In wireless sensor networks, unmanned aerial vehicles play a critical role in enhancing network throughput and extending network lifetime, as sensor nodes are constrained by limited energy resources. In this paper, we aim to minimize the UAV’s trajectory in a data collection mission. First, an efficient clustering algorithm is proposed to partition sensors into clusters within a radius \(\varvec{R}\) , minimizing the number of hovering locations (HLs). Then, the UAV’s trajectory is optimized using both exact and heuristic approaches to solve the Traveling Salesman Problem (TSP). The Ant Colony Optimization (ACO) algorithm is employed to plan the UAV’s path, achieving near-optimal performance with a total flight distance only 3.42% longer than that obtained by the exact optimization method. Finally, a refinement algorithm is introduced to adjust the initial HLs and obtain an improved set of final hovering locations (HLs-F), further reducing the UAV’s flight distance. Numerical results show that the proposed clustering method generates a smaller number of HLs compared to the adaptive \(\varvec{K}\) -means, while the refinement algorithm applied to obtain the final HLs-F leads to significant reductions in trajectory length, thereby reducing the overall energy consumption.
