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A Singular Value Decomposition Based Upgraded DV-Hop Localization Algorithm for Wireless Sensor Networks

A Singular Value Decomposition Based Upgraded DV-Hop Localization Algorithm for Wireless Sensor Networks

Aarti Jain
Copyright: © 2020 |Volume: 10 |Issue: 1 |Pages: 20
ISSN: 2155-6393|EISSN: 2155-6407|EISBN13: 9781799807278|DOI: 10.4018/IJKBO.2020010104
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MLA

Jain, Aarti. "A Singular Value Decomposition Based Upgraded DV-Hop Localization Algorithm for Wireless Sensor Networks." IJKBO vol.10, no.1 2020: pp.62-81. http://doi.org/10.4018/IJKBO.2020010104

APA

Jain, A. (2020). A Singular Value Decomposition Based Upgraded DV-Hop Localization Algorithm for Wireless Sensor Networks. International Journal of Knowledge-Based Organizations (IJKBO), 10(1), 62-81. http://doi.org/10.4018/IJKBO.2020010104

Chicago

Jain, Aarti. "A Singular Value Decomposition Based Upgraded DV-Hop Localization Algorithm for Wireless Sensor Networks," International Journal of Knowledge-Based Organizations (IJKBO) 10, no.1: 62-81. http://doi.org/10.4018/IJKBO.2020010104

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Abstract

Finding the location of unknown nodes using a computationally inexpensive range-free localization method is one of the most popular research areas in wireless sensor networks (WSNs). DV-hop localization is one of the most popular and localization methods in WSNs. In this article, the authors propose an upgraded DV-Hop based localization method that provides better location accuracy along with minimal computational complexity. In the proposed method, the second step of DV-Hop algorithm is modified by improving the effective average hop size and the third step of DV-Hop is modified to enhance the localization accuracy by minimizing the ranging errors. The said minimization of ranging errors has been achieved by using singular value decomposition-based estimation method. The authors have compared the upgraded DV-Hop localization method with original DV-Hop localization method as well as with improved versions of DV-Hop localization method. The simulation results show that the proposed method has less computational complexity and high location accuracy than the compared methods.

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