基于能量效益的无人机辅助移动群智感知系统任务分配

UAV-assisted mobile crowd sensing system task assignment based on energy efficiency

与传统移动群智感知系统的感知工人相比,无人机具有能够执行危险任务、易控制、不需要额外的激励成本等优点,因此无人机更适合作为任务执行的主体。然而无人机的能量有限,且感知任务的完成时间有限制,因此如何综合考虑以上2个因素设计一种高效的任务分配方法是一个关键问题。由此提出一种基于能量效益的无人机辅助移动群智感知系统任务分配方法,在能量效益最大化的同时提高系统任务完成率。该方法首先在无人机获得的回报和消耗的能量基础上定义了能量效益计算方式,用于评价任务分配方案的优劣;然后,设计了一种改进的模拟退火遗传算法以获得能量效益最大化的任务分配方案。经实验证明,与其他基准算法相比,所提出方法在任务平均能耗、系统任务完成率、系统能量效益有更好的表现。

Compared with the traditional mobile crowd sensing system for sensing workers,UAVs have the advantages of being able to perform hazardous tasks,being easy to control,and not requiring additional incentive costs,making them more suitable as the subject of task execution.However,the energy of UAVs is limited and the completion time of sensing tasks is restricted,so how to design an efficient task assignment method considering the above two factors is a key issue.Thus,an energy-efficient UAVassisted mobile crowd sensing system task assignment method is proposed to improve the system task completion rate while maximizing energy efficiency.The method firstly defines an energy-efficient calculation based on the obtained returns and consumed energy by the UAV for evaluating the merits of the task assignment scheme;then,an improved simulated annealing genetic algorithm is designed to obtain an energy-efficient task assignment scheme that maximizes energy efficiency.It is experimentally demonstrated that the proposed method has better performance in average task energy consumption,system task completion rate,and system energy efficiency compared with other benchmark algorithms.