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Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 57700

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Special Issue Editors

Dr. Rosa Ma Alsina-Pagès

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Guest Editor

Universitat Ramon Llull, Barcelona, Spain
Interests: acoustic event detection; real-time signal processing; adaptive signal processing; noise monitoring; noise annoyance; impact of noise events
Special Issues, Collections and Topics in MDPI journals

Dr. Patrizia Bellucci

E-Mail Website
Guest Editor

ANAS S.p.A., DIV Research and Development, Rome, Italy
Interests: noise monitoring; noise modeling; noise mitigation measures design; dynamic noise measurement; real time noise mapping; electro-acoustic systems development; standardization

Dr. Giovanni Zambon

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Guest Editor

Department of Earth and Environmental Sciences, Università degli Studi di Milano-Bicocca, Milan, Italy
Interests: soundscape in urban and natural areas; eco-acoustics; soundscape in marine habitats; noise mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Environmental Noise Directive (END) requires that a five-year updating of noise maps is carried out, to check and report on the changes occurred during the reference period. The updating process is usually achieved using a standardized approach, consisting of collecting and processing information through acoustic models to produce the updated noise maps. This procedure is time consuming and costly and has a significant impact on the financial statement of the authorities responsible for providing the maps. Furthermore, the END requires that easy-to-read noise maps are made available to the public, to provide information on noise levels and the subsequent actions to be undertaken by local and central authorities to reduce noise impacts. In order to update the noise maps more easily and in a more effective way, it is convenient to design an integrated system incorporating real-time noise measurement and signal processing to identify and analyze the noise sources present in the mapping area (e.g. road traffic noise, leisure noise, etc.), as well as to automatically generate and present the corresponding noise maps. This wireless acoustic sensor network design requires transversal knowledge, from accurate hardware design for the acoustic sensors, to network structure design and management of the information, signal processing to identify the origin of the measured noise and graphical user interface application design to present the results to end users. This Special Issue is focused on all the technologies necessary for the development of an efficient wireless acoustic sensor network, from the first stages of its design to the tests conducted during deployment, its final performance and possible subsequent implications for authorities in terms of the definition of policies. This Special Issue is proposed by three Guest Editors, and will collect the contributions of other LIFE and H2020 projects aimed at the design and implementation of intelligent acoustic sensor networks, with a focus on the publication of good practices for the design and deployment of intelligent networks in other locations.

Dr. Rosa Ma Alsina-Pagès
Dr. Patrizia Bellucci
Dr. Giovanni Zambon
Guest Editors

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Keywords

smart cities
noise monitoring
acoustic sensor design
noise mapping
acoustic event detection
map generation
public information
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Published Papers (12 papers)

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Editorial

Jump to: Research, Review

4 pages, 173 KiB

Open AccessEditorial

Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities

by Rosa Ma Alsina-Pagès, Patrizia Bellucci and Giovanni Zambon

Sensors 2020, 20(17), 4765; https://doi.org/10.3390/s20174765 - 23 Aug 2020

Cited by 3 | Viewed by 2854

Abstract

This Special Issue is focused on all the technologies necessary for the development of an efficient wireless acoustic sensor network, from the first stages of its design to the tests conducted during deployment; its final performance; and possible subsequent implications for authorities in terms of the definition of policies. This Special Issue collects the contributions of several LIFE and H2020 projects aimed at the design and implementation of intelligent acoustic sensor networks, with a focus on the publication of good practices for the design and deployment of intelligent networks in any locations. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

Research

Jump to: Editorial, Review

19 pages, 1782 KiB

Open AccessArticle

Characterization of Noise Level Inside a Vehicle under Different Conditions

by Daniel Flor, Danilo Pena, Luan Pena, Vicente A. de Sousa, Jr. and Allan Martins

Sensors 2020, 20(9), 2471; https://doi.org/10.3390/s20092471 - 27 Apr 2020

Cited by 17 | Viewed by 4329

Abstract

Vehicular acoustic noise evaluations are a concern of researchers due to health and comfort effects on humans and are fundamental for anyone interested in mitigating audio noise. This paper focuses on the evaluation of the noise level inside a vehicle by using statistical tools. First, an experimental setup was developed with microphones and a microcomputer located strategically on the car’s panel, and measurements were carried out with different conditions such as car window position, rain, traffic, and car speed. Regression analysis was performed to evaluate the similarity of the noise level from those conditions. Thus, we were able to discuss the relevance of the variables that contribute to the noise level inside a car. Finally, our results revealed that the car speed is strongly correlated to interior noise levels, suggesting the most relevant noise sources are in the vehicle itself. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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16 pages, 2078 KiB

