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Target localization in wireless sensor networks using online semi-supervised support vector regression.

Jaehyun Yoo1, H Jin Kim2

  • 1Department of Mechanical and Aerospace Engineering, Seoul National University, 599 Gwanangno, Gwanak-gu, Seoul KS013, Korea. yjh5455@snu.ac.kr.

Sensors (Basel, Switzerland)
|May 30, 2015
PubMed
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This study introduces an online semi-supervised support vector regression (OSS-SVR) algorithm for accurate target localization in wireless sensor networks (WSNs). OSS-SVR reduces labeled data needs and adapts to changing noise, outperforming other methods.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Machine learning offers robust target localization in wireless sensor networks (WSNs), despite nonlinear and noisy sensor data.
  • Efficient and adaptive learning is crucial for WSNs to handle dynamic environments and varying noise characteristics.

Purpose of the Study:

  • To introduce an Online Semi-Supervised Support Vector Regression (OSS-SVR) algorithm for enhanced target localization in WSNs.
  • To reduce the dependency on large labeled datasets while maintaining high estimation accuracy.
  • To enable adaptive learning for tracking system changes, such as fluctuating noise levels.

Main Methods:

  • Development and implementation of the Online Semi-Supervised Support Vector Regression (OSS-SVR) algorithm.
Keywords:
online support vector regressionsemi-supervised learningwireless sensor network

Related Experiment Videos

Last Updated: Apr 11, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

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  • Evaluation of OSS-SVR against semi-supervised manifold learning, online Gaussian process, and online semi-supervised colocalization.
  • Localization of a mobile robot within a WSN using the proposed and comparative algorithms.

    • Main Results:

    • OSS-SVR demonstrated superior accuracy, especially with limited labeled training data.
    • The algorithm exhibited robustness against varying noise conditions.
    • OSS-SVR achieved faster computation and superior localization performance compared to alternative methods.

    Conclusions:

    • OSS-SVR provides an efficient and adaptive solution for target localization in WSNs.
    • The algorithm effectively balances accuracy with reduced data requirements and adaptability to environmental changes.
    • OSS-SVR represents a significant advancement in WSN localization techniques.