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A Bluetooth-Low-Energy Sensor Node for Acoustic Monitoring of Small Birds


Magno, Michele; Vultier, Fabien; Szebedy, Bence; Yamahachi, Homare; Hahnloser, Richard H R; Benini, Luca (2020). A Bluetooth-Low-Energy Sensor Node for Acoustic Monitoring of Small Birds. IEEE Sensors Journal, 20(1):425-433.

Abstract

Animals can generate sounds that serve a wide range of vital functions such as to defend themselves or their territories, to attract a partner, to maintain contact with other members of their social group, and to help themselves and their partner/group during navigation. Ethologists are interested in recording and analyzing these sounds, many of which are vocalizations. Advances in sensing and wireless technology permit today acoustic data acquisition and transmission in a wireless manner. In many applications, the wireless sensor needs to be placed on the animal’s body and should be unobtrusive, light-weight, small, and long-lasting. This paper presents the design and development of an ultra-low power miniaturized and lightweight wireless sensor node for monitoring captive zebra finches. The node is designed to be worn with minimal effort by small-sized birds to collect, process, and send/receive data to/from a remote host via Bluetooth Low-Energy. The main feature of the developed node is the capability to stream compressed or uncompressed audio and temperature data continuously. Multiple nodes can monitor several birds simultaneously and acquire and transmit high-quality audio streams, one for each bird, with low audio interference. Due to the combination of low-power hardware and software techniques and technologies, the 1.4 g node achieves a lifetime of up to 24 h at 4 kHz sampling rate on a single zinc-air battery. Experimental results on birds confirm the functionality of the developed wireless node and the lifetime benefits of compression.

Abstract

Animals can generate sounds that serve a wide range of vital functions such as to defend themselves or their territories, to attract a partner, to maintain contact with other members of their social group, and to help themselves and their partner/group during navigation. Ethologists are interested in recording and analyzing these sounds, many of which are vocalizations. Advances in sensing and wireless technology permit today acoustic data acquisition and transmission in a wireless manner. In many applications, the wireless sensor needs to be placed on the animal’s body and should be unobtrusive, light-weight, small, and long-lasting. This paper presents the design and development of an ultra-low power miniaturized and lightweight wireless sensor node for monitoring captive zebra finches. The node is designed to be worn with minimal effort by small-sized birds to collect, process, and send/receive data to/from a remote host via Bluetooth Low-Energy. The main feature of the developed node is the capability to stream compressed or uncompressed audio and temperature data continuously. Multiple nodes can monitor several birds simultaneously and acquire and transmit high-quality audio streams, one for each bird, with low audio interference. Due to the combination of low-power hardware and software techniques and technologies, the 1.4 g node achieves a lifetime of up to 24 h at 4 kHz sampling rate on a single zinc-air battery. Experimental results on birds confirm the functionality of the developed wireless node and the lifetime benefits of compression.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Instrumentation
Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Instrumentation, Electrical and Electronic Engineering
Language:English
Date:1 January 2020
Deposited On:14 Feb 2020 09:53
Last Modified:29 Jul 2020 14:15
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1530-437X
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/jsen.2019.2940282
Project Information:
  • : FunderSNSF
  • : Grant ID200021_162524
  • : Project TitleMicroLearn: Micropower Deep Learning

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