A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 6 Issue 1
Jan.  2019

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Zhengguo Sheng, Saskia Pfersich, Alice Eldridge, Jianshan Zhou, Daxin Tian and Victor C. M. Leung, "Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 64-74, Jan. 2019. doi: 10.1109/JAS.2019.1911324
Citation: Zhengguo Sheng, Saskia Pfersich, Alice Eldridge, Jianshan Zhou, Daxin Tian and Victor C. M. Leung, "Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 64-74, Jan. 2019. doi: 10.1109/JAS.2019.1911324

Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

doi: 10.1109/JAS.2019.1911324
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  • Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring. The scale of the data involved requires automated methods, however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive, hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis. Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server. Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.


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