Mobile Node Localization and Tracking in Wireless Sensor Networks using Extended Kalman Filter
C. Ambika Bhuvaneswari1, M. Sivarathina Bala2
1C. Ambika Bhuvaneswari, Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai (T.N), India.
2M. Sivarathina Bala, Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai (T.N), India.
Manuscript received on 15 February 2019 | Revised Manuscript received on 06 March 2019 | Manuscript Published on 08 June 2019 | PP: 468-470 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10980275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mobile node Localization and tracking is a continuous research on wireless sensor networks (WSN). Tracking the mobile node without an external hardware device like Global Positioning System (GPS) is the major advantage for indoor localization. The two-thirst area of the WSN, increase in Battery lifetime and reduction in implementation cost has been achieved in the non-GPS devices. Traditional Received Signal strength is the stain of environmental noise due to the secular variations. In this paper, tracking of the mobile is measured from the RSSI using the mathematical expression of signal attenuation. A constant velocity model is proposed with the Random motion of the mobile node is considered for the position estimation using Extended Kalman Filtering (EKF) technique. The Extended kalman filter used to recover the noiseless RSS measurement and uncertainty measure of the estimates. The proposed RSSI with EKF algorithm results the better tracking estimation while comparing with the traditional RSSI.
Keywords: WSN, Localization, Kalman Filter, RSSI.
Scope of the Article: Wireless ad hoc & Sensor Networks