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

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 sys...

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Published in:Energies (Basel) 2023-01, Vol.16 (1), p.555
Main Authors: Molla, Jahir Pasha, Dhabliya, Dharmesh, Jondhale, Satish R., Arumugam, Sivakumar Sabapathy, Rajawat, Anand Singh, Goyal, S. B., Raboaca, Maria Simona, Mihaltan, Traian Candin, Verma, Chaman, Suciu, George
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Energy consumption
Energy efficiency
generalized regression neural network (GRNN)
Indoor environments
indoor localization
kalman filter (KF)
Kalman filtering
Kalman filters
Localization
Machine learning
Methods
Neural networks
Polynomials
Propagation
received signal strength indicator (RSSI)
Sensors
Signal processing
Signal strength
Support vector machines
support vector regression (SVR)
Trilateration
Wireless sensor networks
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16010555