TRACT: Towards Large-Scale Crowdsensing With High-Efficiency Swarm Path Planning.

Unmanned Aerial Vehicle (UAV) sensing swarms have grown in popularity because of their advantages of low-cost, high mobility, and high maneuverability in crowdsensing. The effectiveness of UAV swarm sensing is determined by the path of each UAV in the swarm. However, it is challenging to do path planning for a UAV swarm to satisfy various data requirements in large-scale crowdsensing tasks due to the area scale and long-term planning requirements. To achieve this, we propose TRACT, TowaRds lArge-sCale crowdsensing wiTh high-efficiency swarm path planning algorithm. We expand the sensing coverage problem for large-scale modeling. Furthermore, we propose a policy matrix searching technique with a simulated annealing algorithm to address the complexity of long-term planning. Our experiment shows that TRACT adapts to various data requirements, with the performance improved by 90% in terms of KL-divergence between the time-aggregated data value and the data requirements, while time cost for path planning was reduced by 43% compared with the previous state-of-the-art method approach.