A Big Data Framework for Urban Noise Analysis and Management in Smart Cities

Navarro, Juan M.; Tomas-Gabarron, Juan-Bautista; Escolano, José

doi:10.3813/aaa.919084

Cited by 13 publications

(11 citation statements)

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“…Thirdly, the gathered data shall be filtered [70], mined and displayed [71]. For this purpose, data management systems need to be deployed [72], [73] and skillfully used. This final step is non trivial and is currently researched extensively.…”

Section: A Wireless Acoustic Sensors Networkmentioning

confidence: 99%

The Internet of Audio Things: State of the Art, Vision, and Challenges

Turchet

Fazekas

Lagrange

et al. 2020

IEEE Internet Things J.

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The Internet of Audio Things (IoAuT) is an emerging research field positioned at the intersection of the Internet of Things, sound and music computing, artificial intelligence, and human-computer interaction. The IoAuT refers to the networks of computing devices embedded in physical objects (Audio Things) dedicated to the production, reception, analysis and understanding of audio in distributed environments. Audio Things, such as nodes of wireless acoustic sensor networks, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. In this paper, we first review the state of the art of this field, then we present a vision for the IoAuT and its motivations. In the proposed vision, the IoAuT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel applications and services based on auditory information. The ecosystems associated with the IoAuT include interoperable devices and services that connect humans and machines to support human-human and human-machines interactions. We discuss challenges and implications of this field, which lead to future research directions on the topics of privacy, security, design of Audio Things, and methods for the analysis and representation of audio-related information.

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Section: A Wireless Acoustic Sensors Networkmentioning

confidence: 99%

The Internet of Audio Things: State of the Art, Vision, and Challenges

Turchet

Fazekas

Lagrange

et al. 2020

IEEE Internet Things J.

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show abstract

“…WASNs can be classified in two categories depending on their nodes spatial location: fixed [23] or mobile [30] stations, that includes the mobile participatory measurement networks like smartphones. Wireless communication technologies add to the devices an ease of deployment and the ability to capture data in near real-time, store and obtain big data statistics [31] and also create dynamic noise maps [32]. Moreover, long-term monitoring of sources of noise pollution is important for the understanding of how these sounds evolve with time, in order to control and prevent them [29].…”

Section: Wireless Acoustic Sensor Network Related Workmentioning

confidence: 99%

“…Storing data in a cloud-based platform in near real time can help managers make early decisions [31].…”

mentioning

confidence: 99%

Long-Term Temporal Analysis of Psychoacoustic Parameters of the Acoustic Environment in a University Campus Using a Wireless Acoustic Sensor Network

Montoya-Belmonte

Navarro

2020

Sustainability

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Higher education institution campuses have been growing in size to become small cities that encompass all the infrastructures and offer many services to the university community. In order to achieve sustainable growth, it is necessary to take into account different environmental aspects, including the acoustic environment. In this paper, the application of a wireless acoustic sensor network is proposed for the long-term analysis of psychoacoustic parameters of the acoustic environment in a university campus. A network of acoustic nodes is designed and four nodes are deployed at both indoor and outdoor locations of the campus for environmental noise monitoring. A measurement campaign has been running continuously for nine months, including three months in which the state of emergency due to COVID-19 pandemic was established and the university switched to online classes. Acquired and stored data include not only the basic sound level parameters, e.g., equivalent or percentile sound pressure levels, but also psychoacoustic parameters, that are more related with noise assessment of the environment, e.g., loudness or sharpness sound levels. After analysing the acoustic environment of the campus through the complete set of nodes, results show two zones with higher noise indicators where some action plan should be developed. A detailed analysis of the temporal evolution of noise levels in these acoustically saturated zones is done, comparing values between a period of regular activity and the period of state of emergency. Different frequency distributions of sound levels are detected between day and evening periods providing a better insight into the noise sources. The results also show that binaural psychoacoustic annoyance values are correlated with loudness values in these locations, however sharpness values have minor effects.

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“…The advantages provided by IoT technology [10], including low power consumption of the equipment and wide area coverage, allow for easy deployment of a large number of devices throughout the city as well as transmitting values of acoustic parameters every short time interval, e.g., every minute [11]. The analysis of this large amount of information generated by the WASN can be considered a big data problem [12].…”

Section: Introductionmentioning

confidence: 99%

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

Pita

Rodríguez

Navarro

2021

IJERPH

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As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.

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A Big Data Framework for Urban Noise Analysis and Management in Smart Cities

Cited by 13 publications

References 0 publications

The Internet of Audio Things: State of the Art, Vision, and Challenges

The Internet of Audio Things: State of the Art, Vision, and Challenges

Long-Term Temporal Analysis of Psychoacoustic Parameters of the Acoustic Environment in a University Campus Using a Wireless Acoustic Sensor Network

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

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