The rapid progress of wireless communication and the availability of
many small-sized, light-weighted and low-cost communication and computing devices
nowadays have greatly impacted the development of wireless sensor network.
Localization using sensor network has attracted much attention for its
comparable low-cost and potential use with mon- itoring and
targeting purposes in real and hostile application scenarios. Currently, there
are many available approaches to locating persons/things based on global
positioning system (GPS) and radio-frequency identification (RFID) technologies.
However, in some application scenario, e.g., disaster rescue application, such
localization devices may be damaged and may not provide the location
information of the survivors. The main goal of this paper is to design and develop a
robust localization technique for human existence detection in case of
disasters such as earthquake or fire. In this paper, we propose a 3-D
localization technique based on the hop-count data collected from sensor
anchors to estimate the location of the activated sensor mote in 3-D coordination.
Our algorithm incorporates two salient features, cubic-based output
and event-triggering mechanism, to guarantee both improved accuracy and power efficiency. Both simulation and experimental results indicate
that the proposed algorithm can improve the localization precision of the human
existence and work well in real environment.
Cite this paper
H. Shwe, C. Wang, P. Chong and A. Kumar, "Robust Cubic-Based 3-D Localization for Wireless Sensor Networks," Wireless Sensor Network, Vol. 5 No. 9, 2013, pp. 169-179. doi: 10.4236/wsn.2013.59020.
 F. Akyildiz, et al., “Wireless Sensor Networks: A Survey,” Computer Networks, Vol. 38, No. 4, 2002, pp. 393-422. doi:10.1016/S1389-1286(01)00302-4
 H. Karl and A. Willig, “A Short Survey of Wireless Sensor Networks,” Technical Report TKN-03-018, Telecommunication Networks Group, Technical University, Berlin, 2003.
 C. K. Seow and S. Y. Tan, “Non Line of Sight Localization in Multipath Environment,” IEEE Transactions on Mobile Computing, Vol. 7, No. 5, 2008, pp. 647-660.
 L. Hu and D. Evans, “Localization for Mobile Sensor Networks,” Proceedings of ACM MobiCom’04, Vol. 1, Philadelphia, 26 September-1 October 2004, pp. 45-57.
 A. Baggio and K. Langendoen, “Monte-Carlo Localization for Mobile Wireless Sensor Networks,” Ad Hoc Networks, Vol. 6, No. 5, 2008, pp. 718-733.
 A. Ward, A. Jones and A. Hopper, “A New Location Technique for the Active Office,” IEEE Personal Communications, Vol. 4, No. 5, 1997, pp. 42-47.
 N. B. Priyantha, A. Chakraborty and H. Balakrishnan, “The Cricket Location-Support System,” Proceedings of ACM MobiCom’00, Vol. 1, Boston, 2000, pp. 32-43.
 Y. Liu, J. P. Xing and R. Wang, “3D-OSSDL: Three Dimensional Optimum Space Step Distance Localization Scheme in Stereo Wireless Sensor Networks,” Advances in Intelligent and Soft Computing, Vol. 112, 2012, pp. 17-25. doi:10.1007/978-3-642-25194-8_3
 G. Mao, B. Fidan and B. D. O. Anderson, “Wireless Sensor Network Localization Techniques,” Computer Networks, Vol. 51, No. 10, 2007, pp. 2529-2553.
 D. Niculescu and N. Badri, “Ad-Hoc Positioning System (APS) Using AOA,” Proceedings of IEEE/ACM INFOCOM’03, Vol. 3, San Francisco, 30 March-3 April 2003, pp. 1734-1743.
 D. Niculescu and N. Badri, “DV Based Positioning in Ad Hoc Networks,” Journal of Telecommunication Systems, Vol. 22, No. 1-4, 2003, pp. 267-280.
 D. Fox, W. Burgard, F. Dellaert and S. Thrun, “Monte-Carlo Localization: Efficient Position Estimation for Mobile Robots,” Proceedings of the National Gonfemnce on Artificalntelligence, AAAI, 1999.
 A. Baggio and K. Langendoen, “Monte Carlo Localization for Mobile Wireless Sensor Networks,” Ad Hoc Networks, Vol. 6, No. 5, 2008, pp. 718-733.
 Crossbow Technology, “MICAz,” 2007.
 Crossbow Technology, “MPR-MIB Users Manual,” 2007.
 Crossbow Technology, “MTS/MDA Sensor Board Users Manual,” 2007.