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Swarm Intelligence Based Localization in Wireless Sensor Networks

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7080))

Abstract

In wireless sensor networks, sensor node localization is an important problem because sensor nodes are randomly scattered in the region of interest and they get connected into network on their own. Finding location without the aid of Global Positioning System (GPS) in each node of a sensor network is important in cases where GPS is either not accessible, or not practical to use due to power, cost, or line of sight conditions. The objective of this paper is to find the locations of nodes by using Particle Swarm Optimization and Artificial Bee Colony algorithm and compare the performance of these two algorithms. The term swarm is used in a general manner to refer to a collection of interacting agents or individuals. We also propose multi stage localization and compared multi stage localization performance with single stage localization.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India

    Dama Lavanya & Siba K. Udgata

Authors
  1. Dama Lavanya
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  2. Siba K. Udgata
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Editor information

Editors and Affiliations

  1. Faculty of Informatics, Mahasarakham University, Khamreing, 44150, Kantarawichai, Mahasarakham, Thailand

    Chattrakul Sombattheera

  2. Department of Computers and Information Science, University of Hyderabad, 500046, Hyderabad, Andhra Pradesh, India

    Arun Agarwal

  3. Department of Computer and Information Sciences, University of Hyderabad, 500046, Hyderabad, India

    Siba K Udgata

  4. School of Information Technology, King Mongkut’s University of Technology, Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

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© 2011 Springer-Verlag Berlin Heidelberg

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Lavanya, D., Udgata, S.K. (2011). Swarm Intelligence Based Localization in Wireless Sensor Networks. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_28

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  • DOI: https://doi.org/10.1007/978-3-642-25725-4_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25724-7

  • Online ISBN: 978-3-642-25725-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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