A Novel Trusted Intelligent Computing-aware Routing Service Offloading Method Based on MOPSO for Internet of Vehicles

Abstract

Due to the high-speed displacement of vehicles, various computing resources in the Internet of Vehicles have such characteristics as limited communication bandwidth, unstable network connections, and dynamic changes in network topology. Therefore, establishing a trusted service offloading location and supplying consumers with dependable and low-latency services in a resource-constrained mobile edge computing system is still a significant difficulty. This paper proposes a “device-edge-cloud” collaborative trusted edge computing-aware network model, and presents an intelligent computing-aware routing service offloading method based on multi-objective particle swarm optimization for this system model. First, a trustworthiness model for data transmission across distributed computing resources in the Internet of Vehicles environment is proposed. Then, the literature overview points out that the trustworthiness of computing resources is relative, dynamic, reflexive, symmetrical, and not transitive. Based on the trustworthiness model, a multi-dimensional QoS attribute model for computing resources is established, and the scheduling problem for computing resources is abstracted into a multi-objective optimization problem. Finally, an intelligent computing-aware routing scheduling method based on a multi-objective particle swarm optimization algorithm is proposed for solving the task scheduling problem in the Internet of Vehicles environment. Simulation results show that in comparison with the random scheduling algorithm and the greedy scheduling algorithm, the MOPSO scheduling algorithm is significantly better with regard to the reliability of calculation results and communication cost.