Multi-UAV assisted air-to-ground data collection for ground sensors with unknown positions

Abstract Unmanned aerial vehicles (UAVs) are increasingly being employed in air-to-ground communication applications due to their lightweight design, high speed, and ability to utilize low-altitude resources. Utilizing UAVs for data collection from wireless ground sensors is an efficient, convenient, and cost-effective approach. However, due to the increasing miniaturization of wireless sensors, obtaining precise locations for deploying such sensors simultaneously in large areas is challenging or costly. While sensor localization techniques have been widely explored, including collaborative localization in sensor networks and received signal strength-based positioning, previous research has not addressed scenarios where sensor positions are completely unknown. Existing work on using UAVs to collect data from ground sensors has not yet addressed the scenario where the positions of ground sensors are unknown. In this article, we design an air-to-ground cooperative communication scheme that leverages ground base stations (BS) to locate sensors with unknown positions and uses UAVs to collect data from the located ground sensors. To address this problem, we decouple it into two separate tasks: sensor localization and UAV path planning. First, we use signals from sensors captured by ground BS to determine their positions. We model the ground channel and apply trilateration techniques to overcome the impact of receiver noise, achieving precise sensor localization. Continuing from obtaining the sensor coordinates, to enhance the efficiency of multi-UAV cooperative tasks, we propose a classification algorithm based on minimum distances. This algorithm divides the regions containing sensors into multiple task areas, with each area served by a single UAV. Finally, we propose a graph-based UAV path planning algorithm to cover all sensors in the subtasks after classification. This ensures optimal data collection from all sensors within the task area. Simulation results reveal that, compared to the existing algorithms, the proposed approach adeptly tackles the data collection challenge when sensor positions remain unknown while significantly enhancing task execution efficiency across diverse environmental conditions. In addition, we have included a detailed discussion analyzing the computational efficiency and real-world deployment challenges of our method, offering valuable insights for applications such as precision agriculture, disaster response, and environmental monitoring.