BEIA is happy to announce a new publication in MDPI Energies Journal – Special Issue Advanced Technologies in Agricultural Engineering and Energy Optimization.
See more info about the paper below.
Paper name: Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression
Authors: by 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
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)
Acknowledgments
This paper was partially supported by UEFISCDI Romania and MCI through BEIA projects AutoDecS, SOLID-B5G, T4ME2, DISAVIT, PIMEO-AI, AISTOR, MULTI-AI, ADRIATIC, Hydro3D, PREVENTION, DAFCC, EREMI, ADCATER, MUSEION, FinSESCo, iPREMAS, IPSUS, U-GARDEN, CREATE and by European Union′s Horizon 2020 research and innovation program under grant agreements No. 883522 (S4ALLCITIES) and No. 101016567 (VITAL-5G).
Find out the full paper here.