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Adaptive Sensor Ranking Based on Utility Using Logistic Regression

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

Abstract

Wireless Sensor Networks (WSN) consists of several tens to hundreds of nodes, interacting with each other. Thus, they have multiple communications between them, transferring and receiving several packets of data to each other. In order to reduce the overall traffic in the network and lessen the presence of redundant node data, this paper proposes an adaptive sensor ranking method by evaluating the task necessity, utility, and region coverage of a particular node in a given WSN. Logistic regression has been used to adaptively train the WSN to assign a status to node as on or off, thereby, decreasing the overall data transmission into the network, while still accounting for the entire range of the WSN.

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Author information

Authors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Vellore, India

    S. Sundar, Cyril Joe Baby, Anirudh Itagi & Siddharth Soni

Authors
  1. S. Sundar
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  2. Cyril Joe Baby
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  3. Anirudh Itagi
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  4. Siddharth Soni
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Corresponding author

Correspondence to S. Sundar .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Sundar, S., Baby, C.J., Itagi, A., Soni, S. (2020). Adaptive Sensor Ranking Based on Utility Using Logistic Regression. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_29

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