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Localization Optimization in WSNs Using Meta-Heuristics Optimization Algorithms: A Survey

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Abstract

In Wireless Sensor Networks, node localization is one of the most important system parameters. Determining the exact position of nodes in these networks is one of vital and tedious tasks. This paper presents a review of the most localization methods which optimize the localization error. It provides a new taxonomy of techniques used in this field, including Mobile Anchor, Machine Learning, Matematical Models and Meta-heuristics. In this later, we survey its different algorithms such as Genetic Algorithm, Particle Swarm optimization, Ant Colony Optimization, BAT optimization algorithm, Firefly Optimization Algorithm, Flower Pollination Algorithm, Grey Wolf Optimization algorithm, Artificial Bees Colony Optimization Algorithm, Fish Swarm Optimization Algorithm and others. Further, the comparison between these metaheuristics algorithms based localization optimization is done. Finally, a comprehensive discussion of the performance parameters such as accuracy, convergence rate, energy consumption and the number of localized nodes is given.

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Notes

  1. For example, inside buildings.

  2. For example, in forest.

  3. For instance, in oceans.

  4. For instance, in mines.

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Authors and Affiliations

  1. Research Unit LAMOS, Faculty of Exact Sciences, University of Bejaia, 06000, Bejaia, Algeria

    Zahia Lalama & Samra Boulfekhar

  2. Mechatronics Laboratory-E1764200, Optics and Precision Mechanics Institute, University of Setif 1, 19000, Setif, Algeria

    Fouzi Semechedine

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  1. Zahia Lalama
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  2. Samra Boulfekhar
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  3. Fouzi Semechedine
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Correspondence to Zahia Lalama.

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Lalama, Z., Boulfekhar, S. & Semechedine, F. Localization Optimization in WSNs Using Meta-Heuristics Optimization Algorithms: A Survey. Wireless Pers Commun 122, 1197–1220 (2022). https://doi.org/10.1007/s11277-021-08945-8

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