@INPROCEEDINGS{18.belief,
   author =  "Daniel AlShamaa and Farah Chehade and Paul Honeine",
   title =  "The Belief Functions Theory for Sensors Localization in Indoor Wireless Networks",
   editor="Destercke, S{\'e}bastien and Denoeux, Thierry and Cuzzolin, Fabio and Martin, Arnaud",
   booktitle =  "Proc. 5th International Conference on Belief Functions (BELIEF 2018): Belief Functions: Theory and Applications",
   year  =  "2018",
   month =  "17 - 21~" # sep,
   publisher = "Springer International Publishing",
   address = "Compiègne, France",
   pages = "10--13",
   keywords =  "machine learning, wireless sensor networks",
   acronym =  "BELIEF",
   isbn="978-3-319-99383-6",
   url_paper  =  "http://honeine.fr/paul/publi/18.belief.pdf",
   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.",
}%   address =  "Compiègne, France",
%   url_link= "https://link.springer.com/chapter/10.1007%2F978-3-319-99383-6_2",

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