Optimizing Wireless Sensor Network Localization: Hybrid Natural Inspired with Bat Swarm Algorithm

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Comparative analysis demonstrates enhanced location estimation in wireless sensor networks, indicating improved accuracy and reduced time complexity.

Hybrid nature-inspired algorithms improve sensor node coordinate estimation, achieving superior accuracy over conventional bat swarm algorithms in wireless sensor networks.
Comparative analysis evaluates genetic algorithm, bat algorithm, and particle swarm optimization variants using anchor nodes across iteration metrics and time complexity.
Accurate location estimation ensures meaningful telemetry across sensor nodes, though physical hardware experiments are required to validate algorithmic performance in field settings.

Perumal et al. (2024) studied this question.

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View Full Paper

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Authors

Comparative analysis demonstrates enhanced location estimation in wireless sensor networks, indicating improved accuracy and reduced time complexity.

Hybrid nature-inspired algorithms improve sensor node coordinate estimation, achieving superior accuracy over conventional bat swarm algorithms in wireless sensor networks.
Comparative analysis evaluates genetic algorithm, bat algorithm, and particle swarm optimization variants using anchor nodes across iteration metrics and time complexity.
Accurate location estimation ensures meaningful telemetry across sensor nodes, though physical hardware experiments are required to validate algorithmic performance in field settings.

Perumal et al. (2024) studied this question.

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