UAV Path Optimization for WSN in Smart Agriculture

Unmanned Aerial Vehicles (UAVs) play a crucial role in Wireless Sensor Network (WSN) applications, particularly in smart agriculture, enabling efficient data collection from large-scale sensor deployments. A key challenge in this field is optimizing the UAV's flight path while considering real-world constraints such as UAV dynamics, sensor heterogeneity, communication ranges, hovering requirements, overlapping cluster heads, altitude variation, and UAV limitations. Many existing studies oversimplify this problem by considering some of these constraints while neglecting others, leading to suboptimal solutions. In this work, we propose a novel UAV path optimization algorithm, "OptiFly", designed for WSN-based agricultural systems, incorporating all these constraints in a single framework. The optimization problem is formulated as a Nonlinear Programming (NLP) model and solved using an appropriate solver. Unlike existing approaches, OptiFly integrates UAV kinematics, dynamics, and aerodynamics into the optimization process while accounting for heterogeneous sensors, as commonly observed in real-world agricultural implementations. The algorithm ensures that the UAV hovers at an optimal position within each sensor's coverage area, minimizing unnecessary movements. Additionally, we consider overlapping cluster heads, allowing the UAV to hover over their intersection regions and collect data from both at the same point, thereby minimizing travel distance. For sensors with small communication ranges, the UAV dynamically adjusts its altitude to maintain connectivity while conserving energy. Furthermore, OptiFly enables the UAV to be self-aware of its endurance, terminating the mission before exceeding its maximum flight time. Power consumption is also considered based on the UAV's dynamics. Simulation results demonstrate that OptiFly significantly reduces both travel distance and energy consumption compared to unoptimized and optimized approaches. Additionally, the proposed algorithm proves to have low computational complexity in various scenarios. The achieved improvements validate the effectiveness of our method, making it a promising solution for UAV-WSN applications in smart agriculture and beyond.