Abstract
In wireless sensor networks, a critical system service is the localization service that determines the locations of geographically distributed sensor nodes. The raw data used by this service are the distance measurements between neighboring nodes and the position knowledge of anchor nodes. However, these raw data may contain outliers that strongly deviate from their true values, which include both the outlier distances and the outlier anchors. These outliers can severely degrade the accuracy of the localization service. Therefore, we need a robust localization algorithm that can reject these outliers. Previous studies in this field mainly focus on enhancing multilateration with outlier rejection ability, since multilateration is a primitive operation used by localization service. But patch merging, a powerful operation for increasing the percentage of localizable nodes in sparse networks, is almost neglected. We thus propose a robust patch merging operation that can reject outliers for both multilateration and patch merging. Based on this operation, we further propose a robust network localization algorithm called RobustLoc. This algorithm makes two major contributions. (1) RobustLoc can achieve a high percentage of localizable nodes in both dense and sparse networks. In contrast, previous methods based on robust multilateration almost always fail in sparse networks with average degrees between 5 and 7. Our experiments show that RobustLoc can localize about 90% of nodes in a sparse network with 5.5 degrees. (2) As far as we know, RobustLoc is the first to uncover the differences between outlier distances and outlier anchors. Our simulations show that RobustLoc can reject colluding outlier anchors reliably in both convex and concave networks.
- Eren, T., Goldenberg, D. K., Whiteley, W., Yang, Y. R., Morse, A. S., Anderson, B. D. O., and Belhumeur, P. N. 2004. Rigidity, computation, and randomization in network localization. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies.Google Scholar
- Foy, W. H. 1976. Position-location solutions by Taylor-series estimation. IEEE Trans. Aerospace Electron. Syst. 12, 2, 187--194.Google ScholarCross Ref
- Goldenberg, D. K., Bihler, P., Cao, M., Fang, J., Anderson, B. D. O., Morse, A. S., and Yang, Y. R. 2006. Localization in sparse networks using sweeps. In Proceedings of the Annual ACM International Conference on Mobile Computing and Networking. Google ScholarDigital Library
- He, T., Huang, C., Blum, B. M., Stankovic, J. A., and Abdelzaher, T. 2003. Range-free localization schemes for large scale sensor networks. In Proceedings of the Annual ACM International Conference on Mobile Computing and Networking. Google ScholarDigital Library
- Horn, B. K. P., Hilden, H., and Negahdaripour, S. 1988. Closed-form solution of absolute orientation using orthonormal matrices. J. Opti. Soc. Am. A, 4, 629.Google ScholarCross Ref
- Jian, L.-R., Yang, Z., and Liu, Y.-H. 2010. Beyond triangle inequality: Sifting noisy and outlier distance measurements for localization. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies. Google ScholarDigital Library
- Kannan, A. A., Fidan, B., and Mao, G.-Q. 2011. Robust distributed sensor network localization based on analysis of flip ambiguities. Wirel. Netw. 17, 5, 1157--1171. Google ScholarDigital Library
- Kiyavash, N. and Koushanfar, F. 2007. Anti-collusion position estimation in wireless sensor networks. In Proceedings of the IEEE Conference on Mobile, Ad Hoc and Sensor System.Google Scholar
- Kung, H. T., Lin, C.-K., Lin, T.-H., and Vlah, D. 2009. Localization with snap-inducing shaped residuals (SISR): Coping with errors in measurement. In Proceedings of the Annual ACM International Conference on Mobile Computing and Networking. Google ScholarDigital Library
- Li, M. and Liu, Y.-H. 2007. Rendered path: Range-free localization in anisotropic sensor networks with holes. In Proceedings of the Annual ACM International Conference on Mobile Computing and Networking. 51--62. Google ScholarDigital Library
- Li, Z., Trappe, W., Zhang, Y., and Nath, B. 2005. Robust statistical methods for securing wireless localization in sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks. 12. Google ScholarDigital Library
- Lim, H. and C., H. J. 2005. Localization for anisotropic sensor networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies. 138--149.Google Scholar
