Privacy-Preserving Data Scheduling in Incentive-Driven Vehicular Network

Abstract

The lightweight privacy-preserving algorithm in the vehicular networks (VNs) improves the reliability of data transmission for the vehicles. However, it is challenging for vehicles to execute resource-consuming algorithms while driving. In addition, the high-speed mobility of vehicles also brings data scheduling problems for vehicles and other equipment. To tackle the problems mentioned above, this article proposes a privacy-preserving data scheduling in an incentive-driven VN, which achieves efficient and secure data transmission based on an incentive mechanism among vehicles. The algorithm first balances the benefits between the source, forwarding, and destination nodes through a multidimensional incentive mechanism to ensure positive benefits. After the vehicles participate in the task under the action of the incentive mechanism, the key task will then be identified and completed. Finally, the data interference mechanism guarantees the security of data transmission between the vehicle and the edge server. The simulation experiment results show that the proposed algorithm is superior to other algorithms in revenue, satisfaction, and reliability.