untitled
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<identifier>oai:HAL:hal-00519594v1</identifier>
<datestamp>2017-12-21</datestamp>
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<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Distributed diagnosis over wireless sensors networks.</title>
<creator>Dessart, Nathalie</creator>
<creator>Fouchal, Hacène</creator>
<creator>Hunel, Philippe</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>Revue de rang A</description>
<description>International audience</description>
<source>ISSN: 1532-0626</source>
<source>EISSN: 1532-0634</source>
<source>Concurrency and Computation: Practice and Experience</source>
<publisher>Wiley</publisher>
<identifier>hal-00519594</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00519594</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00519594</source>
<source>Concurrency and Computation: Practice and Experience, Wiley, 2010, pp.1240-1251. 〈10.1002/cpe.1583〉</source>
<identifier>DOI : 10.1002/cpe.1583</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1002/cpe.1583</relation>
<language>en</language>
<subject lang=en>wireless sensor networks</subject>
<subject lang=en>monitoring</subject>
<subject lang=en>distributed decision</subject>
<subject lang=en>TinyOS</subject>
<subject>Wiley</subject>
<subject>[INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>This paper studies how to detect anomalies in a distributed manner by using wireless sensor networks (WSNs). We focus on a medical context, where the existing works generally provide large environments to monitor patients using sensors as simple transducers. Those devices forward sensed health parameters to a main base station. This station collects received data and may perform some computations. In this paper, we perform some distributed tasks on the sensors. We propose a distributed algorithm, which allows to raise alarms under some initial rules to alert efficiently medical staff in case of critical situations without needless warnings. Each mote monitors a parameter. When this parameter reaches an abnormal value, the mote communicates with other motes in order to check if it is a ‘local' anomaly or if the patient is in an abnormal situation. In such cases an alarm is raised. We implemented our algorithm over a network of micaZ sensors running under TinyOS. The obtained results show promising perspectives.</description>
<date>2010-02</date>
</dc>
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