Persistent Monitoring UAV Path Planning Based on Entropy Optimization

The unmanned aerial vehicle (UAV) has demonstrated high efficiency and stability as it is applied in persistent monitoring in large region. Generally, the target region expects to be monitored as soon as possible, so as to obtain high performance of age of information (AOI). This will drive the UAV flying with periodic low-cost trajectories. However, periodic trajectories will incur information security problem, particularly when the adversarial intruders are ready to destroy the region. This study focusses on the UAV path planning on the purpose of improving the monitoring information freshness (MIF) and monitoring privacy protection (MPP). To address the above problems, this paper investigates the single UAV path planning problem of simultaneously optimizing the monitoring AOI and path security. Firstly, by defining the node remaining life, we build the state equation of the monitored nodes. To strengthen the monitoring privacy protection, remaining life entropy of the nodes are introduced to illustrate the path randomness. Then, UAV path planning model is established by maximizing the monitored node remaining life, as well as the average remaining life entropy. Finally, the model is solved by the greedy algorithm (GA). The simulation results show that the proposed method can effectively improve the average remaining life of the monitoring nodes and the security of the UAV monitoring path, which is of great significance for enhancing the security of persistent monitoring and preventing intrusion.