Strategy-proof mechanism based on dwarf mongoose optimization for task offloading in vehicle computing

Abstract

Along with intelligent vehicle (IV) development, IVCs can be used as mobile computing platforms to provide users with various services. The aim of this paper is to design an efficient task offloading mechanism to maximize group efficiency in vehicle computing. Considering that sensing data inherently support multi-user sharing, we introduce a resource-sharing model in which multiple users share sensing resources. To provide a scalable service, we propose auction-based dynamic pricing. To achieve a balance between quality and efficiency, the efficient task offloading mechanism proposed in this study is based on dwarf mongoose optimization. The initialization algorithm generates random, best-fit, and greedy allocations based on probability. Convergence characteristics are improved using a new scouting algorithm and a new babysitter algorithm, both of which also contribute to maintaining population diversity. We demonstrate that the proposed mechanism achieves strategy-proofness, group strategy-proofness, individual rationality, budget balance, and consumer sovereignty. The novelty consists in our showing how to design the strategy-proof mechanism based on swarm optimization. Furthermore, the approximate ratio of the proposed mechanism is analyzed. Experimental verifications are conducted to show the proposed mechanism shows good performance in different environments.