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Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Author

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  • Soumya J. Bhat

    (Manipal Academy of Higher Education)

  • K. V. Santhosh

    (Manipal Academy of Higher Education)

Abstract

Internet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

Suggested Citation

  • Soumya J. Bhat & K. V. Santhosh, 2022. "Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(2), pages 309-321, February.
    • Handle: RePEc:spr:telsys:v:79:y:2022:i:2:d:10.1007_s11235-021-00862-2
    DOI: 10.1007/s11235-021-00862-2
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    References listed on IDEAS

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    1. Gaurav Sharma & Ashok Kumar, 2018. "Fuzzy logic based 3D localization in wireless sensor networks using invasive weed and bacterial foraging optimization," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(2), pages 149-162, February.
    2. Siqi Zhang & Fang Fan & Wei Li & Shu-Chuan Chu & Jeng-Shyang Pan, 2021. "A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(2), pages 213-223, October.
    3. Sana Messous & Hend Liouane & Noureddine Liouane, 2020. "Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 73(1), pages 75-86, January.
    4. M. Mazhar Rathore & Anand Paul & Awais Ahmad & Gwanggil Jeon, 2017. "IoT-Based Big Data: From Smart City towards Next Generation Super City Planning," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 28-47, January.
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    Cited by:

    1. Achour Achroufene, 2023. "RSSI-based Hybrid Centroid-K-Nearest Neighbors localization method," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(1), pages 101-114, January.

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