基于移动边缘计算的无人机资源分配及轨迹优化

Resource Allocation and Trajectory Optimization of the UAV Based on Mobile Edge Computing

传感器网络中传感器设备计算能力有限,且不方便收集处理其存储的信息。针对这个问题,提出一种传感器网络中无人机支持下的移动边缘计算系统,且对无人机设计一种动态任务缓存模型。提出一种基于天牛群算法的块坐标下降法来提高无人机的能耗效率,并引入Cubic映射和Levy飞行改进天牛群算法得到最优的无人机轨迹。实验结果表明,与其他能耗控制方法相比,该方法提高了无人机的能耗效率,进而提高整个移动边缘计算系统的收益。

Sensors in wireless sensor network have limited computing capability,and it is not convenient to harvest the stored information for processing.To address this problem,an UAV assisted mobile edge computing system is proposed in wireless sensor network,and a dynamic task caching model based on the UAV is presented.In this paper,the block coordinate descent algorithm on the strength of the beetle swarm optimization is used to maximize the energy efficiency of the UAV.And the optimal UAV trajectory is derived by the beetle swarm optimization based on Cubic mapping and Lėvy flight mutation mechanism.Numerical results indicate that the proposed algorithm improve the energy efficiency of the UAV compared with other solutions,and then increase the revenue of the total mobile edge computing system.