Abstract
WSNs (Wireless sensor networks) are used in multiple applications including IoT (Internet of Things) applications like intelligent control, prediction, tracking, and other communication network services (Internet-of-Things). Due to their limited design for two-dimensional space, high computing costs, or sensitivity to measurement errors, the typical localization frameworks might not perform well in real-world settings. Location information of deployed sensor nodes in their surrounding environments is important for algorithmic three-dimensional localizations. But there are drawbacks to current 3D localization algorithms in many parameters including complexity, positional precisions, and excessive energy consumption. Hence, this work proposes Enhanced 3D-DV-Hop (3D-Distance Vector Hop) localizations based on PSO (Particle Swarm Optimization) and GAs (Genetic algorithms) for the aforementioned issues. To further increase the diversity and accuracy of the DV-Hop outputs, a learning technique is employed. The learning technique leads to an improvement in search effectiveness, convergence speed, and result in quality. The simulation results demonstrate that the suggested strategy can improve positioning coverage while maintaining positioning accuracy.
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All data generated or analysed during this study are included in the manuscript.
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Reddy, M.R., Ravi Chandra, M.L. An enhanced 3D-DV-hop localisation algorithm for 3D wireless sensor networks. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03356-y
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DOI: https://doi.org/10.1007/s11276-023-03356-y