Abstract-

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Resource-intensive applications on smart vehicles is posing difficulties to the use of traditional cloud computing for computation offloading in vehicular networks. In particular, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. As an integration of mobile edge computing and vehicular networks, vehicular edge computing is a promising paradigm that aims to improve vehicular services by performing computation offloading in close proximity to vehicles. In this paper, the task offloading algorithm that efficiently optimizes task delay and computing resource consumption in multiuser , multi-server vehicular edge computing scenarios is studied. The offloading algorithm not only determines where the tasks are performed, but also indicates the execution order of the tasks on the server. In order to reduce the time complexity, this paper proposes a hybrid intelligent optimization algorithm based on partheno genetic algorithm and heuristic rules. Extensive simulations are conducted, and the results show that compared with the baseline algorithms, the proposed algorithm effectively improves the offloading utility of the VEC system and is suitable for task offloading in various situations. INDEX TERMS Computation offloading, Internet of Things, mobile edge computing, task scheduling, vehicular networks.

No abstract

The emergence of computation-intensive vehicle applications poses a significant challenge to the limited computation capacity of on-board equipments. Mobile edge computing has been recognized as a promising paradigm to provide highperformance vehicle services by offloading the applications to edge servers. However, it is still a challenge to efficiently utilize the available resources of vehicle nodes. In this article, we introduce mobile edge computing technology to vehicular ad hoc network to build a vehicular edge computing system, which provides a wide range of reliable services by utilizing the computing resources of vehicles on the road. Then, we study the computation offloading decision problem in this system and propose a novel multi-objective vehicular edge computing task scheduling algorithm which jointly optimizes the allocation of communication and computing resources. Extensive performance evaluation demonstrates that the proposed algorithm can effectively shorten the task execution time and has high reliability.