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Sparse recovery formulation for secure distance-based localization in the presence of cheating anchors

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Abstract

Secure localization in the presence of cheating anchors is a critical issue in wireless sensor networks, where compromised anchors attempt to falsify the measured distances in the location references. In this study, such misbehaviors of unfaithful anchors are modeled as unknown perturbations to the measured distances. By exploiting the sparsity of malicious anchors, the secure localization problem is formulated as a sparse signal recovery problem whose objective is to concurrently locate the sensors and identify the malicious anchors. Under the above formulation, the upper bound for the number of tolerable malicious anchors is obtained and the localization error bound is also provided. A gradient projection algorithm with variable step size is proposed to solve the sparse recovery problem. To further improve the localization accuracy, another modified gradient projection algorithm is presented. Simulation results show that the proposed algorithms can identify the unfaithful anchors with high probability and achieve good localization accuracy.

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References

  1. Fang, S.-H., Chuang, C.-C., & Wang, C. (2012). Attack-resistant wireless localization using an inclusive disjunction model. IEEE Transactions on Communications, 60(5), 1209–1214.

    Article  Google Scholar 

  2. Li, X., Chen, Y., Yang, J., & Zheng, X. (2011). Achieving robust wireless localization resilient to signal strength attacks. Wireless Networks, 18(1), 45–58.

    Article  Google Scholar 

  3. Chen, H., Lou, W., Wang, Z., Wu, J., Wang, Z., & Xia, A. (2015). Securing DV-Hop localization against wormhole attacks in wireless sensor networks. Pervasive and Mobile Computing, 16, 22–35.

    Article  Google Scholar 

  4. Sarigiannidis, P., Karapistoli, E., & Economides, A. A. (2015). Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information. Expert Systems with Applications, 42(21), 7560–7572.

    Article  Google Scholar 

  5. Boustani, A., Alamatsaz, N., Jadliwala, M., & Namboodiri, V. (2014). LocJam: A novel jamming-based approach to secure localization in wireless networks. In Consumer Communications and Networking Conference (CCNC), 2014 IEEE 11th (pp. 336–344). doi:10.1109/CCNC.2014.6866592.

  6. Zeng, Y., Cao, J., Hong, J., Zhang, S., & Xie, L. (2013). Secure localization and location verification in wireless sensor networks: A survey. Journal of Supercomputing, 64(3), 685–701.

    Article  Google Scholar 

  7. Milocco, R. H., & Boumerdassi, S. (2015). An efficient adaptive method for estimating the distance between mobile sensors. Wireless Networks, 21(8), 2519–2529.

    Article  Google Scholar 

  8. Halder, S., & Ghosal, A. (2016). A survey on mobile anchor assisted localization techniques in wireless sensor networks. Wireless Networks, 22(7), 2317–2336.

    Article  Google Scholar 

  9. Park, M.-H., Nam, K.-G., Kim, J. S., Yum, D. H., & Lee, P. J. (2013). Secure and lightweight localization method for wireless sensor networks. IEICE Transactions on Information and Systems, E, 96D(3), 723–726.

    Article  Google Scholar 

  10. Liu, D., Ning, P., Liu, A., Wang, C., & Du, W. K. (2008). Attack-resistant location estimation in wireless sensor networks. ACM Transactions on Information and System Security, 11(4), 22:1–22:39.

    Article  Google Scholar 

  11. Liu, D., Ning, P., & Du, W. (2005). Detecting malicious beacon nodes for secure location discovery in wireless sensor networks. In 25th IEEE International conference on distributed computing systems, 2005. ICDCS 2005. Proceedings, pp. 609–619. doi:10.1109/ICDCS.2005.21.

  12. Chen, H., Lou, W., & Wang, Z. (2010). A novel secure localization approach in wireless sensor networks. Eurasip Journal on Wireless Communications and Networking, 2010, 981280.

    Article  Google Scholar 

  13. Han, G., Jiang, J., Shu, L., Guizani, M., & Nishio, S. (2013). A two-step secure localization for wireless sensor networks. Computer Journal, 56(10), 1154–1166.

    Article  Google Scholar 

  14. Zhu, W. T., Xiang, Y., Zhou, J., Deng, R. H., & Bao, F. (2011). Secure localization with attack detection in wireless sensor networks. International Journal of Information Security, 10(3), 155–171.

