Browse > Article
http://dx.doi.org/10.4218/etrij.2017-0116

Multiregional secure localization using compressive sensing in wireless sensor networks  

Liu, Chang (College of Computer Science and Electronic Engineering, Hunan University)
Yao, Xiangju (College of Computer Science and Electronic Engineering, Hunan University)
Luo, Juan (College of Computer Science and Electronic Engineering, Hunan University)
Publication Information
ETRI Journal / v.41, no.6, 2019 , pp. 739-749 More about this Journal
Abstract
Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive-sensing-based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource-constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.
Keywords
compressive sensing; indoor localization; received signal strength; secure localization; wireless networks;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Y. Li et al., Qiloc: A qi wireless charging based system for robust user-initiated indoor location services, in IEEE Int. Conf. Sens., Commun., Netw., Seattle, WA, USA, June 2014, pp. 184-185.
2 L. Tao, Z. Li, and L. Wu, Outlet: outsourcing wearable computing to the ambient mobile computing edge, IEEE Access 6 (2018), 18408-18419.   DOI
3 L. Yin, J. Luo, and H. Luo, Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing, IEEE Trans. Ind. Inform. 14 (2018), 4712-4721.   DOI
4 R. Garg, A. Varna, and M. Wu, An efficient gradient descent approach to secure localization in resource constrained wireless sensor networks, IEEE Trans. Inform. Forensics Security 7 (2012), 717-730.   DOI
5 S. Go and J. Chong, Improved TOA-based localization method with BS selection scheme for wireless sensor networks, ETRI J. 37 (2015), 707-716.   DOI
6 D. E. Chaitanya and G. S. Rao, Unknown radio source localization based on a modified closed form solution using TDOA measurement technique, Procedia Comput. Sci. 87 (2016), 184-189.   DOI
7 N. A. Khanbashi et al., Measurements and analysis of fingerprinting structures for WLAN localization systems, ETRI J. 38 (2016), 634-644.   DOI
8 S. Sorour et al., Joint indoor localization and radio map construction with limited deployment load, IEEE Trans. Mobile Comput. 14 (2015), 1031-1043.   DOI
9 C. Feng, S. Valaee, and Z. Tan, Multiple target localization using compressive sensing, in Global Telecommun. Conf., Honolulu, HI, USA, 2009, pp. 1-6.
10 C. Feng et al., Compressive sensing based positioning using RSS of WLAN access points, in IEEE INFOCOM, San Diego, CA, USA, Mar. 2010, pp. 1-9.
11 M. Jadliwala et al., Secure distance-based localization in the presence of cheating beacon nodes, IEEE Trans. Mobile Comput. 9 (2010), 810-823.   DOI
12 E. J. Candes and M. B. Wakin, An introduction to compressive sensing, Signal Process. Mag. 25 (2008), 21-30.   DOI
13 L. Liu et al., Adaptive source location estimation based on compressed sensing in wireless sensor networks, Int. J. Distrib. Sens. Netw. 8 (2012), 141-149.
14 Y. Mo et al., A spatial division clustering method and low dimensional feature extraction technique based indoor positioning system, Sens. 14 (2014), 1850-1876.   DOI
15 J. Luo et al., Secure indoor localization based on extracting trusted fingerprint, Sens. 18 (2018), 469-492.   DOI
16 Y. Chen et al., Indoor localization using FM signals, IEEE Trans. Mobile Comput. 12 (2013), 1502-1517.   DOI
17 T. Higuchi et al., Mobile node localization focusing on stop-and-go behavior of indoor pedestrians, IEEE Trans. Mobile Comput. 13 (2014), 1564-1578.   DOI
18 R. Baraniuk et al., A simple proof of the restricted isometry property for random matrices, Constructive Approximation 28 (2008), 253-263.   DOI
19 F. Anjum, S. Pandey, and P. Agrawal, Secure localization in sensor networks using transmission range variation, in IEEE Int. Conf. Mobile Adhoc Sens. Syst., Washington, DC, USA, Nov. 2005, pp. 9-203.
20 B. Sklar, Digital communications, Prentice-Hall, Upper Saddle River, NJ, USA, 2001, p. 190.
21 D. Milioris et al., Low-dimensional signal-strength fingerprint-based positioning in wireless LANs, Ad hoc Netw. 12 (2014), 100-114.   DOI
22 J. Romberg, Imaging via compressive sampling, IEEE Signal Process. Mag. 25 (2008), 14-20.   DOI
23 B. Zhang et al., Sparse target counting and localization in sensor networks based on compressive sensing, in IEEE INFOCOM, Shanghai, China, Apr. 2011, pp. 2255-2263.
24 E. Cande and J. Romberg, Sparsity and incoherence in compressive sampling, Inverse Prob. 23 (2006), 969-985.   DOI
25 C. Feng, S. Valaee, and Z. Tan, Multiple target localization using compressive sensing, in Global Telecommun. Conf., Honolulu, HI, USA, 2009, pp. 1-6.
26 J. Jiang et al., Secure localization in wireless sensor networks: A survey, J. Commun. 6 (2011), 460-470.