Abstract
Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article introduces a scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function and then passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy efficiency of the proposed distributed method versus a centralized approach. For received signal-strength (RSS) based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to the Cramér-Rao lower bound. Further, RSS and time-of-arrival (TOA) channel measurements are used to demonstrate performance as good as the centralized maximum-likelihood estimator (MLE) in a real-world sensor network.
- Albowicz, J., Chen, A., and Zhang, L. 2001. Recursive position estimation in sensor networks. In IEEE International Conference on Network Protocols. 35--41. Google Scholar
- Benzécri, J. 1973. L'Analyse des Données, Tome 2, L'Analyse des Correspondences. Dunod, Paris.Google Scholar
- Caffery Jr., J. and Stuber, G. L. 1998. Subscriber location in cdma cellular networks. IEEE Trans. Veh. Tech. 47, 2 (May), 406--416.Google Scholar
- Čapkun, S., Hamdi, M., and Hubaux, J.-P. 2001. GPS-free positioning in mobile ad-hoc networks. In 34th IEEE Hawaii International Conference on System Sciences (HICSS-34). Google Scholar
- Catovic, A. and Sahinoglu, Z. 2004. The Cramer-Rao bounds of hybrid TOA/RSS and TDOA/RSS location estimation schemes. Tech. Rep. TR2003-143, Mitsubishi Electric Research Laboratory. (Jan).Google Scholar
- Chen, P.-C. 1999. A non-line-of-sight error mitigation algorithm in location estimation. In IEEE Wireless Communication and Networking Conference. 316--320.Google Scholar
- Cleveland, W. 1979. Robust locally weighted regression and smoothing scatterplots. J. Amer. Stat. Assoc. 74, 368, 829--836.Google Scholar
- Correal, N. S., Kyperountas, S., Shi, Q., and Welborn, M. 2003. An ultra wideband relative location system. In IEEE Conference on Ultra Wideband Systems and Technologies.Google Scholar
- Coulson, A. J., Williamson, A. G., and Vaughan, R. G. 1998. A statistical basis for lognormal shadowing effects in multipath fading channels. IEEE Trans. Veh. Tech. 46, 4 (April), 494--502.Google Scholar
- Cox, D. 1972. Delay doppler characteristics of multipath propagation at 910 MHz in a suburban mobile radio environment. IEEE Trans. Antennas and Propagation AP-20, 5 (Sep), 625--635.Google Scholar
- Cox, T. and Cox, M. 1994. Multidimensional Scaling. Chapman & Hall, London.Google Scholar
- Davidson, M. L. 1983. Multidimensional Scaling. Wiley, Ney York.Google Scholar
- Doherty, L., Pister, K. S. J., and Ghaoui, L. E. 2001. Convex position estimation in wireless sensor networks. In IEEE INFOCOM. Vol. 3. 1655--1663.Google Scholar
- Fleming, R. and Kushner, C. 1995. Low-power, miniature, distributed position location and communication devices using ultra-wideband, nonsinusoidal communication technology. Tech. rep., Aetherwire Inc., Semi-Annual Technical Report, ARPA Contract J-FBI-94-058. (July).Google Scholar
- Girod, L., Bychkovskiy, V., Elson, J., and Estrin, D. 2002. Locating tiny sensors in time and space: a case study. In IEEE International Conference on Computer Design. 214--219. Google Scholar
- Greenacre, M. J. 1984. Theory and Applications of Correspondence Analysis. Academic Press Inc., London.Google Scholar
- Groenen, P. 1993. The majorization approach to multidimensional scaling: Some problems and extensions. DSWO Press.Google Scholar
- Gupta, P. and Kumar, P. R. 2000. The capacity of wireless networks. IEEE Trans. Inform. Theory 46, 2, 388--404. Google Scholar
- Hashemi, H. 1993. The Indoor Radio Propagation Channel. Proc. IEEE 81, 7 (July), 943--968.Google Scholar
- Ji, X. and Zha, H. 2004. Sensor positioning in wireless ad-hoc sensor networks with multidimensional scaling. In IEEE INFOCOM, 2652--2661.Google Scholar
- Kim, S., Pals, T., Iltis, R., and Lee, H. 2002. CDMA multipath channel estimation using generalized successive interference cancellation algorithm for radiolocation. In 37th Annual Conference on Information Sciences and Systems.Google Scholar
- Kruskal, J. 1964a. Multidimensional scaling by optmizing goodness-of-fit to a nonmetric hypothesis. Psychometrika 29, 1--27.Google Scholar
- Kruskal, J. 1964b. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29, 115--129.Google Scholar
- Lange, K., Hunter, D. R., and Yang, I. 2000. Optimization transfer using surrogate objective functions. J. Computational Graph. Stat. 9, 1 (March), 1--20.Google Scholar
- Moses, R. L., Krishnamurthy, D., and Patterson, R. 2002. An auto-calibration method for unattended ground sensors. In Proceedings of IEEE International Conference on Acoustic Speech and Signal Processing. Vol. 3. 2941--2944.Google Scholar
