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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2018

30.10.2017

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

verfasst von: Wei Zhao, Shoubao Su, Fei Shao

Erschienen in: Wireless Personal Communications | Ausgabe 4/2018

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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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Metadaten
Titel
Improved DV-Hop Algorithm Using Locally Weighted Linear Regression in Anisotropic Wireless Sensor Networks
verfasst von
Wei Zhao
Shoubao Su
Fei Shao
Publikationsdatum
30.10.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-5017-2

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