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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2023-01-01
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| Online Access: | https://www.mdpi.com/1996-1073/16/1/555 |
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| author | 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 Suciu |
| author_facet | 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 Suciu |
| author_sort | Jahir Pasha Molla |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:02:19Z |
| format | Article |
| id | doaj.art-cafd59a0fb3349c3a72bd3d19016aaa2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T10:02:19Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-cafd59a0fb3349c3a72bd3d19016aaa22023-11-16T15:20:43ZengMDPI AGEnergies1996-10732023-01-0116155510.3390/en16010555Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector RegressionJahir Pasha Molla0Dharmesh Dhabliya1Satish R. Jondhale2Sivakumar Sabapathy Arumugam3Anand Singh Rajawat4S. B. Goyal5Maria Simona Raboaca6Traian Candin Mihaltan7Chaman Verma8George Suciu9Department of Computer Science and Engineering, G. Pullaiah College of Engineering and Technology (GPCET), Kurnool 518002, IndiaDepartment of IT, Vishwakarma Institute of Information Technology, Pune 411048, IndiaElectronics and Telecommunication Department, Amrutvahini College of Engineering, Sangamner 422608, IndiaDepartment of ECE, Dr. N.G.P. Institute of Technology, Coimbatore 641048, IndiaSchool of Computer Science and Engineering, Sandip University, Nashik 422213, IndiaFaculty of Information Technology, City University, Petaling Jaya 46100, MalaysiaICSI Energy Department, National Research and Development Institute for Cryogenics and Isotopic Technologies, 240050 Ramnicu Valcea, RomaniaFaculty of Building Services, Technical University of Cluj-Napoca, 40033 Cluj-Napoca, RomaniaFaculty of Informatics, University of Eötvös Loránd, 1053 Budapest, HungaryR&D Department Beia Consult International, 041386 Bucharest, RomaniaThe 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.https://www.mdpi.com/1996-1073/16/1/555received signal strength indicator (RSSI)trilaterationindoor localizationkalman filter (KF)support vector regression (SVR)generalized regression neural network (GRNN) |
| spellingShingle | 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 Suciu Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression Energies received signal strength indicator (RSSI) trilateration indoor localization kalman filter (KF) support vector regression (SVR) generalized regression neural network (GRNN) |
| title | Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression |
| title_full | Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression |
| title_fullStr | Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression |
| title_full_unstemmed | Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression |
| title_short | Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression |
| title_sort | energy efficient received signal strength based target localization and tracking using support vector regression |
| topic | received signal strength indicator (RSSI) trilateration indoor localization kalman filter (KF) support vector regression (SVR) generalized regression neural network (GRNN) |
| url | https://www.mdpi.com/1996-1073/16/1/555 |
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