- Liu, D., Ning, P., and Du, W. K. 2005. Attack-resistant location estimation in sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks. Google ScholarDigital Library
- Mao, G.-Q. and Fidan, B. 2009. Localization Algorithms and Strategies for Wireless Sensor networks. IGI Global, Hershey, PA. Google ScholarDigital Library
- Moore, D., Leonard, J., Rus, D., and Teller, S. 2004. Robust distributed network localization with noisy range measurements. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems. Google ScholarDigital Library
- Newsome, J., Shi, E., Song, D., and Perrig, A. 2004. The sybil attack in sensor networks: Analysis & defenses. In Proceedings of the International Conference on Information Processing in Sensor Networks. Google ScholarDigital Library
- Niculescu, D. and Nath, B. 2003. DV based positioning in ad hoc networks. Kluwer J. Telecommun. Syst.Google ScholarDigital Library
- Priyantha, N. B., Balakrishnan, H., Demaine, E., and Teller, S. 2003. Anchor-free distributed localization in sensor networks. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems. Google ScholarDigital Library
- Savvides, A., Park, H., and Srivastava, M. B. 2003. The n-hop multilateration primitive for node localization problems. J. Mobile Netw. Appl. 8, 4, 443--451. Google ScholarDigital Library
- Shang, Y. and Ruml, W. 2004. Improved MDS-based localization. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies. 2640--2651.Google Scholar
- Wang, C., Liu, A., and Ning, P. 2007. Cluster-based minimum mean square estimation for secure and resilient localization in wireless sensor networks. In Proceedings of the International Conference on Wireless Algorithms Systems and Applications. Google ScholarDigital Library
- Wang, C. and Xiao, L. 2006. Locating sensors in concave areas. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies. 1--12.Google Scholar
- Wang, X.-P., Liu, Y.-H., Yang, Z., Liu, J.-L., and Luo, J. 2010. ETOC: Obtaining robustness in component-based localization. In Proceedings of the Annual International Conference on Network Protocols. Google ScholarDigital Library
- Wang, X.-P., Luo, J., Li, S.-S., Dong, D.-Z., and Cheng, W.-F. 2008. Component based localization in sparse wireless ad hoc and sensor networks. In Proceedings of the Annual International Conference on Network Protocols.Google Scholar
- Whitehouse, K., Karlof, C., Woo, A., Jiang, F., and Culler, D. 2005. The effects of ranging noise on multihop localization: an empirical study. In Proceedings of the International Conference on Information Processing in Sensor Networks. Google ScholarDigital Library
- Xiao, B., Chen, H., and Zhou, S. 2008. Distributed localization using a moving beacon in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 19, 5, 587--600. Google ScholarDigital Library
- Xiao, B., Chen, L., Xiao, Q.-J., and Li, M.-L. 2010a. Reliable anchor-based sensor localization in irregular areas. IEEE Trans. Mobile Comput. 9, 60--72. Google ScholarDigital Library
- Xiao, Q.-J., Xiao, B., Cao, J.-N., and Wang, J.-P. 2010b. Multihop range-free localization in anisotropic wireless sensor networks: A pattern-driven scheme. IEEE Trans. on Mobile Comput. 9, 1592--1607. Google ScholarDigital Library
Index Terms
- Robust localization against outliers in wireless sensor networks
Recommendations
A Comparative Analysis of Intelligent Algorithms for Localization in Wireless Sensor Networks
In a smart and decision making environment the location information of the sensors and devices under monitoring and control, is very much important, otherwise the sensed data becomes meaningless. This paper proposes three intelligent algorithms namely ...
Distance outlier detection with a mobile beacon in wireless sensor networks localisation
Range-based localisation algorithms rely on distance measurements between nodes. If outlier distances exist, they make the estimated positions deviate from the true positions. Thus, it is important to detect and remove outlier distance measurements to ...
A survey on mobility-assisted localization techniques in wireless sensor networks
Identifying locations of sensor nodes in wireless sensor networks (WSNs) is significant for both network operations and most application level tasks. Although geographical positioning system (GPS) based localization schemes are used for determining node ...
Comments