Jammer Localization Through Smart Estimation of Jammer’s Transmission Power

Wireless Sensors Network (WSNs) are susceptible to jamming attacks due to the shared nature and open access medium. Jammer disrupts the wireless channel by injecting its signal into the legitimate traffic which causes it to increase the amount of noise at the receiver. In order to improve the localization accuracy, this paper proposed Distance Ratio (DR) based on Signal to Noise Ratio (SNR). The primary process of the Distance to Signal Noise Ratio (DSNR) algorithm consists of four steps: capturing jamming Signal Strength (JRSS) and computing the received power between boundary node and its neighbor, compute DR, estimating jammer's transmission power and its location, and minimizing localization error. Finally, extensive simulations are conducted to evaluate the performance, effectiveness, and the robustness of the proposed method compared to similar localization algorithms.

[1]  Srikanth V. Krishnamurthy,et al.  Lightweight Jammer Localization in Wireless Networks: System Design and Implementation , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[2]  Mohamed A. Zohdy,et al.  Localizing Jammer in an Indoor Environment by Estimating Signal Strength and Kalman Filter , 2018 .

[3]  Theodore S. Rappaport,et al.  Probabilistic Omnidirectional Path Loss Models for Millimeter-Wave Outdoor Communications , 2015, IEEE Wireless Communications Letters.

[4]  Wenyuan Xu,et al.  Jamming sensor networks: attack and defense strategies , 2006, IEEE Network.

[5]  Seung-Jun Yu,et al.  Wireless Communication , 1916, Nature.

[6]  Charalampos Konstantopoulos,et al.  A survey on jamming attacks and countermeasures in WSNs , 2009, IEEE Communications Surveys & Tutorials.

[7]  Xianglin Wei,et al.  A step further of PDR-based jammer localization through dynamic power adaptation , 2015 .

[8]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[9]  Han Zhou,et al.  Anti-Jamming Path Planning for Unmanned Aerial Vehicles with Imperfect Jammer Information , 2018, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[10]  Xianglin Wei,et al.  GSA-Based Jammer Localization in Multi-Hop Wireless Network , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).

[11]  Theodore S. Rappaport,et al.  Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications , 2016, IEEE Transactions on Vehicular Technology.

[12]  Mohamed Zohdy,et al.  Tracking a Jammer in Wireless Sensor Networks and Selecting Boundary Nodes by Estimating Signal-to-Noise Ratios and Using an Extended Kalman Filter , 2018, J. Sens. Actuator Networks.

[13]  Wenyuan Xu,et al.  Determining the position of a jammer using a virtual-force iterative approach , 2011, Wirel. Networks.

[14]  Eva Rajo-Iglesias,et al.  Wireless Corner [Introduction to "Path-Loss Model Including LOS-NLOS Transition Regions for Indoor Corridors at 5 GHz"] , 2013 .

[15]  F. Golatowski,et al.  Weighted Centroid Localization in Zigbee-based Sensor Networks , 2007, 2007 IEEE International Symposium on Intelligent Signal Processing.

[16]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[17]  M. V. Kulikova,et al.  Moore‐Penrose‐pseudo‐inverse‐based Kalman‐like filtering methods for estimation of stiff continuous‐discrete stochastic systems with ill‐conditioned measurements , 2018, IET Control Theory & Applications.

[18]  Michael Cheffena,et al.  Empirical Path Loss Models for Wireless Sensor Network Deployment in Snowy Environments , 2017, IEEE Antennas and Wireless Propagation Letters.

[19]  Wenyuan Xu,et al.  Channel surfing and spatial retreats: defenses against wireless denial of service , 2004, WiSe '04.

[20]  Upena Dalal,et al.  Wireless Communication , 2010 .

[21]  Xiongwen Zhao,et al.  Path-Loss Model Including LOS-NLOS Transition Regions for Indoor Corridors at 5 GHz [Wireless Corner] , 2013, IEEE Antennas and Propagation Magazine.

[22]  John E. Mitchell,et al.  Effective-SNR estimation for wireless sensor network using Kalman filter , 2013, Ad Hoc Networks.