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The Belief Functions Theory for Sensors Localization in Indoor Wireless Networks

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Belief Functions: Theory and Applications (BELIEF 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11069))

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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.

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References

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Acknowledgment

The authors would like to thank the European Regional Development Fund and Grand Est region in France for funding this work.

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Authors and Affiliations

  1. Institut Charles Delaunay, Université de Technologie de Troyes, Troyes, France

    Daniel Alshamaa & Farah Mourad-Chehade

  2. LITIS lab, Université de Rouen Normandie, Rouen, France

    Paul Honeine

Authors
  1. Daniel Alshamaa
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  2. Farah Mourad-Chehade
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  3. Paul Honeine
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Corresponding author

Correspondence to Daniel Alshamaa .

Editor information

Editors and Affiliations

  1. University of Technology of Compiègne, Compiègne Cedex, France

    Sébastien Destercke

  2. UMR CNRS 7253 Heudiasyc, Université de Technologie de Compiègne, Compiègne Cedex, France

    Thierry Denoeux

  3. Department of Computing and Communication, Oxford Brookes University, Oxford, United Kingdom

    Fabio Cuzzolin

  4. Université de Rennes 1, Lannion Cedex, France

    Arnaud Martin

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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

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  • DOI: https://doi.org/10.1007/978-3-319-99383-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99382-9

  • Online ISBN: 978-3-319-99383-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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