WSN Coverage Optimization Based on the Improved Multi-Objective Sparrow Search Algorithm

To enhance the coverage and overall performance of wireless sensor networks, this paper introduces an improved multi-objective sparrow search algorithm (SSA). Building on the basic SSA, we integrate Tent mapping and reverse learning via shot imaging to initialize the population and increase its dive...

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Veröffentlicht in:2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE) S. 175 - 179
Hauptverfasser: Jiao, Xuyang, Du, Fei, Zhao, Xiongwen, Geng, Suiyan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 10.01.2025
Schlagworte:
Accuracy
Artificial neural networks
Buildings
coverage optimization
Genetic algorithms
Imaging
multiple target
Optimization
Particle swarm optimization
Simulation
Sorting
SSA
Wireless sensor networks
WSN
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Zusammenfassung:To enhance the coverage and overall performance of wireless sensor networks, this paper introduces an improved multi-objective sparrow search algorithm (SSA). Building on the basic SSA, we integrate Tent mapping and reverse learning via shot imaging to initialize the population and increase its diversity. We have enhanced existing boundary handling strategies to boost the algorithm's global search capability and accuracy. By incorporating fast non-dominated sorting, a crowding strategy, and optimal external archiving, the algorithm is adapted into a multi-objective framework to further elevate its performance. Simulation results demonstrate that the proposed algorithm can effectively improve network coverage and achieve optimal network deployment.
DOI:10.1109/NNICE64954.2025.11064431