Dual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks

TitleDual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks
Publication TypeConference Paper
Year of Publication2007
AuthorsStevens-Navarro, E., V. Vivekanandan, and V. W. S. Wong
Conference NameWireless Communications and Networking Conference, 2007.WCNC 2007. IEEE
Pagination4024 -4028
Date Publishedmar.
Keywordscomputational time, distributed algorithms, dual MCL, estimated location accuracy, mixture MCL, mobile computing, mobile wireless sensor networks, Monte Carlo localization algorithms, Monte Carlo methods, wireless sensor networks

In this paper, we consider a mobile wireless sensor network where both sensor nodes and the seeds are moving. We propose and analyze two variations of the Monte Carlo localization (MCL) algorithms, namely: dual MCL and mixture MCL, for mobile sensor networks. We conduct simulation experiments to evaluate the performance of these two algorithms by varying the number of seeds, number of nodes, number of samples, velocity of nodes, and radio pattern degree of irregularity. Results show that both dual MCL and mixture MCL are more accurate than the original MCL algorithm. In terms of the trade off between the computational time and estimated location accuracy, the mixture MCL has a better performance than both dual MCL and the original MCL algorithms.


a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2020 The University of British Columbia