A trajectory prediction-based edge offloading strategy for internet of vehicles
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.
internet of vehiclesedge computingtask offloadingMarkov decisiontrajectory prediction
万方数据
维普
