Node Placement Optimization in WSN with Adaptive PSO for Energy Efficiency and Optimal Coverage
Keywords:
Wireless Sensor Network, Adaptive Particle Swarm Optimization, Energy Efficiency, Area Coverage, Node OptimizationAbstract
The development of Wireless Sensor Network (WSN) technology has encouraged its widespread application in environmental monitoring systems and the Internet of Things (IoT). However, the energy limitations of sensor nodes and the imbalance in node placement distribution remain major challenges that impact energy efficiency and blank spot areas. This study aims to optimize the placement of sensor nodes in WSN to achieve maximum energy efficiency and optimal area coverage through the application of the Adaptive Particle Swarm Optimization (APSO) algorithm. This study uses a quantitative approach based on experimental simulation, with a two-dimensional network model measuring 100×100 meters and 50 nodes. APSO parameters, such as inertia weight and acceleration coefficient, are adjusted adaptively to balance exploration and exploitation. Evaluation is performed by comparing the performance of APSO against classical PSO and random placement methods using indicators of area coverage ratio, total energy consumption, and convergence time. The simulation results show that APSO is capable of increasing area coverage by up to 96.8%, saving energy by 31.5%, and accelerating convergence by up to 23% compared to classic PSO. The resulting node distribution is more even, reducing empty areas and extending the network's lifetime. The application of APSO has proven to be effective in overcoming energy limitations and optimizing coverage in WSNs. The research objectives were achieved with significant improvements in network efficiency and performance. Further studies are recommended to develop the application of APSO in dynamic routing optimization and adaptive energy management in large-scale smart environment and IoT systems.
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