Connected Vehicles Computation Task Offloading Based on Opportunism in燙ooperative Edge Computing

The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud. Meanwhile, offloading decision and resource allocation of the user CVs must be ascertained. Furthermore, the joint offloading decision and resource allocation problem is described as a Mixed Integer Nonlinear Programming (MINLP) problem, which optimizes the task response latency of user CVs under various constraints. The original problem is decomposed into two subproblems. First, the Lagrange dual method is used to acquire the best resource allocation with the fixed offloading decision. Then, the satisfaction-driven method based on trial and error (TE) learning is adopted to optimize the offloading decision. Finally, a comprehensive series of experiments are conducted to demonstrate that our suggested scheme is more effective than other comparison schemes.