多无人机协同直播场景下自适应任务卸载决策

Adaptive task offloading decision of multi-UAVs cooperation in live broadcasting scenario

针对多无人机协同执行任务过程中计算量大和能耗高的问题,基于计算卸载原理以及博弈理论,提出一种多无人机自适应任务卸载方案.在方案中首先对系统进行建模,构造出多节点相互制约的移动卸载模型;其次,根据卸载模型分别构建无人机执行任务时的时延与能耗计算方法,通过综合考虑延时和能耗两方面因素,生成系统全局代价函数;然后,设计出基于博弈理论和纳什均衡的自适应任务卸载算法,通过卸载算法与权重的分配实现最优计算节点的选取,实现整个直播系统的代价最小,从而平衡无人机计算时延与能量消耗;最后,与现有卸载模型相比,所提出的方案在任务执行过程中具有较强的移动性,能耗更低且时效性更高.仿真结果验证了所提出理论的有效性,具有现实意义.

In order to address the problem of large computation and high energy consumption in the process of multi-UAV collaborative missions, a multi-UAV adaptive task offloading scheme is proposed based on the computational offloading principle and game theory. The first step in the scheme is to model the system and construct a multi-node, mutually constrained mobile offloading model. Then, based on the unloading model, the method is constructed to calculate the time delay and energy consumption of the UAV when it performs its tasks respectively. The global cost function of the system is generated by considering both delay and energy consumption. An adaptive task offloading algorithm is designed based on the game theory and Nash equilibrium. The selection of the optimal computation node is achieved for minimizing the cost of the entire live system, thus balancing the UAV computation delay and energy consumption. Finally, compared with the existing models of offoading, task execution process of the proposed method has strong mobility, lower energy consumption and higher efficiency. The simulation results verify the effectiveness of the theory, which has a practical significance.