Wireless acoustic sensor networks and edge computing for rapid acoustic monitoring

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

doi:10.1109/jas.2019.1911324

Cited by 47 publications

(23 citation statements)

References 36 publications

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“…The use of existing platforms (1) simplifies the sensor development by using components that have already been optimized in terms of energy consumption (TelosB, CiNet, Teensy USB, Tmote) [33,42,54,56,59,63]. Those platforms generally include all the components needed to develop an environmental sensor (radio communication module, ADC, storage memory, connectors for other sensors, etc.).…”

Section: Sensor Platformmentioning

confidence: 99%

“…In both cases, the final objective is to integrate, after optimization, these algorithms directly into the sensors. On the other hand, biodiversity monitoring rather emphasizes the need to preserve raw audio signals, which creates constraints for continuously capturing and transmitting data in a fully autonomous wireless SN [31,33,79].…”

Section: Noise Indicatorsmentioning

confidence: 99%

“…From the initial design stage, this means overestimating the capacities of the panels and batteries to ensure trouble-free operation of the sensors over the expected service life. Lastly, one can also mention that the technical improvement of the energy system must also be accompanied by the development of algorithms and procedures to optimize or reduce energy consumption [33].…”

Section: Power Resourcesmentioning

confidence: 99%

“…The use of such a noise SN also opens up many additional interesting opportunities, such as to assess and manage road traffic noise [12][13][14], to enhance the traditional strategic noise mapping process [15][16][17][18][19], to produce dynamics noise maps [20] or to capture the sound sources of interest or acoustic events within the signal [21][22][23][24][25][26][27][28][29]. One can mention also that low-cost SN may be used in other fields in acoustics, such as sound source localisation [30] and biodiversity monitoring [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review

Picaut

Can

Fortin

et al. 2020

Sensors

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Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

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Section: Sensor Platformmentioning

confidence: 99%

Section: Noise Indicatorsmentioning

confidence: 99%

Section: Power Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review

Picaut

Can

Fortin

et al. 2020

Sensors

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“…Although the approach of edge computing in UWB networks is novel, researchers have recently started applying edge computing in other domains. The authors of [25] propose edge computing for wireless acoustic sensor networks. Their findings demonstrate significant power savings by mitigating transmissions of large quantities of audio.…”

Section: B Edge Computing In Wireless Networkmentioning

confidence: 99%

Edge Inference for UWB Ranging Error Correction Using Autoencoders

et al. 2020

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Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU.

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Pandemics and the Need for Automated Systems for Biodiversity Monitoring

Sugai

2020

J Wildl Manag

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Wireless acoustic sensor networks and edge computing for rapid acoustic monitoring

Cited by 47 publications

References 36 publications

Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review

Low-Cost Sensors for Urban Noise Monitoring Networks—A Literature Review

Edge Inference for UWB Ranging Error Correction Using Autoencoders

Pandemics and the Need for Automated Systems for Biodiversity Monitoring

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