Tracking the Mobile Jammer in Wireless Sensor Networks Using Extended Kalman Filter

Wireless Sensor Networks (WSNs) are susceptible to jamming attacks due to the shared wireless medium. The jammer can disrupt any specific or entire radio frequency based on its function and strategies. Locating the jammer location is very important against the jamming in the wireless network and restore the communication channel. To support the existing anti-jamming techniques, we proposed an algorithm based on the Extended Kalman filter (EKF) and power received to track the jammer. Detecting jammer location is the first step taking to defend such attacks. Besides, estimating jammer location supports a wide range of defense. Range-based jammer localization technique based on the received power is used in this work to detect the external malicious node location by designed the position, velocity, and acceleration approach of Extended Kalman filter. An extensive simulation conducted to evaluate the performance of EKF compares to the Virtual Force Iteration Localization (VFIL), Weighted Centroid Localization (WCL), and Centroid Localization algorithms (CL). The EKF proves to be of high efficiency in comparison to VFIL, WCL, and CL.

[1]  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.

[2]  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).

[3]  Sencun Zhu,et al.  An Algorithm for Jammer Localization in Wireless Sensor Networks , 2012, 2012 IEEE 26th International Conference on Advanced Information Networking and Applications.

[4]  Hsiao-Chun Wu,et al.  Physical layer security in wireless networks: a tutorial , 2011, IEEE Wireless Communications.

[5]  Hacène Fouchal,et al.  Towards efficient deployment of wireless sensor networks , 2016, Secur. Commun. Networks.

[6]  Zhi Xue,et al.  Tracking the Mobile Jammer Continuously in Time by Using Moving Vector , 2017, 2017 10th International Symposium on Computational Intelligence and Design (ISCID).

[7]  Xianglin Wei,et al.  Jammer Localization in Multi-Hop Wireless Network: A Comprehensive Survey , 2017, IEEE Communications Surveys & Tutorials.

[8]  Yi Zheng,et al.  The Study of the Weighted Centroid Localization Algorithm Based on RSSI , 2014, 2014 International Conference on Wireless Communication and Sensor Network.

[9]  Ping Li,et al.  M-cluster and X-ray: Two methods for multi-jammer localization in wireless sensor networks , 2014, Integr. Comput. Aided Eng..

[10]  Yimin Zhang,et al.  Anti-Jamming GPS Receiver With Reduced Phase Distortions , 2012, IEEE Signal Processing Letters.

[11]  Mohamed A. Zohdy,et al.  Extended Kalman Filtering and Pathloss modeling for Shadow Power Parameter Estimation in Mobile Wireless Communications , 2017 .

[12]  Srikanth V. Krishnamurthy,et al.  Denial of Service Attacks in Wireless Networks: The Case of Jammers , 2011, IEEE Communications Surveys & Tutorials.

[13]  Xiaodong Wang,et al.  Catch the Jammer in Wireless Sensor Network , 2011, 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications.

[14]  Tongtong Li,et al.  Anti-Jamming Message-Driven Frequency Hopping—Part II: Capacity Analysis Under Disguised Jamming , 2013, IEEE Transactions on Wireless Communications.

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

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

[17]  François Gagnon,et al.  RSSI-based indoor tracking using the extended Kalman filter and circularly polarized antennas , 2014, 2014 11th Workshop on Positioning, Navigation and Communication (WPNC).