A task offloading strategy combined with trajectory prediction was proposed to minimize the offloading cost,and the task offloading was transformed into a server node selection problem.A time-series-based vehicle movement trajectory prediction model was constructed and presented as a nonlinear regression task.According to the vehicle location information and communication range,a dynamic edge cluster method was proposed based on the shortest communication distance.Server computing power and transmission cost were utilized to optimize the load distribution in the edge network and reduce system overhead caused by vehicle movement.The server selection problem in a multi-edge server coverage scenario was effectively addressed by designing a task off-loading strategy based on moving trajectory prediction and dynamic edge server clusters using Markov decision process.Experimental results showed that compared with other algorithms,the proposed algorithm could reduce the task offloading cost by 80%and 57.8%at least on simple and complex movement trajectory.The trajectory prediction error and cost of multi-edge server collaboration could be effectively reduced.
关键词
车联网/边缘计算/任务卸载/马尔可夫决策/轨迹预测
Key words
internet of vehicles/edge computing/task offloading/Markov decision/trajectory prediction
维普
万方数据