Open AccessComparative study between metaheuristic algorithms for internet of things wireless nodes localization
Author(s) -
Rana Jassim Mohammed,
Enas Abbas Abed,
ostafa Mahmoud Elgayar
Publication year - 2022
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v12i1.pp660-668
Subject(s) - computer science , metaheuristic , meta heuristic , heuristic , internet of things , wireless sensor network , algorithm , node (physics) , wireless , swarm behaviour , range (aeronautics) , computer network , artificial intelligence , engineering , telecommunications , embedded system , structural engineering , aerospace engineering
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.