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Study of a Cost-Effective Localization Algorithm in Wireless Sensor Networks

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Mobile Ad-Hoc and Sensor Networks (MSN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 4864))

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Abstract

Most of the current RSS (Received Signal Strength)-based localization algorithms in Wireless Sensor Networks (WSNs) rely on isotropic radio propagation model to infer the distance between a pair of nodes from received signal strength, which however has been proved to be quite unreliable in recent research work. Performance analysis is important to evaluate the applicability of a localization algorithm, however little work has been done on evaluating the existing localization algorithms in simulated realistic settings. This paper firstly presents the motivation and detailed implementation of a proposed Link-State Based Annulus (LSBA) localization algorithm, and then gives a panorama of performance comparison among LSBA and other four localization algorithms in terms of estimation error, convergence speed, computational complexity and communication cost in the simulated realistic environment. Simulation results show that LSBA achieves the best tradeoff among all the four metrics in WSNs with moderate number of anchors, and has good adaptability to irregular node deployment as well.

This work was supported by the National Natural Science Foundation of China under Grant No.60673173.

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Hongke Zhang Stephan Olariu Jiannong Cao David B. Johnson

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© 2007 Springer-Verlag Berlin Heidelberg

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Li, X., Hua, B., Guo, Y. (2007). Study of a Cost-Effective Localization Algorithm in Wireless Sensor Networks. In: Zhang, H., Olariu, S., Cao, J., Johnson, D.B. (eds) Mobile Ad-Hoc and Sensor Networks. MSN 2007. Lecture Notes in Computer Science, vol 4864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77024-4_53

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  • DOI: https://doi.org/10.1007/978-3-540-77024-4_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77023-7

  • Online ISBN: 978-3-540-77024-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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