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

doi:10.1109/JAS.2019.1911324

Volume 6 Issue 1

Jan. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(1): 64-74

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

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

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Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

doi: 10.1109/JAS.2019.1911324

1.
Department of Engineering and Design, University of Sussex, Brighton BNI 9RH, UK
2.
Infineon Technologies AG, Germany
3.
Department of Music, University of Sussex, Falmer BNI 9RG, UK
4.
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
5.
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver V6T 1Z4, Canada

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Author Bio:
Zhengguo Sheng graduated from University of Electronic Science and Technology of China, China, in 2006. He received the M.S. degree from Imperial College London in 2007 and the Ph.D. degree from Imperial College London, UK, in 2011. He is currently a Senior Lecturer at the University of Sussex, UK. His research interests include IoT, vehicular communications, and cloud/edge computing(e-mail: z.sheng@sussex.ac.uk)

Saskia Pfersich is a Field Application Engineering Manager of a German semiconductor company. She graduated from Duale Hochschule BadenWuerttemberg, Germany, in 2011 and holds a Master of Science degree in Engineering Business Management from University of Sussex, England. Her areas of interest are in wireless sensor networks and security solutions, with a focus on embedded security measures in a connected world(e-mail: saskia.pfersich@gmail.com)

Alice Eldridge graduated from the University of Sussex, UK in 2007. She received a B.Sc. degree in psychology from the University of Leeds in 1999, an M.Sc. degree in evolutionary and adaptive systems in 2002 and a Ph.D. degree in informatics in 2007, from the University of Sussex. She is currently a Lecturer in Music Technology at the University of Sussex. Her research interests include machine listening in ecological and creative systems (e-mail: alicee@sussex.ac.uk)

Jianshan Zhou received the B.Sc. and M.Sc. degrees in traffic information engineering and control in 2013 and 2016, respectively. He is currently working towards the Ph.D. degree with the School of Transportation Science and Engineering, Beihang University, China. His current research interests are focused on wireless communication, artificial intelligent system, and intelligent transportation systems (e-mail: jianshanzhou@foxmail.com)

Daxin Tian received the B.A.Sc., M.Sc. and Ph.D. degrees in computer engineering from Jilin University, China, in 2002, 2005 and 2007, respectively. He is currently a Professor at the Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang University, China. His current research interests include mobile computing, intelligent transportation systems, vehicular ad hoc networks, and swarm intelligent (e-mail: dtian@buaa.edu.cn)

Victor C. M. Leung graduated from the University of British Columbia (UBC), Canada, in 1977. He received the Ph.D. degree from UBC, Canada, in 1982. He is currently a Professor of Electrical and Computer Engineering and Holder of the TELUS Mobility Research Chair at the University of British Columbia, Canada. His research is in the broad areas of wireless networks and mobile systems
Corresponding author: Victor C. M. Leung, (e-mail: vleung@ece.ubc.ca)
Received Date: 2018-09-25
Accepted Date: 2018-11-02

Abstract

Abstract

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.
- Acoustic sensor networks,
- edge computing,
- energy efficiency

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References(59)

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Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

doi: 10.1109/JAS.2019.1911324

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