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Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks

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Abstract

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.

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Acknowledgements

This work is partially supported by the National Institute of Technology, Hamirpur, Himachal Pradesh of India (No. B-198) and Ministry of Human Resource Developments (MHRD) of India with Fundamental Research Funds (No. 2K13-PhD-ECE-227).

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, 177005, India

    Gaurav Sharma & Ashok Kumar

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  1. Gaurav Sharma
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  2. Ashok Kumar
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Correspondence to Gaurav Sharma.

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Sharma, G., Kumar, A. Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks. Telecommun Syst 67, 163–178 (2018). https://doi.org/10.1007/s11235-017-0328-x

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  • DOI: https://doi.org/10.1007/s11235-017-0328-x

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