A Gale–Shapley matching game for data collection waypoints using multi-UAVs in wide-area target localization applications

Unmanned aerial vehicles (UAVs) have emerged as a powerful enabler for smart environmental monitoring, particularly in applications that require rapid, flexible, and on-demand data collection such as target localization. Compared to stationary IoT sensing systems, UAVs provide mobility and adaptability, making them well suited for large-scale and hard-to-access areas. However, their limited onboard energy significantly constrains mission duration and coverage, posing a major challenge for wide-area sensing tasks. Therefore, in this work, an energy-aware, information-driven multi-UAV target localization framework is proposed. The framework integrates waypoint (WP) selection and clustering, path planning via Tabu Search, task assignment using two-sided Gale–Shapley game, a probabilistic localization algorithm based on Bayesian, and an interpolation and contouring mechanism. This mechanism introduces regions of importance (RoIs), coupled with a Potential Importance (PI) metric, to guide adaptive waypoint selection. This closed-loop design enables the system to progressively refine sensing decisions, improve localization efficiency while reducing redundant data collection. The proposed framework is assessed in comparison to different benchmarks, where the results show that the proposed approach consistently achieves better performance by up to ∼30%–60% in terms of localization time, mission completion time, energy consumption, system’s residual energy, and quality of localization. Furthermore, scalability and adaptability analyses are conducted under varying area sizes and target strengths, confirming the robustness of the proposed approach. Overall, the results demonstrate the effectiveness of the framework in achieving accurate and timely localization while preserving the energy resources of the UAV swarm.