Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

Zhengguo Sheng; Saskia Pfersich; Alice Eldridge; Jianshan Zhou; Daxin Tian; Victor C.M.Leung

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Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

机译：无线声学传感器网络和边缘计算，用于快速声学监测

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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.

机译：被动声学监测正逐渐成为解决全球新生物多样性评估方法的迫切需求的有前途的解决方案。人们越来越认识到声景的生态意义，而强大的硬件可承受的远程音频录制能力也引起了国际社会对声学潜力的兴趣。用于生物多样性监测的方法。所涉及的数据规模需要自动化的方法，但是，能够在时间和空间上采样声景并将数据中继到可访问的存储位置的声学传感器网络的发展仍然是一项重大的技术挑战，而电源管理记录和传输大量音频数据非常耗电，因此不利于在具有关键生态意义的偏远偏远地区长期部署。本文提出了一种低成本的方法，而不是传输大量音频数据边缘计算技术的高能效无线声传感器网络用于远程声学监测和原位分析的结构。使用内部FFT硬件支持，直接在由低噪声primo电容麦克风和Teensy微控制器构建的边缘设备上进行声指数的记录和计算。结果索引通过基于ZigBee的无线传输到目标服务器的网状网络。音频质量，指标计算和功耗的基准测试表明，与当前解决方案相比，声学等效性和显着的节能效果。

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《自动化学报（英文版）》 |2019年第1期|64-74|共11页
作者
Zhengguo Sheng; Saskia Pfersich; Alice Eldridge; Jianshan Zhou; Daxin Tian; Victor C.M.Leung;
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Department of Engineering and Design, University of Sussex, Brighton BNI 9RH, UK;

Infineon Technologies AG, Germany;

Department of Music, University of Sussex, Falmer BNI 9RG, UK;

Beijing Advanced Innovation Center for Big Data and Brain Computing(BDBC), Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100190,China;

Beijing Advanced Innovation Center for Big Data and Brain Computing(BDBC), Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control, School of Transportation Science and Engineering, Beihang University, Beijing 100190,China;

Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver V6T 1Z4,Canada;

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Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

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