Energy-Efficient UAV-Based Data Collection 3-D Trajectory Optimization With Wireless Power Transfer for Forest Monitoring

Forest environment monitoring is crucial for detecting and predicting natural disasters, such as forest fires. Uncrewed aerial vehicle (UAV) is frequently used to acquire environmental data in forest ecosystems for monitoring purposes. However, the complex geographical terrain of forests, limited UAV battery capacity, and power constraints of ground IoT devices pose significant challenges to data collection. In this article, we design a 3-D trajectory optimization framework for a UAV to collect forest environmental data, considering the altitude limitations of the UAV in complex forest environments and the need to recharge IoT devices using wireless power transfer (WPT) technology, aiming to extend the operational lifetime of the network. Specifically, we aim to achieve a desirable balance between the amount of data collected by the UAV and its energy consumption incurred by data collection and sensor recharging. To this end, we first formulate the trajectory design of the UAV for data collection in forest areas as a nonlinear optimization problem, aiming to maximize the amount of sensor data collected while minimizing the energy consumption of the UAV. To solve this problem, we propose a converging-trajectory design and data collection (C-TDDC) method, which includes two subalgorithms. The first is an ant colony optimization-based traveling salesman problem (ACO-TSP) algorithm to generate optimal UAV trajectories. The second is a proximal policy optimization-based reinforcement learning algorithm, which balances data collection and energy consumption for the UAV. The simulation results demonstrate that the proposed C-TDDC algorithm exhibits more stable convergence and performs better in completing data collection tasks, outperforming both the state-of-the-art algorithm and baseline schemes.