This study proposes an efficient data collection strategy exploiting a team of Unmanned Aerial Vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors' data collection collaboratively while considering existing constraints of the UAV payload and battery capacity. The proposed mission planner system employs the Differential Evolution (DE) optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding the redundant collection of sensors' data. The proposed mission planner is evaluated through extensive simulation and comparative analysis. The simulation results confirm the effectiveness and fidelity of the proposed mission planner to be used for the distributed sensor network monitoring and data collection.

翻译：这项研究提出了高效的数据收集战略,利用无人驾驶航空飞行器小组监测和收集通常用于环境监测、气象学、农业和可再生能源应用的大型分布式传感器网络的数据,开发了一个合作性任务规划系统,使无人驾驶航空飞行器小组能够在考虑无人驾驶航空飞行器有效载荷和电池能力的现有制约因素的同时合作执行和完成传感器数据收集任务。拟议的飞行任务规划员系统使用差异进化优化算法,使无人驾驶飞行器能够根据传感器的优先程度,最大限度地增加已访问的传感器节点数量,避免重复收集传感器数据。拟议的飞行任务规划员通过广泛的模拟和比较分析进行评估。模拟结果证实了拟用于分布式传感器网络监测和数据收集的拟议飞行任务规划员的有效性和真实性。