Open AccessArticle

Stabilization of a p-u Sensor Mounted on a Vehicle for Measuring the Acoustic Impedance of Road Surfaces

by Francesco Bianco, Luca Fredianelli, Fabio Lo Castro, Paolo Gagliardi, Francesco Fidecaro and Gaetano Licitra

Sensors 2020, 20(5), 1239; https://doi.org/10.3390/s20051239 - 25 Feb 2020

Cited by 36 | Viewed by 4189

Abstract

The knowledge of the acoustic impedance of a material allows for the calculation of its acoustic absorption. Impedance can also be linked to structural and physical proprieties of materials. However, while the impedance of pavement samples in laboratory conditions can usually be measured with high accuracy using devices such as the impedance tube, complete in-situ evaluation results are less accurate than the laboratory results and is so time consuming that a full scale implementation of in-situ evaluations is practically impossible. Such a system could provide information on the homogeneity and the correct laying of an installation, which is proven to be directly linked to its acoustic emission properties. The present work studies the development of a measurement instrument which can be fastened through holding elements to a moving laboratory (i.e., a vehicle). This device overcomes the issues that afflict traditional in-situ measurements, such as the impossibility to perform a continuous spatial characterization of a given pavement in order to yield a direct evaluation of the surface’s quality. The instrumentation has been uncoupled from the vehicle’s frame with a system including a Proportional Integral Derivative (PID) controller, studied to maintain the system at a fixed distance from the ground and to reduce damping. The stabilization of this device and the measurement system itself are evaluated and compared to the traditional one. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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16 pages, 692 KiB

Open AccessArticle

Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network

by Juan M. Navarro, Raquel Martínez-España, Andrés Bueno-Crespo, Ramón Martínez and José M. Cecilia

Sensors 2020, 20(3), 903; https://doi.org/10.3390/s20030903 - 7 Feb 2020

Cited by 16 | Viewed by 4234

Abstract

Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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12 pages, 2191 KiB

Open AccessArticle

EAgLE: Equivalent Acoustic Level Estimator Proposal

by Claudio Guarnaccia

Sensors 2020, 20(3), 701; https://doi.org/10.3390/s20030701 - 27 Jan 2020

Cited by 14 | Viewed by 2847

Abstract

Road infrastructures represent a key point in the development of smart cities. In any case, the environmental impact of road traffic should be carefully assessed. Acoustic noise is one of the most important issues to be monitored by means of sound level measurements. When a large measurement campaign is not possible, road traffic noise predictive models (RTNMs) can be used. Standard RTNMs present in literature usually require in input several information about the traffic, such as flows of vehicles, percentage of heavy vehicles, average speed, etc. Many times, the lack of information about this large set of inputs is a limitation to the application of predictive models on a large scale. In this paper, a new methodology, easy to be implemented in a sensor concept, based on video processing and object detection tools, is proposed: the Equivalent Acoustic Level Estimator (EAgLE). The input parameters of EAgLE are detected analyzing video images of the area under study. Once the number of vehicles, the typology (light or heavy vehicle), and the speeds are recorded, the sound power level of each vehicle is computed, according to the EU recommended standard model (CNOSSOS-EU), and the Sound Exposure Level (SEL) of each transit is estimated at the receiver. Finally, summing up the contributions of all the vehicles, the continuous equivalent level, L_eq, on a given time range can be assessed. A preliminary test of the EAgLE technique is proposed in this paper on two sample measurements performed in proximity of an Italian highway. The results will show excellent performances in terms of agreement with the measured L_eq and comparing with other RTNMs. These satisfying results, once confirmed by a larger validation test, will open the way to the development of a dedicated sensor, embedding the EAgLE model, with possible interesting applications in smart cities and road infrastructures monitoring. These sites, in fact, are often equipped (or can be equipped) with a network of monitoring video cameras for safety purposes or for fining/tolling, that, once the model is properly calibrated and validated, can be turned in a large scale network of noise estimators. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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21 pages, 3484 KiB

Open AccessArticle

Aggregate Impact of Anomalous Noise Events on the WASN-Based Computation of Road Traffic Noise Levels in Urban and Suburban Environments

