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Optimized Node Deployment and Routing Framework for Enhanced Drone-Based Surveillance Systems
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Keywords
UAV surveillance
node deployment optimization
energy-efficient routing
metaheuristic algorithms
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Abstract
This study proposes an integrated framework for optimizing node deployment and routing in unmanned aerial vehicle (UAV)-based surveillance systems to address critical challenges related to coverage efficiency, energy constraints, and communication reliability. The framework combines hybrid metaheuristic optimization techniques, including particle swarm optimization (PSO) and ant colony optimization (ACO), with clustering-based network management using an enhanced artificial gorilla troops optimizer (GTO). A multi-objective function is formulated to balance coverage maximization, residual energy utilization, and routing reliability. The proposed approach incorporates adaptive node positioning and dynamic routing strategies to respond to real-time environmental and network variations. Simulation results demonstrate significant improvements over conventional methods, achieving a coverage ratio of 94.7%, energy utilization efficiency of 87%, and communication reliability of 96%, while reducing latency to 18 ms. The integration of clustering and energy-aware routing enhances scalability, minimizes communication overhead, and prolongs UAV operational lifetime. The findings highlight the effectiveness of combining deployment optimization and intelligent routing within a unified framework, offering a robust and scalable solution for applications such as border surveillance, infrastructure monitoring, and urban security systems.
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