A Distributed Sensor Network for Monitoring Noise Level and Noise Sources in Urban Environments

Abeber, Jakob; Götze, Marco; Kühnlenz, Stephanie; Robert, G Rackl; Klug, Christian; Tobias, ClauB; Hanna, Lukashevich

doi:10.1109/ficloud.2018.00053

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…In AbeBer et al [ 56 ], a similar method was implemented for urban noise monitoring. Namely, while STFT was utilized for the noise preprocessing, the classification of noise levels and events was performed by convolutional neural networks (CNNs).…”

Section: Wsn Architecture—arising Platforms and Novel Infrastructure ...mentioning

confidence: 99%

Data Gathering Techniques in WSN: A Cross-Layer View

Gurewitz

Shifrin

Dvir

2022

Sensors

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Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural and environmental monitoring and many more. This expansion is rapidly growing every passing day in terms of the variety, heterogeneity and the number of devices which such applications support. Data collection is commonly the core application in WSN and IoT networks, which are typically composed of a large variety of devices, some constrained by their resources (e.g., processing, storage, energy) and some by highly diverse demands. Many challenges span all the conceptual communication layers, from the Physical to the Applicational. Many novel solutions devised in the past do not scale well with the exponential growth in the population of the devices and need to be adapted, revised, or new innovative solutions are required to comply with this massive growth. Furthermore, recent technological advances present new opportunities which can be leveraged in this context. This paper provides a cross-layer perspective and review of data gathering in WSN and IoT networks. We provide some background and essential milestones that have laid the foundation of many subsequent solutions suggested over the years. We mainly concentrate on recent state-of-the-art research, which facilitates the scalable, energy-efficient, cost-effective, and human-friendly functionality of WSNs and the novel applications in the years to come.

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Section: Wsn Architecture—arising Platforms and Novel Infrastructure ...mentioning

confidence: 99%

Data Gathering Techniques in WSN: A Cross-Layer View

Gurewitz

Shifrin

Dvir

2022

Sensors

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“…A two-class sound classifier based on the Gaussian mixture model (GMM) was used to determine the noise contribution from roads and junctions while ignoring noise events unrelated to traffic noise [27]. Another research project referred to as the StadtLärm project [28,29] focuses on acoustic measurement and sound classification within and around a park located in Jena, Germany. The area under this study is surrounded by two streets that connect the inner city to the nearby highways, trams, and trains.…”

Section: Introductionmentioning

confidence: 99%

Extracting Urban Sound Information for Residential Areas in Smart Cities Using an End-to-End IoT System

Tan

Karnapi

et al. 2021

IEEE Internet Things J.

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“…Together with sound quality concerns in urban environments, the advent of the Internet of Things has led to the implementation of large scale acoustic sensor networks for monitoring purposes in several projects [1,2,3,4]. The aim of these sensor networks is to gather rich information about the sound environment and its content.…”

Section: Introductionmentioning

confidence: 99%

Privacy Aware Acoustic Scene Synthesis Using Deep Spectral Feature Inversion

Gontier

Lagrange

Lavandier

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Gathering information about the acoustic environment of urban areas is now possible and studied in many major cities in the world. Part of the research is to find ways to inform the citizen about its sound environment while ensuring her privacy. We study in this paper how this application can be cast into a feature inversion problem. We argue that considering deep learning techniques to solve this problem allows us to produce sound sketches that are representative and privacy aware. Experiments done considering the dcase2017 dataset shows that the proposed learning based approach achieves state of the art performance when compared to blind inversion approaches.

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A Distributed Sensor Network for Monitoring Noise Level and Noise Sources in Urban Environments

Cited by 14 publications

References 11 publications

Data Gathering Techniques in WSN: A Cross-Layer View

Data Gathering Techniques in WSN: A Cross-Layer View

Extracting Urban Sound Information for Residential Areas in Smart Cities Using an End-to-End IoT System

Privacy Aware Acoustic Scene Synthesis Using Deep Spectral Feature Inversion

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