A trustworthy task offloading system for heterogeneous vehicle-edge-cloud collaboration scenarios

Abstract

Task offloading exhibits significant advantages in energy and delay by offloading tasks that are difficult for vehicles to handle to the edge or cloud servers. However, due to the environmental heterogeneity and interaction complexity, it is difficult to ensure the credibility of offloading tasks and nodes in vehicular network systems, leading to inefficiencies such as task accumulation, resource preemption, and poor collaboration. To this end, we propose a Trustworthy task Offloading system for heterogeneous Vehicle-Edge-Cloud collaboration scenarios in this paper, abbreviated as TOVEC. Specifically, we propose two key systems. First, we design a trust evaluation system for identifying fake tasks and malicious nodes, which can dynamically update trust of tasks, vehicles and MEC servers by analyzing task data sensitivity and node's historical completion quality, collaboration feedback, and current request frequency. Then, we propose a vehicle-edge-cloud collaborative offloading system based on the discrete particle swarm optimization for iteratively searching optimal offloading decision. It redesigns particle representation, fitness evaluation, particle update, and correction mechanisms, and introduces random and greedy ideas, mapping functions to enhance the global optimization capability. Finally, experiments conducted on the synthetic and real-world datasets prove that, TOVEC demonstrates superiority in identification accuracy, energy consumption, and delay in both compact and uniform scenarios. Compared with benchmark methods, it improves identification accuracy by 21.28%-29.24%, and reduces energy consumption at most 15%.