IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i1p555-d1023975.html
   My bibliography  Save this article

Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression

Author

Listed:
  • Jahir Pasha Molla

    (Department of Computer Science and Engineering, G. Pullaiah College of Engineering and Technology (GPCET), Kurnool 518002, India)

  • Dharmesh Dhabliya

    (Department of IT, Vishwakarma Institute of Information Technology, Pune 411048, India)

  • Satish R. Jondhale

    (Electronics and Telecommunication Department, Amrutvahini College of Engineering, Sangamner 422608, India)

  • Sivakumar Sabapathy Arumugam

    (Department of ECE, Dr. N.G.P. Institute of Technology, Coimbatore 641048, India)

  • Anand Singh Rajawat

    (School of Computer Science and Engineering, Sandip University, Nashik 422213, India)

  • S. B. Goyal

    (Faculty of Information Technology, City University, Petaling Jaya 46100, Malaysia)

  • Maria Simona Raboaca

    (ICSI Energy Department, National Research and Development Institute for Cryogenics and Isotopic Technologies, 240050 Ramnicu Valcea, Romania)

  • Traian Candin Mihaltan

    (Faculty of Building Services, Technical University of Cluj-Napoca, 40033 Cluj-Napoca, Romania)

  • Chaman Verma

    (Faculty of Informatics, University of Eötvös Loránd, 1053 Budapest, Hungary)

  • George Suciu

    (R&D Department Beia Consult International, 041386 Bucharest, Romania)

Abstract

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.

Suggested Citation

  • Jahir Pasha Molla & Dharmesh Dhabliya & Satish R. Jondhale & Sivakumar Sabapathy Arumugam & Anand Singh Rajawat & S. B. Goyal & Maria Simona Raboaca & Traian Candin Mihaltan & Chaman Verma & George Su, 2023. "Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression," Energies, MDPI, vol. 16(1), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:555-:d:1023975
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/555/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/555/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:555-:d:1023975. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.