Research on MCL for Mobile Underground Miner Location System Based on ZigBee

Article Preview

Abstract:

In this paper, we try to solve the problem of mine safety and real-time scheduling in underground miner, the Monte-Carlo localization methods of the underground location system based on ZigBee wireless sensor network were proposed. This paper designs software application for the nodes in the network, builds the overall structure of the miner location system and constructs the ZigBee wireless network. After testing and simulation, the results indicate that we can locate precisely in the complex terrain and terrible communication environment by the Monte-Carlo Localization algorithm.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2325-2328

Citation:

Online since:

January 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Han, D. -M., Lim, J. -H.: Smart Home Energy Management System using IEEE 802. 15. 4 and ZigBee. IEEE Transactions on Consumer Electronics 56, 1403–1410 (2010).

DOI: 10.1109/tce.2010.5606276

Google Scholar

[2] B.H. Wellenhoff, H. Lichtenegger, J. Collins, Global Positioning System: Theory and Practice, fourth ed, Springer Verlag, (1997).

Google Scholar

[3] N. Bulusu, J. Heidemann, D. Estrin, GPS-less low cost outdoor localization for very small devices, IEEE Personal Communications Magazine (2000).

DOI: 10.1109/98.878533

Google Scholar

[4] T. He, C. Huang, B.M. Blum, J.A. Stankovic, T. Abdelzaher, Range-free localization schemes for large scale sensor networks, in: MobiCom, (2003).

DOI: 10.1145/938985.938995

Google Scholar

[5] L. Hu, D. Evans, Localization for mobile sensor networks, in: Proc. ACM MobiCom, 2004, 45–57.

Google Scholar

[6] ZigBee Alliance, ZigBee Specification. version1. 1 (November 2006).

Google Scholar

[7] Arnaud Doucet, Nando de Freitas and Neil Gordon. An Introduction to Sequential Monte Carlo Methods. In Sequential Monte Carlo Methods in Practice, eds. Arnaud Doucet, Nando de Freitas and Neil Gordon. (2001).

DOI: 10.1002/wilm.42820030107

Google Scholar

[8] A. Kong, J. S. Liu and W. H. Wong. Sequential Imputations and Bayesian Missing Data Problems. Journal of the American Statistical Association. Volume 89, pp.278-288. (1994).

DOI: 10.1080/01621459.1994.10476469

Google Scholar