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J. Sens. Actuator Netw. 2018, 7(4), 48; https://doi.org/10.3390/jsan7040048

Tracking a Jammer in Wireless Sensor Networks and Selecting Boundary Nodes by Estimating Signal-to-Noise Ratios and Using an Extended Kalman Filter

Department of Electrical and Computer Engineering, Oakland University, Rochester, NY 48309, USA
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Received: 8 October 2018 / Revised: 10 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract

This work investigates boundary node selection when tracking a jammer. A technique to choose nodes to track jammers by estimating signal-to-noise Ratio (SNR), jammer-to-noise ratio (JNR), and jammer received signal strength (JRSS) are introduced in this paper. We proposed a boundary node selection threshold (BNST) algorithm. Every node can become a boundary node by comparing the SNR threshold, the average SNR estimated at the boundary node, and the received BNST value. The maximum sensing range, transmission range, and JRSS are the main parts of this algorithm. The algorithm is divided into three steps. In the first step, the maximum distance between two jammed nodes is found. Next, the maximum distance between the jammed node and its unjammed neighbors is computed. Finally, maximum BNST value is estimated. The extended Kalman filter (EKF) is utilized in this work to track the jammer and estimate its position in a different time step using selected boundary nodes. The experiment validates the benefits of selecting a boundary when tracking a jammer. View Full-Text
Keywords: WSNs; Jammer Received Signal Strength (JRSS); Boundary Nodes Selection Threshold (BNST); Extended Kalman Filter (EKF) WSNs; Jammer Received Signal Strength (JRSS); Boundary Nodes Selection Threshold (BNST); Extended Kalman Filter (EKF)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Aldosari, W.; Zohdy, M. Tracking a Jammer in Wireless Sensor Networks and Selecting Boundary Nodes by Estimating Signal-to-Noise Ratios and Using an Extended Kalman Filter. J. Sens. Actuator Netw. 2018, 7, 48.

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