LAUSR

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.