#5606. Comparative study between metaheuristic algorithms for internet of things wireless nodes localization

August 2026publication date
Proposal available till 22-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Science (all);
Control and Systems Engineering;
Electrical and Electronic Engineering;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Keywords:
Bacteria foraging algorithm; Biogeography-based; Butterfly optimization algorithm; Grey wolf optimization; Optimization; Particle swarm optimization; Salp swarm algorithm; Wireless sensor network

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