- Moses, R. L., Krishnamurthy, D., and Patterson, R. 2003. A self-localization method for wireless sensor networks. EURASIP Journal on Applied Sig. Proc. 4 (Mar.), 348--358.Google Scholar
- Nagpal, R., Shrobe, H., and Bachrach, J. 2003. Organizing a global coordinate system from local information on an ad hoc sensor network. In 2nd International Workshop on Information Processing in Sensor Networks. Google Scholar
- Niculescu, D. and Nath, B. 2001. Ad hoc positioning system. In IEEE Globecom 2001. Vol. 5. 2926--2931.Google Scholar
- Niculescu, D. and Nath, B. 2004. Error characteristics of ad hoc positioning systems. In ACM MOBIHOC. Google Scholar
- Pahlavan, K., Krishnamurthy, P., and Beneat, J. 1998. Wideband radio propagation modeling for indoor geolocation applications. IEEE Comm. Magazine, 60--65. Google Scholar
- Patwari, N. and Hero III, A. O. 2003. Using proximity and quantized RSS for sensor localization in wireless networks. In IEEE/ACM 2nd Workshop on Wireless Sensor Networks & Applications. Google Scholar
- Patwari, N. and Hero III, A. O. 2004. Manifold learning algorithms for localization in wireless sensor networks. In Proceedings of IEEE International Conference on Acoustic Speech and Signal Processing.Google Scholar
- Patwari, N., Hero III, A. O., Perkins, M., Correal, N., and O'Dea, R. J. 2003. Relative location estimation in wireless sensor networks. IEEE Trans. Sig. Proc. 51, 8 (Aug.), 2137--2148. Google Scholar
- Rabbat, M. and Nowak, R. 2004. Distributed optimization in sensor networks. In 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04). Berkeley, CA. Google Scholar
- Ramsay, J. 1982. Some statiscal approaches to multidimensional scaling data. J. R. Statist. Soc. A 145, part 3, 285--312.Google Scholar
- Rappaport, T. S. 1996. Wireless Communications: Principles and Practice. Prentice-Hall Inc., New Jersey. Google Scholar
- Savarese, C., Rabaey, J. M., and Beutel, J. 2001. Locationing in distributed ad-hoc wireless sensor networks. In Proceedings of IEEE International Conference on Acoustic Speech and Signal Processing. 2037--2040.Google Scholar
- Savvides, A., Garber, W. L., Moses, R. L., and Srivastava, M. B. 2004. An analysis of error including parameters in multihop sensor node localization. IEEE Trans. Mobile. Comp. 4, 6, 567--577. Google Scholar
- Savvides, A., Park, H., and Srivastava, M. B. 2002. The bits and flops of the n-hop multilateration primitive for node localization problems. In International Workshop on Sensor Networks & Applications. 112--121. Google Scholar
- Shang, Y., Ruml, W., Zhang, Y., and Fromherz, M. P. J. 2003. Localization from mere connectivity. In Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing. 201--212. Google Scholar
- Takane, Y., Young, F., and de Leeuw, J. 1977. Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features. Psychometrika 42, 7--67.Google Scholar
- Tenenbaum, J. B., de Silva, V., and Langford, J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319--2323.Google Scholar
- Zinnes, J. and MacKay, D. 1983. Probabilistic multidimensional scaling: complete and incomplete data. Psychometrika 48, 27--48.Google Scholar
Index Terms
- Distributed weighted-multidimensional scaling for node localization in sensor networks
Recommendations
Asymmetric Event-Driven Node Localization in Wireless Sensor Networks
Localization of wireless sensor nodes has long been regarded as a problem that is difficult to solve, especially when considering characteristics of real-world environments. This paper formally describes, designs, implements, and evaluates a novel ...
A Weighted DV-Hop Localization Scheme for Wireless Sensor Networks
SCALCOM-EMBEDDEDCOM '09: Proceedings of the 2009 International Conference on Scalable Computing and Communications; Eighth International Conference on Embedded ComputingLocalization is an important problem in wireless sensor networks (WSNs), since location information is widely requested in various location-dependent applications. As one of the range-free localization algorithm DV-Hop, a well known localization ...
Node Localization Algorithm Based on Mobile Anchor in Wireless Sensor Networks
ICIT '17: Proceedings of the 2017 International Conference on Information TechnologyNode localization is one of the prerequisites for the applications of Wireless Sensor Networks. To solve the high overhead problem caused by the requirement for the anchor node density, mobile anchor node(MA) is used instead of the static anchor node. ...
Comments