Metaheuristic optimization of UAV trajectories for data collection in mission-critical IoT and WSN applications

Unmanned Aerial Vehicles (UAVs) have become vital components in modern wireless systems, particularly within Wireless Sensor Networks (WSNs) and Internet of Things (IoT) ecosystems, where timely and flexible data collection is critical. Efficient trajectory planning is essential to balance mission duration, energy consumption, communication reliability, and service continuity. While traditional models such as the Travelling Salesman Problem (TSP) have been widely used, they often fall short in modelling realistic UAV missions. In contrast, the Close Enough Travelling Salesman Problem (CETSP) provides a more practical framework by allowing UAVs to collect data within a defined communication radius rather than requiring direct contact with sensor nodes. In this study, we perform a comparative analysis of four advanced UAV path planning strategies: CETSP-M, CETSP, TSPN, and TSPN-M. Each method is evaluated in conjunction with metaheuristic optimization algorithms, including Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO), to assess their effectiveness in addressing complex routing challenges. The evaluation focuses on minimizing travel distance across various scenarios involving 25, 50, 100, and 150 target areas. The results indicate that CETSP-M consistently outperforms the other approaches in terms of path length reduction and convergence performance, especially under varying node densities and communication radii. Furthermore, this study standardizes key performance metrics and provides application-driven insights to guide the selection of suitable algorithms for use cases.