Advanced Trajectory Planning for Unmanned Aerial Vehicles in the Context of Data Collection from Spatially Distributed Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are extensively used for monitoring large-scale areas with sensors having different coverage zones. Unmanned Aerial Vehicles (UAVs) are deployed to efficiently collect data from these distributed nodes. The effectiveness of this process depends on optimizing the UAV’s flight path, formulated as the Close Enough Traveling Salesman Problem (CETSP), known to be NP-hard. This study presents hybrid methods that integrate heuristic algorithms with geometric strategies to solve the CETSP efficiently. The main contribution lies in proposing efficient, fast-executing, and easily programmable approaches that follow a structured process: identifying new target points when zones intersect, determining near-optimal visiting sequences with heuristic algorithms, and applying iterative geometric refinements to shorten the route. A total of 92 hybrid algorithms based on seven distinct approaches are evaluated using four performance metrics. Experimental results demonstrate the high efficiency of the proposed methods and their strong potential for real-world UAV-assisted data collection tasks in wireless sensor networks.