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Localizing Jammer in an Indoor Environment by Estimating Signal Strength and Kalman Filter

¹ School of Engineering and Computer Science, Oakland University, Rochester Hills, USA
² School of Engineering and Computer Science, Oakland University, Rochester Hills, USA

Wireless Engineering and Technology·Volume 09 (2018)·Pages 20–33·Published 9 April 2018·DOI10.4236/wet.2018.92003

Abstract

Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.

KeywordsJammer DetectingKalman Filter (KF)Linear Prediction (LP)Centroid Localization (CL)Jammer Received Signal Strength (JRSS)Multipath SignalIndoor Location

Kharrufa, H., Al-Kashoash, H. and Kemp, A.H. (2018) A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications. IEEE SENSORS Journal, 18, 2520-2530. https://doi.org/10.1109/JSEN.2018.2794762
Yang, X., Chen, P.P., Gao, S.W. and Niu, Q. (2018) CSI-Based Low-Duty-Cycle Wireless Multimedia Sensor Network for Security Monitoring. Electronics Letters, IET Journals & Magazines, 54, 323-324. https://doi.org/10.1049/el.2017.2515
Zhu, J., Zou, Y.L. and Zheng, B.Y. (2017) Physical-Layer Security and Reliability Challenges for Industrial Wireless Sensor Networks. IEEE Access, IEEE Journals & Magazines, 5, 5313-5320.
Wei, X.L., Wang, Q.P., Wang, T.X. and Fan, J.H. (2017) Jammer Localization in Multi-Hop Wireless Network: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 19, 765-799.
Das, R., Bal, S., Das, S., Sarkar, M.K., Majumder, D., Chakraborty, A. and Majumder, K. (2016) Performance Analysis of Various Attacks under AODV in WSN & MANET Using OPNET 14.5. IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Conferences, 1-9.
Mpitziopoulos, A., Gavalas, D., Konstantopoulos, C. and Pantziou, G. (2009) A Survey on Jamming Attacks and Countermeasures in WSNs. IEEE Communications Surveys & Tutorials, 11, 42-56. https://doi.org/10.1109/SURV.2009.090404
Alagil, A., Alotaibi, M. and Liu, Y. (2016) Randomized Positioning DSSS for Anti-Jamming Wireless Communications. 2016 International Conference on Computing, Networking and Communications (ICNC), IEEE Conferences, 1-6.
Strasser, M., Pöpper, C., Capkun, S. and Cagalj, M. (2008) Jamming-Resistant Key Establishment Using Uncoordinated Frequency Hopping. IEEE Symposium on Security and Privacy (sp 2008), IEEE Conferences, 64-78.
Khan, M.M., Abbasi, Q.H., Alomainy, A. and Hao, Y. (2011) Study of Line of Sight (LOS) and None Line of Sight (NLOS) Ultra Wideband Off-Body Radio Propagation for Body Centric Wireless Communications in Indoor. Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP), 110-114.
Bulusu, N., Heidemann, J. and Estrin, D. (2000) GPS-Less Low Cost Outdoor Localization for very Small Devices. IEEE Personal Communications, IEEE Journals & Magazines, 7, 28-34. https://doi.org/10.1109/98.878533
Dakhlallah, T.K., Zohdy, M.A. and Salim, O.M. (2011) Application of Sensor Similarity, Complementarity and Type-2 Fuzzy Logic to a Dynamic Security Monitoring System. IEEE Conferences, Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON), 25-31. https://doi.org/10.1109/NAECON.2011.6183072

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Localizing Jammer in an Indoor Environment by Estimating Signal Strength and Kalman Filter

Abstract

References (26)