by Francesc Alías, Ferran Orga, Rosa Ma Alsina-Pagès and Joan Claudi Socoró

Sensors 2020, 20(3), 609; https://doi.org/10.3390/s20030609 - 22 Jan 2020

Cited by 12 | Viewed by 3819

Abstract

Environmental noise can be defined as the accumulation of noise pollution caused by sounds generated by outdoor human activities, Road Traffic Noise (RTN) being the main source in urban and suburban areas. To address the negative effects of environmental noise on public health, the European Environmental Noise Directive requires EU member states to tailor noise maps and define the corresponding action plans every five years for major agglomerations and key infrastructures. Noise maps have been hitherto created from expert-based measurements, after cleaning the recorded acoustic data of undesired acoustic events, or Anomalous Noise Events (ANEs). In recent years, Wireless Acoustic Sensor Networks (WASNs) have become an alternative. However, most of the proposals focus on measuring global noise levels without taking into account the presence of ANEs. The LIFE DYNAMAP project has developed a WASN-based dynamic noise mapping system to analyze the acoustic impact of road infrastructures in real time based solely on RTN levels. After studying the bias caused by individual ANEs on the computation of the A-weighted equivalent noise levels through an expert-based dataset obtained before installing the sensor networks, this work evaluates the aggregate impact of the ANEs on the RTN measurements in a real-operation environment. To that effect, 304 h and 20 min of labeled acoustic data collected through the two WASNs deployed in both pilot areas have been analyzed, computing the individual and aggregate impacts of ANEs for each sensor location and impact range (low, medium and high) for a 5 min integration time. The study shows the regular occurrence of ANEs when monitoring RTN levels in both acoustic environments, which are especially common in the urban area. Moreover, the results reveal that the aggregate contribution of low- and medium-impact ANEs can become as critical as the presence of high-impact individual ANEs, thus highlighting the importance of their automatic removal to obtain reliable WASN-based RTN maps in real-operation environments. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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17 pages, 4989 KiB

Open AccessArticle

A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities

by Juan Manuel López, Jesús Alonso, César Asensio, Ignacio Pavón, Luis Gascó and Guillermo de Arcas

Sensors 2020, 20(3), 605; https://doi.org/10.3390/s20030605 - 22 Jan 2020

Cited by 32 | Viewed by 5470

Abstract

Presently, large cities have significant problems with noise pollution due to human activity. Transportation, economic activities, and leisure activities have an important impact on noise pollution. Acoustic noise monitoring must be done with equipment of high quality. Thus, long-term noise monitoring is a high-cost activity for administrations. For this reason, new alternative technological solutions are being used to reduce the costs of measurement instruments. This article presents a design for a versatile electronic device to measure outdoor noise. This device has been designed according to the technical standards for this type of instrument, which impose strict requirements on both the design and the quality of the device’s measurements. This instrument has been designed under the original equipment manufacturer (OEM) concept, so the microphone–electronics set can be used as a sensor that can be connected to any microprocessor-based device, and therefore can be easily attached to a monitoring network. To validate the instrument’s design, the device has been tested following the regulations of the calibration laboratories for sound level meters (SLM). These tests allowed us to evaluate the behavior of the electronics and the microphone, obtaining different results for these two elements. The results show that the electronics and algorithms implemented fully fit within the requirements of type 1 noise measurement instruments. However, the use of an electret microphone reduces the technical features of the designed instrument, which can only fully fit the requirements of type 2 noise measurement instruments. This situation shows that the microphone is a key element in this kind of instrument and an important element in the overall price. To test the instrument’s quality and show how it can be used for monitoring noise in smart wireless acoustic sensor networks, the designed equipment was connected to a commercial microprocessor board and inserted into the infrastructure of an existing outdoor monitoring network. This allowed us to deploy a low-cost sub-network in the city of Málaga (Spain) to analyze the noise of conflict areas due to high levels of leisure noise. The results obtained with this equipment are also shown. It has been verified that this equipment meets the similar requirements to those obtained for type 2 instruments for measuring outdoor noise. The designed equipment is a two-channel instrument, that simultaneously measures, in real time, 86 sound noise parameters for each channel, such as the equivalent continuous sound level (Leq) (with Z, C, and A frequency weighting), the peak level (with Z, C, and A frequency weighting), the maximum and minimum levels (with Z, C, and A frequency weighting), and the impulse, fast, and slow time weighting; seven percentiles (1%, 5%, 10%, 50%, 90%, 95%, and 99%); as well as continuous equivalent sound pressure levels in the one-third octave and octave frequency bands. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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25 pages, 7519 KiB

Open AccessEditor’s ChoiceArticle

Accuracy of the Dynamic Acoustic Map in a Large City Generated by Fixed Monitoring Units

by Roberto Benocci, Chiara Confalonieri, Hector Eduardo Roman, Fabio Angelini and Giovanni Zambon

Sensors 2020, 20(2), 412; https://doi.org/10.3390/s20020412 - 11 Jan 2020

Cited by 27 | Viewed by 3591

Abstract

DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in two pilot areas located in the agglomeration of Milan (Italy) and along the Motorway A90 (Rome-Italy). The paper reports the final assessment of the system installed in the pilot area of Milan. Traffic noise data collected by the monitoring stations, each one representative of a number of roads (groups) sharing similar characteristics (e.g., daily traffic flow), are used to build-up a “real-time” noise map. In particular, we focused on the results of the testing campaign (21 sites distributed over the pilot area and 24 h duration of each recording). It allowed evaluating the accuracy and reliability of the system by comparing the predicted noise level of DYNAMAP with field measurements in randomly selected sites. To this end, a statistical analysis has been implemented to determine the error associated with such prediction, and to optimize the system by developing a correction procedure aimed at keeping the error below some acceptable threshold. The steps and the results of this procedure are given in detail. It is shown that it is possible to describe a complex road network on the basis of a statistical approach, complemented by empirical data, within a threshold of 3 dB provided that the traffic flow model achieves a comparable accuracy within each single groups of roads in the network. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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23 pages, 12837 KiB

