vehicle system","authors":"Tao Jing, Hengyu Yu, Xiaoxuan Wang, Qinghe Gao","doi":"10.1177/15501329221105202","DOIUrl":null,"url":null,"abstract":"The Internet of Things has emerged as a wonder-solution to numerous problems in our everyday lives, such as smart homes and intelligent transportation. As an extension of the IoTs, the Internet of Vehicles (IoVs) also requires increasingly high security and timeliness. This article proposes a vehicle-assisted batch verification (VABV) system for IoV, in which some vehicles called auxiliary authentication terminal (AAT) are selected to assist the roadside unit for Basic Safety Message (BSM) verification. As a measure to enhance the timeliness performance for system dependability, comprehensive AAT selection strategies are designed. To overcome the security weaknesses of VABV system, a Sybil detection scheme based on Extreme Learning Machine is developed. For the evaluation of VABV system, the quantified Age of Information (AoI) is used as an integrated timeliness and security indicator. The proposed AoI indicator synthesizes the effects of BSM verification, re-verification for failure of some AATs, Sybil attack, and Sybil detection scheme. As illustrated by the simulation results, by employing AoI as a performance evaluation indicator, we can better and more intuitively design an AAT optimal selection strategy based on changes in AoI. Simultaneously, the performance of the proposed Sybil detection scheme can be evaluated more intuitively and effectively under different IoV scenarios based on AoI.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Distributed Sensor Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/15501329221105202","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 2
Abstract
The Internet of Things has emerged as a wonder-solution to numerous problems in our everyday lives, such as smart homes and intelligent transportation. As an extension of the IoTs, the Internet of Vehicles (IoVs) also requires increasingly high security and timeliness. This article proposes a vehicle-assisted batch verification (VABV) system for IoV, in which some vehicles called auxiliary authentication terminal (AAT) are selected to assist the roadside unit for Basic Safety Message (BSM) verification. As a measure to enhance the timeliness performance for system dependability, comprehensive AAT selection strategies are designed. To overcome the security weaknesses of VABV system, a Sybil detection scheme based on Extreme Learning Machine is developed. For the evaluation of VABV system, the quantified Age of Information (AoI) is used as an integrated timeliness and security indicator. The proposed AoI indicator synthesizes the effects of BSM verification, re-verification for failure of some AATs, Sybil attack, and Sybil detection scheme. As illustrated by the simulation results, by employing AoI as a performance evaluation indicator, we can better and more intuitively design an AAT optimal selection strategy based on changes in AoI. Simultaneously, the performance of the proposed Sybil detection scheme can be evaluated more intuitively and effectively under different IoV scenarios based on AoI.
期刊介绍:
International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.