An Optimized 2D Ground Area Coverage Using UAV-Enabled Sensor Networks

To meet the demands of terrestrial sensor network applications, unmanned aerial vehicles (UAVs) are increasingly used for efficient ground coverage. This paper presents a two-step methodology for 2D ground coverage using UAV-enabled sensor networks, integrating antenna tilt modeling with angle-aware path optimization based on ant colony optimization (ACO). This combination enables more efficient turning angle control while maintaining competitive path lengths. In the first step, an optimal set of waypoints is generated for the movement of the UAV. This is achieved through aerial decomposition, incorporating the antenna footprint with and without tilting. The ground area is divided into UAV footprints, with each footprint’s center serving as a waypoint for the UAV’s movement. The decomposition is optimized using a metaheuristic approach to balance the inner and outer sensing coverage. The proposed method is evaluated using three optimization algorithms: particle swarm optimization (PSO), genetic algorithm (GA), and cuckoo optimization algorithm (COA). The second step focuses on path planning to visit all generated waypoints while minimizing travel distance and turning angles. The turning angle is measured in terms of the yaw angle, which defines the rotation of the UAV around its vertical axis. We employ an ACO-based coverage path planning algorithm to minimize the path length and the turning angle. Performance is compared with two existing methods, showing an approximate improvement of 32.78% in the turning angle reduction.