Collaborative Edge Computing and Caching in Vehicular Networks

Abstract

Mobile Edge Computing (MEC) can significantly promote the development of Internet of Vehicles (IoV) for providing a low-latency and high-reliability environment. Nevertheless, a huge amount of sensor data or computing requirements generated by massive vehicles in adjacent area may be duplicated. In order to realize the efficient diffusion of information, we propose a hierarchical end-edge framework with the aid of deep collaboration among data communication, computation offloading and content caching to minimize network overheads. Specially, duplicated perceived data and computation results are cached in advance to decrease repeated data uploading and duplicated computation in offloading process. In addition, the problem is formulated as a mixed integer non-linear programming (MINLP) problem, and the deep deterministic policy gradient (DDPG)-based resource allocation scheme is utilized to obtain a sub-optimal solution with low computation complexity. Performance evaluation demonstrates that the proposed scheme can significantly reduce network overheads compared with other benchmark methods.