@INPROCEEDINGS{14.iwssip.wsn,
   author =  "Nisrine Ghadban and Paul Honeine and Farah Mourad-Chehade and Clovis Francis and  Joumana Farah",
   title =  "Mobility using first and second derivatives for kernel-based regression in wireless sensor networks",
   booktitle =  "Proc. 21st International Conference on Systems, Signals and Image Processing",
   address =  "Dubrovnik, Croatia",
   year  =  "2014",
   month =  "12 - 15~" # may,
   acronym =  "IWSSIP",
   pages={203-206}, 
   url_link= "https://ieeexplore.ieee.org/document/6837666",
   url_paper   =  "http://honeine.fr/paul/publi/14.iwssip.wsn.pdf",
   abstract={This paper deals with the problem of tracking and monitoring physical phenomena using wireless sensor networks. It proposes an original mobility scheme that aims at improving the tracking process. To this end, a model is defined using kernel-based methods and a learning process. The sensors are given the ability to move in a manner that minimizes the approximation error, and thus improves the efficiency of the model. First and second derivatives of the approximation error are used to define the new positions of the nodes. The performance of the proposed method is illustrated in the context of monitoring gas diffusion with wireless sensor networks.}, 
   keywords={regression analysis, wireless sensor networks, kernel-based regression, wireless sensor networks, original mobility scheme, learning process, approximation error, gas diffusion monitoring, second derivatives, first derivatives, Robot sensing systems, Mathematical model, Monitoring, Power measurement, Vectors}, 
   ISSN={2157-8672}, 
}

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It proposes an original mobility scheme that aims at improving the tracking process. To this end, a model is defined using kernel-based methods and a learning process. The sensors are given the ability to move in a manner that minimizes the approximation error, and thus improves the efficiency of the model. First and second derivatives of the approximation error are used to define the new positions of the nodes. The performance of the proposed method is illustrated in the context of monitoring gas diffusion with wireless sensor networks.","keywords":"regression analysis, wireless sensor networks, kernel-based regression, wireless sensor networks, original mobility scheme, learning process, approximation error, gas diffusion monitoring, second derivatives, first derivatives, Robot sensing systems, Mathematical model, Monitoring, Power measurement, Vectors","issn":"2157-8672","bibtex":"@INPROCEEDINGS{14.iwssip.wsn,\n author = \"Nisrine Ghadban and Paul Honeine and Farah Mourad-Chehade and Clovis Francis and Joumana Farah\",\n title = \"Mobility using first and second derivatives for kernel-based regression in wireless sensor networks\",\n booktitle = \"Proc. 21st International Conference on Systems, Signals and Image Processing\",\n address = \"Dubrovnik, Croatia\",\n year = \"2014\",\n month = \"12 - 15~\" # may,\n acronym = \"IWSSIP\",\n pages={203-206}, \n url_link= \"https://ieeexplore.ieee.org/document/6837666\",\n url_paper = \"http://honeine.fr/paul/publi/14.iwssip.wsn.pdf\",\n abstract={This paper deals with the problem of tracking and monitoring physical phenomena using wireless sensor networks. 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