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Comparative analysis demonstrates enhanced location estimation in wireless sensor networks, indicating improved accuracy and reduced time complexity.
In wireless sensor networks, the data received from sensor nodes is processed and communicated to the next node or cluster head. Sensed data are meaningful only when location information is embedded with the data. Therefore, the sensor should be able to estimate its location information and embed the location information with the sensed data. This research work proposes three hybrid nature-inspired localization optimization algorithms for location estimation of sensor nodes, namely the Hybrid genetic –Bat Localization Algorithm (GA-BAT LA), Hybrid Bat- Particle Swarm Optimization Localization Algorithm (BAT-PSO LA), and Hybrid genetic -Bat- Particle Swarm Optimization Localization Algorithm (GA-BAT-PSO LA). The localization algorithms are developed, implemented, and compared to ensure the accuracy of the estimation of location information. A fixed number of location-known anchor nodes with a variable location-unknown sensor node are considered. The algorithms are compared for a minimum number of iterations, average number of iterations, standard deviation, maximum number of iterations, time complexity, and accuracy to estimate the location of the sensor nodes.
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Perumal et al. (2024) studied this question.
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