Multi-Vehicle Intelligent Collaborative Computing Strategy for Internet of Vehicles

The computation-intensive applications pose unprecedented demands on the Internet of Vehicles (IoVs). How to address the delay constraint to execute the computation tasks effectively becomes a significant issue for this scenario. Compared with remote cloud, edge servers reduce the delay by being deployed close to vehicles. However, most edge servers are connected to fixed access points, which leads to the inflexible edge computing architecture. Considering the dynamics of vehicles' location and service request, it is a promising paradigm that multi-vehicle compute the task collaboratively by utilizing the vehicles' available computing resources. In this paper, by jointly considering the local execution, V2V offloading, and multi-vehicle collaboration, we determine the optimal task partition ratio after the cooperative vehicles are selected. Then, double deep Q-network (DDQN) is used to take the optimal dual actions. Finally, we develop a multi-vehicle intelligent collaborative computing strategy (MV-ICCS) to minimize the total system delay. Simulation results show the advantage of the proposed strategy and evaluate the system performance.