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Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks

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Abstract

The original DV-hop algorithm performs pretty well in isotropic Wireless Sensor Networks in which nodes distribute uniformly. However, the localization accuracy degrades severely in anisotropic networks caused by uneven nodal distribution or irregularity of deployment region. In this paper, we propose a novel DV-hop algorithm based on Locally Weighted Linear Regression (LWLR-DV-hop), in which kernel method is adopted to improve the localization accuracy by raising the weight of neighboring anchor nodes. In the simulation section, algorithms are evaluated within two deployments and three topologies: the regular and random deployments, the L-shaped, O-shaped and X-shaped topologies. As performance metrics, the Average Localization Error and the Cumulative Distribution Function are used. The results of simulation and experiment reveal that LWLR-DV-hop performs better than original DV-Hop in anisotropic networks of different topologies, in which localization accuracy is improved by about 40% on average.

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Acknowledgments

The work is partially supported by the National Natural Science Foundation of China (No.61375121), the Natural Scientific Research Funds for Jiangsu Universities (Nos.17KJB520008, 17KJA520001), the Scientific Research Foundation of Jinling Institute of Technology (Nos. JIT-B-201429, jit-rcyj-201505, JIT-2016-jlxm-20), and sponsored by the Funds for Innovation Team of Swarm Computing & Smart Software led by Prof. SB Su (Corresponding Author).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Data Science & Smart Software, Jinling Institute of Technology, Nanjing, 211169, China

    Wei Zhao, Shoubao Su & Fei Shao

  2. School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China

    Wei Zhao, Shoubao Su & Fei Shao

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  1. Wei Zhao
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  2. Shoubao Su
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Correspondence to Shoubao Su.

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Zhao, W., Su, S. & Shao, F. Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks. Wireless Pers Commun 98, 3335–3353 (2018). https://doi.org/10.1007/s11277-017-5017-2

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