Abstract
This paper investigates the usage of the belief functions theory to localize sensors in indoor environments. The problem is tackled as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The proposed approach uses the belief functions theory to define an evidence framework, for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach as compared to other localization methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alshamaa, D., Mourad-Chehade, F., Honeine, P.: A hierarchical classification method using belief functions. Sig. Process. 148, 68–77 (2018)
Mercier, D., Lefèvre, É., Delmotte, F.: Belief functions contextual discounting and canonical decompositions. Int. J. Approximate Reasoning 53(2), 146–158 (2012)
Fu, C., Yang, S.: The conjunctive combination of interval-valued belief structures from dependent sources. Int. J. Approximate Reasoning 53(5), 769–785 (2012)
Smets, P.: Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. I. J. Approximate Reasoning 9(1), 1–35 (1993)
Koyuncu, H., Yang, S.H.: A 2D positioning system using WSNs in indoor environment. Int. J. Electr. Comput. Sci. IJECS-IJENS 11(3), 70–77 (2011)
Liu, D., Li, T., Liang, D.: Incorporating logistic regression to decision-theoretic rough sets for classifications. Int. J. Approximate Reasoning 55(1), 197–210 (2014)
Acknowledgment
The authors would like to thank the European Regional Development Fund and Grand Est region in France for funding this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Alshamaa, D., Mourad-Chehade, F., Honeine, P. (2018). The Belief Functions Theory for Sensors Localization in Indoor Wireless Networks. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-99383-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99382-9
Online ISBN: 978-3-319-99383-6
eBook Packages: Computer ScienceComputer Science (R0)