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Tracking mobile users in wireless networks via semi-supervised colocalization.

Jeffrey Junfeng Pan1, Sinno Jialin Pan, Jie Yin

  • 1Facebook Inc., 1601 S. California Ave., Palo Alto, CA 94304, USA. panjunfeng@gmail.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 17, 2011
PubMed
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This study introduces a new machine learning approach for accurate user localization in wireless sensor networks (WSNs). The method effectively uses limited labeled data and unlabeled data for precise mobile user and access point positioning.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Machine Learning

Background:

  • Wireless sensor networks (WSNs) are increasingly popular for practical applications.
  • Accurate user localization in WSNs is challenging due to the need for extensive labeled location data and knowledge of transmitter/access point locations.

Purpose of the Study:

  • To develop a novel machine learning approach for accurate user localization in WSNs with limited labeled data.
  • To simultaneously learn the locations of mobile users and access points by leveraging both labeled and unlabeled data.

Main Methods:

  • A hybrid approach combining collaborative filtering and graph-based semi-supervised learning.
  • A two-phase solution involving an offline manifold-based model training and an online weighted k-nearest-neighbor localization.

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  • Extension to an online, incremental model adaptable to sequential data and environmental changes, incorporating an action model for additional sensor signals.

    • Main Results:

    • The proposed framework demonstrates higher accuracy compared to state-of-the-art systems.
    • Requires significantly less calibration effort, as validated through experiments on three distinct testbeds.
    • Effectively utilizes both labeled and unlabeled data from mobile devices and access points.

    Conclusions:

    • The novel machine learning framework offers a more accurate and efficient solution for user localization in wireless sensor networks.
    • The approach effectively addresses the challenge of limited labeled data by integrating semi-supervised learning techniques.
    • The adaptable and incremental nature of the model allows for real-world deployment and continuous improvement in mobile tracking performance.