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Application of Supervised Learning Approach for Target Localization in Wireless Sensor Network

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Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1132))

Abstract

In the context of indoor environments, the Received Signal Strength Indicator (RSSI) measurements are generally coupled with noise uncertainty due to signal propagation issues such as multipath propagation, Non-Line of Sight (NLOS), reflection. In order to deal with this problem, the localization algorithm is required to be efficient in terms of Localization Accuracy and Execution Speed. The Artificial Neural Network (ANN) does not need prior knowledge of noise statistics during its operations. This paper evaluates the comparison of localization performance of various supervised learning architectures such as Generalized Regression Neural Network (GRNN), Multilayer Perceptron (MLP), Radial Basis Function Network (RBFN), and Feed Forward Neural Network (FFNT) for the Wireless Sensor Network (WSN) based indoor localization problem. The comparison of localization accuracy under the simulated static indoor environment of 100 × 100 m2 with 15 anchor nodes advocate the suitability of the application of supervised learning approach for the indoor localization problems over the traditional trilateration-based approach. The proposed supervised learning implementations are tested and compared with the traditional trilateration-based localization technique by varying the variance of RSSI measurement noise from 0 dBm to 5 dBm in the steps of 1 dBm. Out of all the proposed supervised learning architectures, the GRNN based implementation shows higher localization accuracy.

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Author information

Authors and Affiliations

  1. Department of E&TC, Amrutvahini College of Engineering, Sangamner, India

    Satish R. Jondhale, Rekha P. Labade & Pramod R. Gunjal

  2. Department of Electrical Engineering and Computer Science, MIT, Cambridge, USA

    Raed Shubair

  3. Polytechnic University of Valencia, Valencia, Spain

    Jaime Lloret

Authors
  1. Satish R. Jondhale
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  2. Raed Shubair
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  3. Rekha P. Labade
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  4. Jaime Lloret
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  5. Pramod R. Gunjal
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Corresponding author

Correspondence to Satish R. Jondhale .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

  2. Department of Computer Sciences, Purdue University, West Lafayette, IN, USA

    Bharat K. Bhargava

  3. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Marcin Paprzycki

  4. Department of Computer Science and Engineering, National Institute of Technology, Delhi, India

    Narottam Chand Kaushal

  5. School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou, Jiangsu, China

    Wei-Chiang Hong

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Jondhale, S.R., Shubair, R., Labade, R.P., Lloret, J., Gunjal, P.R. (2020). Application of Supervised Learning Approach for Target Localization in Wireless Sensor Network. In: Singh, P., Bhargava, B., Paprzycki, M., Kaushal, N., Hong, WC. (eds) Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's. Advances in Intelligent Systems and Computing, vol 1132. Springer, Cham. https://doi.org/10.1007/978-3-030-40305-8_24

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  • DOI: https://doi.org/10.1007/978-3-030-40305-8_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40304-1

  • Online ISBN: 978-3-030-40305-8

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