Robust and Asynchronous Multi-Node Cooperative Vehicular Fog Computing Enhanced IoV

The Vehicular Fog Computing (VFC) provides low-latency computing service to support emerging intelligent transportation applications in Internet of Vehicles (IoV). Multi-node cooperative VFC can utilize Connected and Autonomous Vehicles (CAVs) to implement cooperative intelligence. Due to the mobility of vehicles, service migration is necessary when task offloading service providers change. This paper proposes an Asynchronous Task Offloading Scheme (ATO-S) that allows each CAV to choose an independent optimization period and provides robust task offloading services under unknown vehicle mobility probability distribution. To the best of our knowledge, this is the first work to investigate asynchronous and robust multi-node cooperative task offloading in dynamic VFC-enhanced IoV scenarios. Furthermore, we formulate the long-term energy consumption minimization problem of VFC and transfer it into each time slot problem by Lyapunov optimization. Then we design Asynchronous Task Offloading Algorithm (ATO-A) to jointly optimizing CAVs matching, communication and computation resource allocation, and transmission power based on multiple mathematical techniques and hybrid heuristic algorithm. Extensive simulations based on real-world traffic scenario are conducted by varying multiple crucial parameters. Simulation results demonstrate the energy efficiency and task queue stability achieved by ATO-A, and service robustness achieved by ATO-S, in comparison with benchmark solutions.