Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence

The use of unmanned aerial vehicles (UAVs) is a promising solution to efficiently acquire data in large-scale wireless sensor networks (WSNs). In a wide-area WSN environment, UAV path planning is one of the challenging issues in solving the optimization problem to achieve complex and multiple objectives under various constraints related to UAV operation. In this article, we propose a novel asynchronous UAV path planning mechanism for multiobjective UAV operation. In a 3-D sensor field, a grid-based sensor field and information gathering model is introduced. We define a UAV coverage area where line-of-sight communication is possible between a UAV and the sensors and propose a method to quickly find the grid cells within the coverage area. We define a multipurpose fitness function that maximizes the value of the acquired sensing information and at the same time minimizes the time and energy required for UAV operation. The value of the sensing information reflects the sensor density for each sensor type within UAV coverage, as well as changes in the sensing information values over time. In the proposed method, time and energy objective functions are learned by considering location-dependent communication link quality, sensor density, and the next UAV locations. The optimum UAV position and the movement schedule of each UAV are asynchronously derived using the proposed particle swarm optimization (PSO) algorithm with several UAV operational constraints. The experimental results demonstrate that the proposed method can maximize the utility of the objective function and achieve fast convergence in finding the optimal solution compared with other methods.