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Enhancing wireless sensor networks with acoustic sensing technology: Use cases, applications & experiments

 
: Hollosi, D.; Nagy, G.; Rodigast, R.; Goetze, S.; Cousin, P.

:

IEEE Computer Society; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Green Computing and Communications (GreenCom 2013) and IEEE Internet of Things (iThings 2013) and IEEE cyber, physical and social computing (CPSCom 2013) : Beijing, China, 20 - 23 August 2013
Piscataway, NJ: IEEE, 2013
ISBN: 978-1-4799-0631-4
ISBN: 978-0-7695-5046-6
S.335-342
International Conference on Internet of Things (iThings) <2013, Beijing>
Englisch
Konferenzbeitrag
Fraunhofer IDMT ()

Abstract
This paper presents research carried out within the EU FP7 EAR-IT project, which is working on the challenges of bringing acoustic sensing intelligence to large-scale indoor and outdoor wireless sensor networks, i.e. into two existing test beds out of the EU FP7 FIRE projects SmartSantander and Hobnet. Besides the benefits by integrating machine-learning based acoustic sensing technology, the general deployment approach of the so-called Acoustic Processing Unit, an embedded device whose capabilities go beyond state-of-the-art IoT sensors, as well as the EAR-IT indoor and outdoor use cases are described. This includes hardware qualification for the applications such as energy efficiency of buildings, traffic monitoring and emergency vehicle detection and tracking outdoor as well as acoustic emergency detection for indoor environments and further, a detailed description of the individual Acoustic Processing Unit software components. Latest efforts and simulation results f or acoustic source localization using audio sensing technology for wireless sensor networks are presented, indicating that a more intelligent usage of the audio modality enables a wide range of applications and services with high social and technological value.

: http://publica.fraunhofer.de/dokumente/N-300741.html