    Article  Google Scholar 

  15. Garg, R., Varna, A. L., & Wu, M. (2012). An efficient gradient descent approach to secure localization in resource constrained wireless sensor networks. IEEE Transactions on Information Forensics and Security, 7(2), 717–730.

    Article  Google Scholar 

  16. Li, Z., Trappe, W., Zhang, Y., & Nath, B. (2005). Robust statistical methods for securing wireless localization in sensor networks. In Fourth International Symposium on Information Processing in Sensor Networks, 2005. IPSN 2005 (pp. 91–98). doi:10.1109/IPSN.2005.1440903.

  17. Misra, S., Xue, G., & Bhardwaj, S. (2009). Secure and robust localization in a wireless ad hoc environment. IEEE Transactions on Vehicular Technology, 58(3), 1480–1489.

    Article  Google Scholar 

  18. Wang, D., & Yang, L. (2012). Cooperative robust localization against malicious anchors based on semi-definite programming. In Military Communications Conference, 2012—MILCOM 2012 (pp. 1–6). doi:10.1109/MILCOM.2012.6415656.

  19. Elad, M. (2010). Sparse and redundant representations: From theory to applications in signal and image processing (1st ed.). Incorporated: Springer.

    Book  MATH  Google Scholar 

  20. Jadliwala, M., Zhong, S., Upadhyaya, S., Qiao, C., & Hubaux, J.-P. (2010). Secure distance-based localization in the presence of cheating beacon nodes. IEEE Transactions on Mobile Computing, 9(6), 810–823.

    Article  Google Scholar 

  21. Merkel, S., Mostaghim, S., & Schmeck, H. (2014). Hop count based distance estimation in mobile ad hoc networks—Challenges and consequences. Ad Hoc Networks, 15, 39–52.

    Article  Google Scholar 

  22. Gurung, S., Hossain, A. K. M. M., & Kanchanasut, K. (2014). A hop-count based positioning algorithm for wireless ad-hoc networks. Wireless Networks, 20(6), 1431–1444.

    Article  Google Scholar 

  23. Chen, H., Liu, B., Huang, P., Liang, J., & Gu, Y. (2012). Mobility-assisted node localization based on toa measurements without time synchronization in wireless sensor networks. Mobile Networks and Applications, 17(1), 90–99.

    Article  Google Scholar 

  24. Zhang, W., Xu, H., & Yang, L. (2011). A novel \({\mathbf{\ell }}\)1-regularized LS formulation for target localization and malicious anchor identification. In Military Communications Conference, 201—MILCOM 201, pp. 766–771. doi:10.1109/MILCOM.2011.6127769.

  25. Capkun, S., & Hubaux, J.-P. (2006). Secure positioning in wireless networks. IEEE Journal on Selected Areas in Communications, 24(2), 221–232.

    Article  Google Scholar 

  26. Baraniuk, R. G. (2007). Compressive sensing [lecture notes]. IEEE Signal Processing Magazine, 24(4), 118–121.

    Article  Google Scholar 

  27. Bertsekas, D. P. (1999). Nonlinear programming (2nd ed.). Cambridge, MA: Athena Scientific.

    MATH  Google Scholar 

  28. Figueiredo, M. A. T., Nowak, R. D., & Wright, S. J. (2007). Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing, 1(4), 586–597.

    Article  Google Scholar 

  29. Steven, M. K. (1993). Fundamentals of statistical processing, volume i: Estimation theory (1 edition.). Prentice Hall.

  30. Chan, F. K. W., So, H. C., Zheng, J., & Lui, K. W. K. (2008). Best linear unbiased estimator approach for time-of-arrival based localisation. IET Signal Processing, 2(2), 156–162.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous reviewers and the associate editors for their valuable suggestions. This work has been supported by the National Natural Science Foundation of China under Grant No. 61371135.

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Authors and Affiliations

  1. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, 100191, China

    Qiang Zhang, Jiangwen Wan, Dandan Wang & Donghao Wang

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  1. Qiang Zhang
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  2. Jiangwen Wan
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  3. Dandan Wang
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  4. Donghao Wang
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Correspondence to Jiangwen Wan.

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Zhang, Q., Wan, J., Wang, D. et al. Sparse recovery formulation for secure distance-based localization in the presence of cheating anchors. Wireless Netw 24, 2657–2668 (2018). https://doi.org/10.1007/s11276-017-1490-5

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