Masoomeh Rudafshani
ABSTRACT
A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -- they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.
- J. Bachrach and C. Taylor. Handbook of Sensor Networks, chapter Localization in Sensor Networks. Wiley, 2005.Google Scholar
- P. Bahl and V. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In Proceedings of INFOCOM, pages 775--784, 2000.Google ScholarCross Ref
- P. Bergamo and G. Mazzimi. Localization in sensor networks with fading and mobility. In The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pages 750--754, 2002.Google ScholarCross Ref
- U. Bischoff, M. Strohbach, M. Hazas, and G. Kortuem. Constraint-based distance estimation in ad-hoc wireless sensor networks. In Proceedings of the Third European Workshop on Wireless Sensor Networks (EWSN), pages 54--68, 2006. Google ScholarDigital Library
- T. Camp, J. Boleng, and V. Davies. A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, 2(5):483--502, 2002.Google Scholar
- S. Datta, C. Klinowski, M. Rudafshani, and S. Khaleque. Distributed localization in static and mobile sensor networks. In IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 69--76, 2006. Google ScholarDigital Library
- B. Dil, S. Dulman, and P.J.M. Havinga. Range-based localization in mobile sensor networks. In Proccedings of the Third European Workshop on Wireless Sensor Networks (EWSN), pages 164--179, 2006. Google ScholarDigital Library
- A. Doucet, N. Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.Google ScholarCross Ref
- A. Doucet, S. Godsill, and C. Andrieu. On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10(3):197--208, 2000. Google ScholarDigital Library
- D. Fox, W. Burgard, F. Dellaert, and S. Thrun. Monte carlo localization: Efficient position estimation for mobile robots. In AAAI 1999, pages 343--349, 1999. Google ScholarDigital Library
- S. Guha, R. Murty, and E. Sirer. Sextant: a unified node and event localization framework using non-convex constraints. In ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pages 205--216, 2005. Google ScholarDigital Library
- T. He, C. Huang, B.M. Blum, J.A. Stankovic, and T. Abdelzaher. Range-free localization schemes for large scale sensor networks. In Proceedings of the 9th annual international conference on Mobile computing and networking (MobiCom), pages 81--95, 2003. Google ScholarDigital Library
- B. Hofmann-WeUenhof, H. Lichtenegger, and J. Collins. Global Positioning System, Theory and Practice. Springer-Verlag, 1993.Google Scholar
- L. Hu and D. Evans. Localization for mobile sensor networks. In Proceedings of the 10th annual international conference on Mobile computing and networking (MobiCom), pages 45--57, 2004. Google ScholarDigital Library
- P. Juang, H. Oki, Y. Wang, M. Martonosi, L.S. Peh, and D. Rubenstein. Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with zebranet. In Proceedings of the 10th international conference on Architectural support for programming languages and operating systems (ASPLOS), pages 96--107, 2002. Google ScholarDigital Library
- B. Karp and H.T. Kung. GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of the Sixth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom), pages 243--254, 2000. Google ScholarDigital Library
- B. Kusý, A. Lédeczi, M. Maróti, and L. Meertens. Node density independent localization. In Proceedings of the fifth international conference on Information processing in sensor networks (IPSN), pages 441--448, 2006. Google ScholarDigital Library
- M. Maróti, P. Völgyesi, S. Dóra, B. Kusý, A. Nádas, A. Lédeczi ,G. Balogh, and K. Molnár.Radio interferometric geolocation. In Proceedings of the 3rd international conference on Embedded networked sensor systems (SenSys), pages 1--12, 2005. Google ScholarDigital Library
- W. Mendenhall, D. Wackerly, and R. Scheaffer. Mathematical Statistics with Applications. PWS-Kent Publishing Company, 1989.Google Scholar
- R. Nagpal, H. Shrobe, and J. Bachrach. Organizing a global coordinate system from local information on an ad hoc sensor network. In Second International Workshop on Information Processing in Sensor Networks (IPSN), pages 333--348, 2003. Google ScholarDigital Library
- D. Niculescu and B. Nath. Ad hoc positioning system (APS). In Proceedings of GLOBECOM, pages 2926--293, 2001.Google ScholarCross Ref
- D. Niculescu and B. Nath. Ad hoc positioning system (APS)using AoA. In Proceedings of INFOCOM, pages 1734--1743, 2003.Google ScholarCross Ref
- N. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In Proceedings of the Sixth Annual ACM International Conference on Mobile Computing and Networking (MOBICOM), pages 32--43, 2000. Google ScholarDigital Library
- A. Savvides, C.-C. Han, and M.B. Strivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th annual international conference on Mobile computing and networking (MobiCom), pages 166--179, 2001. Google ScholarDigital Library
- A. Savvides, M. Srivastava, L. Girod, and D. Estrin. Wireless sensor networks, chapter Localization in sensor networks, pages 327--349. Kluwer Academic Publishers, Norwell, MA, USA, 2004. Google ScholarDigital Library
- M. Tanner. Tools for Statistical Inference, Theory and Practice, 3rd edition. Springer-Verlag, 1996.Google Scholar
- S. Tilak, V. Kolar, N.B. Abu-Ghazaleh, and K.D. Kang.Dynamic localization control for mobile sensor networks. In 24th IEEE International Performance, Computing,and Communications Conference (IPCCC), pages 587--592, 2005.Google Scholar
- M. Torrent-Moreno, F. Schmidt-Eisenlohr, H. Fussler, and H. Hartenstein. Effects of a realistic channel model on packet forwarding in vehicular ad hoc networks. In Wireless Communications and Networking Conference (WCNC), volume 1, pages 385--391, 2006.Google ScholarCross Ref
- A. Ward, A. Jones, and A. Hopper. A new location technique for the active office. IEEE Personal Communications, 4(5):42--47, 1997.Google ScholarCross Ref
- J. Yoon, M. Liu, and B. Noble. Sound mobility models.In MobiCom '03:Proceedings of the 9th annual international conference on Mobile computing and networking, pages 205--216, 2003. Google ScholarDigital Library
Index Terms
- Localization in wireless sensor networks
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