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<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Acoustic counting algorithms for wireless sensor networks</title>
<creator>Gros-Désormeaux, Harry</creator>
<creator>Hunel, Philippe</creator>
<creator>Vidot, Nicolas</creator>
<creator>Stattner, Erick</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks</source>
<source>6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks</source>
<coverage>New York, United States</coverage>
<publisher>ACM</publisher>
<identifier>hal-00602263</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00602263</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00602263</source>
<source>6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, 2009, New York, United States. ACM, pp.79--84, 2009, 〈10.1145/1641876.1641891〉</source>
<identifier>DOI : 10.1145/1641876.1641891</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1145/1641876.1641891</relation>
<language>en</language>
<subject lang=en>wireless sensors</subject>
<subject lang=en>unit disk graph</subject>
<subject lang=en>swarm computing</subject>
<subject lang=en>distributed network</subject>
<subject lang=en>habitat monitoring</subject>
<subject>[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI]</subject>
<subject>[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS]</subject>
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<type>Conference papers</type>
<description lang=en>This study presents two algorithms that count birds with wireless sensors equipped with microphones. Audio inputs are parametrized to get some kind of fingerprints which are used to recognize the song of the birds in a classification process. Afterward, counting algorithms derive an estimate of the number of singing birds in the habitat. Unlike traditional approaches which leverage the audio waveform to get the location of the targets, we use a trilateration technique on graphs derived from the detection of the birds. These graphs exhibit nice properties which enable the finding of the estimate in polynomial time. Further, experiments are conducted and confirmm the efficiency of our counting algorithms even in the presence of noise.</description>
<date>2009</date>
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