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

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 and George Suciu ()
Additional contact information
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

Energies, 2023, vol. 16, issue 1, 1-17

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.

Keywords: received signal strength indicator (RSSI); trilateration; indoor localization; kalman filter (KF); support vector regression (SVR); generalized regression neural network (GRNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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