Open AccessArticle

Wireless Acoustic Sensor Nodes for Noise Monitoring in the City of Linares (Jaén)

by Jose-Angel Fernandez-Prieto, Joaquín Cañada-Bago and Manuel-Angel Gadeo-Martos

Sensors 2020, 20(1), 124; https://doi.org/10.3390/s20010124 - 24 Dec 2019

Cited by 17 | Viewed by 4762

Abstract

Noise pollution is a problem that affects millions of people worldwide. Over the last few years, many researchers have devoted their attention to the design of wireless acoustic sensor networks (WASNs) to monitor the real data of continuous and precise noise levels and to create noise maps in real time and space. Although WASNs are becoming a reality in smart cities, some research studies argue that very few projects have been deployed around the world, with most of them deployed as pilots for only days or weeks, with a small number of nodes. In this paper, we describe the design and implementation of a complete system for a WASN deployed in the city of Linares (Jaén), Spain, which has been running continuously for ten months. The complete system covers the network topology design, hardware and software of the sensor nodes, protocols, and a private cloud web server platform. As a result, the information provided by the system for each location where the sensor nodes are deployed is as follows: L_Aeq for a given period of time; noise indicators L_den, L_day, L_evening, and L_night; percentile noise levels (L_A01T, L_A10T, L_A50T, L_A90T, and L_A99T); a temporal evolution representation of noise levels; and the predominant frequency of the noise. Some comparisons have been made between the noise indicators calculated by the sensor nodes and those from a commercial sound level meter. The results suggest that the proposed system is perfectly suitable for use as a starting point to obtain accurate maps of the noise levels in smart cities. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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15 pages, 5818 KiB

Open AccessArticle

Application of the Intermittency Ratio Metric for the Classification of Urban Sites Based on Road Traffic Noise Events

by Giovanni Brambilla, Chiara Confalonieri and Roberto Benocci

Sensors 2019, 19(23), 5136; https://doi.org/10.3390/s19235136 - 23 Nov 2019

Cited by 19 | Viewed by 2960

Abstract

Human hearing adapts to steady signals, but remains very sensitive to fluctuations as well as to prominent, salient noise events. The higher these fluctuations are, the more annoying a sound is possibly perceived. To quantify these fluctuations, descriptors have been proposed in the literature and, among these, the intermittency ratio (IR) has been formulated to quantify the eventfulness of an exposure from transportation noise. This paper deals with the application of IR to urban road traffic noise data, collected in terms of 1 s A-weighted sound pressure level (SPL), without being attended, monitored continuously for 24 h in 90 sites in the city of Milan. IR was computed on each hourly data of the 251 time series available (lasting 24 h each), including different types of roads, from motorways to local roads with low traffic flow. The obtained hourly IR values have been processed by clustering methods to extract the most significant temporal pattern features of IR in order to figure out a criterion to classify the urban sites taking into account road traffic noise events, which potentially increase annoyance. Two clusters have been obtained and a “non-acoustic” parameter x, determined by combination of the traffic flow rate in three hourly intervals, has allowed to associate each site with the cluster membership. The described methodology could be fruitfully applied on road traffic noise data in other cities. Moreover, to have a more detailed characterization of noise exposure, IR, describing SPL short-term temporal variations, has proved to be a useful supplementary metric accompanying L_Aeq, which is limited to measure the energy content of the noise exposure. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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22 pages, 36141 KiB

Open AccessArticle

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

by Adam Glowacz

Sensors 2019, 19(2), 269; https://doi.org/10.3390/s19020269 - 11 Jan 2019

Cited by 76 | Viewed by 6030

Abstract

Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes—TE_D = 96%, TE_CG-A = 97%, TE_CG-B = 100%). Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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Review

Jump to: Editorial, Research

31 pages, 387 KiB

Open AccessReview

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

by Judicaël Picaut, Arnaud Can, Nicolas Fortin, Jeremy Ardouin and Mathieu Lagrange

Sensors 2020, 20(8), 2256; https://doi.org/10.3390/s20082256 - 16 Apr 2020

Cited by 44 | Viewed by 10367

Abstract

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. Full article

(This article belongs to the Special Issue Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities)

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