Minimizing The Global Waiting Time of Swarm UAV Network for Efficient Battery Charging in Persistent Monitoring Scenario

Persistent monitoring is important in indoor farming to maintain a safe environment for plant growth. Typically, various static sensors are employed to ensure continuous surveillance within indoor farming. However, these static sensors are susceptible to damage, and repairing them can be time-consuming. Such repairs can disrupt the real-time monitoring process. In this study, we employ Crazyflie swarmUAVs to substitute for malfunctioning static sensors while they undergo repairs or replacements. The primary objective of this research is to develop Ant Colony Optimization (ACO) and Cloud-Based Drone Navigation (CBDN) algorithms to minimize the global waiting time of the swarm UAV network. Simulation results showed that the proposed modified algorithm successfully allocates the Crazyflie UAVs to specific task areas and enables efficient battery charging, thereby minimizing the overall travel time.