Localization for Mobile Sensor Networks
Lingxuan Hu and David Evans
Physical Cryptography and Security Group
at the University of Virginia

Abstract

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report results from simulated experiments. Our scheme outperforms the best known static localization schemes under a wide range of conditions.

Keywords: Localization, sensor networks, mobility, Monte Carlo Localization.

Paper

Lingxuan Hu and David Evans. Localization for Mobile Sensor Networks. Tenth Annual International Conference on Mobile Computing and Networking (ACM MobiCom 2004). 26 September - 1 October 2004. [PDF]

Talk Slides [PPT]

Related Projects

Our work on using directional antennas:
Lingxuan Hu and David Evans. Using Directional Antennas to Prevent Wormhole Attacks. Network and Distributed System Security Symposium, February 2004. [PDF]

Other papers that have built on this work include:

Aline Baggio and Koen Langendoen. Monte-Carlo Localization for Mobile Wireless Sensor Networks. 2nd Int. Conference on Mobile Ad-hoc and Sensor Networks (MSN 2006), Hong Kong, China, December 2006 [PDF]

Marcelo H. T. Martins, Hongyang Chen, Kaoru Sezaki. OTMCL: Orientation Tracking-based Monte Carlo Localization for Mobile Sensor Networks. Sixth Annual International Conference on Networked Sensing Systems (INSS 2009). Pittburgh, PA, June 2009. [PDF]

Enrique Stevens-Navarro, Vijayanth Vivekanandan, and Vincent W.S. Wong, Dual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks. IEEE Wireless Communications and Networking Conference (WCNC'07), Hong Kong, China, March 2007. [PDF]

Masoomeh Rudafshani and Suprakash Datta. Localization in Wireless Sensor Networks. The 6th International Conference on Information Processing in Sensor Networks (IPSN). Cambridge, MA, April 2007.

Jiyoung Yi, Sungwon Yang, Hojung Cha. Multi-hop-based Monte Carlo Localization for Mobile Sensor Networks. Fourth Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2007), June 2007.

Shigeng Zhang, Jiannong Cao, Lijun Chen, Daoxu Chen. Locating Nodes in Mobile Sensor Networks More Accurately and Faster. 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2008), 2008.

Software and Data

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swarm University of Virginia
Department of Computer Science
Physicrypt
Sponsored by the National Science Foundation David Evans
evans@cs.virginia.edu