Mobility-Aware Task Parallel Offloading for Vehicle Fog Computing

When applying fog computing paradigm into Internet of vehicle, vehicles are regarded as intelligent devices with computation and communication capability. These moving intelligent devices are often employed to assist various computation-intensive task offloading in vehicle fog computing, which brings real-time responses. However, vehicles mobility and network dynamics make it challenging to offload tasks to ideal target nodes for user-vehicle. In this paper, leveraging the result of vehicle mobility-awareness, we investigate the task offloading problem in vehicle fog computing aiming to minimize service time. Specifically, we consider that a task can be decomposed into subtasks in any proportion and offloaded from user-vehicle to multi service vehicles in parallel via vehicle-to-vehicle (V2V) links. Mobility information of vehicles collected by RSU is modeled to predicted the states of V2V links based on hidden Markov model (HMM). Then, we refine a rule to select target service-vehicles and the size of each subtask according to predicted results. Comparing with random and single-point task offloading, the proposed approach indicates a better performance on amount of finished task and service time in vehicle dense area.