| Identifiant de l'article : |
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inria-00490195, version 2 |
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| Domaine : |
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| Titre : |
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Reconstructing Social Interactions Using an unreliable Wireless Sensor Network |
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| Auteur(s) : |
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Adrien Friggeri1, 2, Guillaume Chelius1, 2, Eric Fleury1, 2, Antoine Fraboulet3, 4, France Mentré5, Jean-Christophe Lucet6 |
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| Projet(s) / laboratoire(s) : |
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| 1 : |
ENS / LIP Laboratoire de l'Informatique du Parallélisme / INRIA Grenoble Rhône-Alpes - DNET |
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LIP - Laboratoire de l'Informatique du Parallélisme |
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CITI - Communications, Images et Traitement de l'Information |
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CITI Insa Lyon / Inria Grenoble Rhône-Alpes - AMAZONES |
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Modèles et méthodes de l'évaluation thérapeutique des maladies chroniques |
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Hôpital Bichat - Claude Bernard |
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| Résumé : |
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In the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed for their analysis. These works have led to the identification of similarities in the structures of such networks arising in very different fields, and to the development of a body of knowledge, tools and methods for their study. While many interesting questions remain open on the subject of static networks, challenging issues arise from the study of dynamic networks. In particular, the measurement, analysis and modeling of social interactions are first class concerns. In this article, we address the challenges of capturing physical proximity and social interaction by means of a wireless network. In particular, as a concrete case study, we exhibit the deployment of a wireless sensor network applied to the measurement of Health Care Workers' exposure to tuberculosis infected patients in a service unit of the Bichat-Claude Bernard hospital in Paris, France. This network has continuously monitored the presence of all HCWs in all rooms of the service during a 3 month period. We both describe the measurement system that was deployed and some early analysis on the measured data. We highlight the bias introduced by the measurement system reliability and provide a reconstruction method which not only leads to a significantly more coherent and realistic dataset but also evidences phe- nomena a priori hidden in the raw data. By this analysis, we suggest that a processing step is required prior to any adequate exploitation of data gathered thanks to a non-fully reliable measurement architecture. |
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| Langue du document : |
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Anglais |
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| Mots-clés : |
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complex networks – interaction networks – wireless sensor networks – medical applications |
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| Contrat, financement : |
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This work was supported by a public grant from the French National Agency for Food, Environmental and Occupational Health Safety (ANSES/AFSSET, EST 2007-50) |
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