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Computational Intelligence for Localization of Mobile Wireless Sensor Networks

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Computational Intelligence: Theories, Applications and Future Directions - Volume II

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 799))

Abstract

The localization of mobile nodes in wireless sensor networks has been formulated as a concave optimization problem. The same has been approached through biologically inspired firefly algorithm (FA) and the artificial bee colony (ABC) algorithm. In the proposed method, a mobile node approximates its distance from multiple anchor nodes. The distance and the coordinates of the anchors are the parameters used by FA and ABC algorithms for the accurate estimation of the location by minimizing the suitably defined localization error. The localization method used here is iterative, and it works in a distributed fashion. A comparison of the performances of FA and ABC algorithms in terms of localization accuracy and computation time has been presented. FA exhibits higher accuracy of localization, while ABC is quicker.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru, India

    Vaishali R. Kulkarni

  2. Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, India

    Veena Desai

Authors
  1. Vaishali R. Kulkarni
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  2. Veena Desai
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Corresponding author

Correspondence to Vaishali R. Kulkarni .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

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Kulkarni, V.R., Desai, V. (2019). Computational Intelligence for Localization of Mobile Wireless Sensor Networks. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_34

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