PSO-Based Dynamic UAV Positioning Algorithm for Sensing Information Acquisition in Wireless Sensor Networks

In this paper, we propose a dynamic unmanned aerial vehicle (UAV) positioning method to maximize the value of the sensor data information acquired from multiple UAVs in wireless sensor networks. In operations of UAVs to monitor environmental disasters or perform military missions, the value of the acquired sensing information depends on the sensor types and the elapsed time after the previous sensing time. To support real-time sensor data monitoring, the sensed data should be successfully delivered to the ground base station (or sink node) using a UAV flying ad-hoc network; herein, it is important to maintain the connectivity between the UAVs and to consider the reliability of the communication links. In this paper, particle swarm optimization (PSO) is used to derive optimum UAV locations. For a specified wireless sensor network, at each time instance, new UAV locations are updated that guarantee complete connectivity of the UAVs and maximize the value of the aggregated sensor data information. In this paper, we have formulated the UAV location search model as a constrained optimization problem with multi-objective utility functions using PSO-based bio-inspired algorithm. The simulation results demonstrate that by maximizing the intended multi-objective utility function, the proposed method can dynamically derive the optimal locations of multiple UAVs and achieve better sensing information acquisition compared to other methods.