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Metaheuristic Techniques to Optimize Trajectory Planning of UAV Swarms: Enhancing Data Acquisition in Wireless Sensor Networks
Unmanned aerial vehicles (UAVs) have become increasingly integrated into various applications due to their cost-efficiency, rapid deployment, flexible maneuvers, and enhanced performance. This has led to the development of a new field called UAV-assisted Wireless Sensor Networks (U-WSNs), which focu...
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| Format: | Thesis |
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AUC Knowledge Fountain
2025
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| Summary: | Unmanned aerial vehicles (UAVs) have become increasingly integrated into various applications due to their cost-efficiency, rapid deployment, flexible maneuvers, and enhanced performance. This has led to the development of a new field called UAV-assisted Wireless Sensor Networks (U-WSNs), which focus on data routing, network performance optimization, and planning UAV trajectories between sensor nodes in wireless sensor networks. In this thesis, a new framework has been proposed to manage a swarm of UAVs cooperatively serving large-scale wireless sensor networks. The framework consists of three optimization problems: distributing sensor nodes among UAVs, finding optimal trajectories in the presence of obstacles, and performing online replanning of UAV paths to adapt to removed or added sensor nodes. The objectives are to minimize the traveling distance of all UAVs, optimize the distance to threats, and minimize data upload time for each UAV. The framework uses metaheuristic optimization to find near-optimal solutions to these optimization problems. The results show that the proposed framework for UAV-assisted data acquisition in WSNs is more comprehensive compared to the literature by providing a multistage optimization procedure that starts with abstract ideas about base stations and sensor nodes and ends with optimized collision-free tours for all UAVs. Moreover, the proposed metaheuristic-based extensive local search framework for solving multi-depot multi-traveling salesmen problems is more effective than other optimization techniques in converging to the Pareto front in large-scale problems. Additionally, metaheuristic algorithms with diverse search operators and less tunable parameters are more efficient as they balance exploration and exploitation. |
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