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Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

Author

Listed:
  • Mihoubi Miloud

    (EEDIS Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria)

  • Rahmoun Abdellatif

    (LabRI-SBA Laboratory, Higher School of Computer Science, Sidi Bel-Abbes, Algeria)

  • Pascal Lorenz

    (University of Haute Alsace, France)

Abstract

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

Suggested Citation

  • Mihoubi Miloud & Rahmoun Abdellatif & Pascal Lorenz, 2019. "Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 10(1), pages 82-109, January.
    • Handle: RePEc:igg:jdst00:v:10:y:2019:i:1:p:82-109
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