Wireless acoustic sensor networks and edge computing for rapid acoustic monitoring
| Autor: | Saskia Pfersich, Daxin Tian, Zhengguo Sheng, Alice Eldridge, Victor C. M. Leung, Jianshan Zhou |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Power management Soundscape Wireless mesh network Edge device Computer science business.industry Real-time computing 020206 networking & telecommunications 02 engineering and technology 010603 evolutionary biology 01 natural sciences Artificial Intelligence Control and Systems Engineering TK5101 0202 electrical engineering electronic engineering information engineering Wireless Sound quality business Wireless sensor network Edge computing Information Systems |
| Zdroj: | IEEE/CAA Journal of Automatica Sinica. 6:64-74 |
| ISSN: | 2329-9274 2329-9266 |
| DOI: | 10.1109/jas.2019.1911324 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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