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This article presents a fully differential tunable high-gain transimpedance amplifier (TIA) conceived as a front-end circuit for monolithic CMOS-MEMS resonators operating in self-sustained oscillation mode. The proposed solution is based on a capacitive-feedback network topology by means of a varactor for gain control. The design is specifically oriented to CMOS-MEMS biosensing applications that require post-CMOS MEMS processing since the adoption of a TIA solution provides robustness against one of such processing steps (i.e., oxygen plasma activation) and a $10\times $ improvement in feedthrough signal elimination compared to a single-ended alternative. The ASIC was fabricated using a 0.35- $\mu $ m commercial CMOS technology from Austria Microsystems featuring a maximum transimpedance gain of 164 dB $\Omega $ with a 10-dB tuning range. It operates at frequencies up to 10 MHz with an input-referred current noise density as low as 13 fA $\cdot $ Hz $^{\mathrm {-1/2}}$ providing an exceptional high-performance sensing. With a 3.3 V supply voltage, it exhibits a 1.6 mW power consumption. The TIA was integrated on-chip with various MEMS resonator topologies, either oriented to volatile organic compounds (VOCs) detection or to microfluidics integration for Lab-on-Chip (LoC) systems, corroborating self-sustained oscillation with a 135-ppb measured Allan deviation and 1.1 V output voltage swing.

With the emergence of containerization 10 years ago, we saw a compact, convenient and portable way of running apps directly concurrently with virtualization. The major difference is in the architecture. Containers share the same kernel as the guest and then do not virtualize low-layer components like the Central Processing Unit (CPU). On the one hand, they are lighter and more flexible than virtual machines (VMs). On the other hand, VMs can more precisely meet the low-layer needs and are completely autonomous systems. Nowadays, what is the best architecture to use to develop an application? In this paper, we will study the two main virtual methods of deploying this. We will compare both methods on several criteria: compatibility based on user experience and the ease of installation/deployment, scalability based on the automatic elasticity facing the workload and energy efficiency in terms of energy and computer resources. After the tests, we conclude that the containerization option is the most ecologically advantageous option in terms of energy consumption.

Identification of different species of animals has become an important issue in biology and ecology. Ornithology has made alliances with other disciplines in order to establish a set of methods that play an important role in the birds’ protection and the evaluation of the environmental quality of different ecosystems. In this case, the use of machine learning and deep learning techniques has produced big progress in birdsong identification. To make an approach from AI-IoT, we have used different approaches based on image feature comparison (through CNNs trained with Imagenet weights, such as EfficientNet or MobileNet) using the feature spectrogram for the birdsong, but also the use of the deep CNN (DCNN) has shown good performance for birdsong classification for reduction of the model size. A 5G IoT-based system for raw audio gathering has been developed, and different CNNs have been tested for bird identification from audio recordings. This comparison shows that Imagenet-weighted CNN shows a relatively high performance for most species, achieving 75% accuracy. However, this network contains a large number of parameters, leading to a less energy efficient inference. We have designed two DCNNs to reduce the amount of parameters, to keep the accuracy at a certain level, and to allow their integration into a small board computer (SBC) or a microcontroller unit (MCU).

Abstract Urban parks constitute one of the main leisure areas, especially for the most vulnerable people in our society, children, and the elderly. Contact with soils can pose a health risk. Microbiological testing is a key aspect in determining whether they are suitable for public use. The aim of this work is to map the spatial distribution of potential dangerous Enterobacteria but also bioremediation useful (lipase producers) isolates from soils in an urban park in the area of Valencia (Spain). To this end, our team has collected 25 samples of soil and isolated 500 microorganisms, using a mobile application to collect information of the soil samples (i.e. soil features, temperature, humidity, etc.) with geolocation. A combined protocol including matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and 16S rDNA sequencing PCR has been established to characterize the isolates. The results have been processed using spatial statistical techniques (using Kriging method), taking into account the number of isolated strains, also proving the reactivity against standard pathogenic bacterial strains (Escherichia coli, Bacillus cereus, Salmonella, Pseudomonas and Staphylococcus aureus), and have increased the number of samples (to 896 samples) by interpolating spatially each parameter with this statistical method. The combined use of methods from biology and computer science allows the quality of the soil in urban parks to be predicted in an agile way, which can generate confidence in its use by citizens.

5G communications allow the integration of different wireless technologies to exploit the added value of multihoming scenarios. Furthermore, in connection with the Internet of Things (IoT), multi-homed nodes are very interesting, since it allows one to address new challenges and adapt the usage of communication technologies to new requirements imposed by the location of each deployment, the available energy resources on each node, and the IoT application itself. There are available commercial wireless nodes that embed and handle multiple interfaces. Terms such as seamless and transparent communications as well as technology integration fit well within the 5G standards. However, in practice, these wireless nodes are basically relayed in one of these wireless technologies when connected, and in the case of using other interfaces, their usage is not coordinated. In this context, we propose and implement a novel and efficient heterogeneous interface selection algorithm for multihoming and integrative communications using commercial products by performing local decisions on a per-packet basis, analysing different issues and approaches from a practical point of view, both hardware and software. We compare our proposal with other alternatives, highlighting the opportunities by exploiting these multihoming features in 5G-enabled IoT nodes.

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects.

The proliferation and great variety of low-cost air quality (AQ) sensors, combined with their flexibility and energy efficiency, gives an opportunity to integrate them into Wireless Sensor Networks (WSN). However, with these sensors, AQ monitoring poses a significant challenge, as the data collection and analysis process is complex and prone to errors. Although these sensors do not meet the performance requirements for reference regulatory-equivalent monitoring, they can provide informative measurements and more if we can adjust and add further processing to their raw measurements. Therefore, the integration of these sensors aims to facilitate real-time monitoring and achieve a higher spatial and temporal sampling density, particularly in urban areas, where there is a strong interest in providing AQ surveillance services since there is an increase in respiratory/allergic issues among the population. Leveraging a network of low-cost sensors, supported by 5G communications in combination with Artificial Intelligence (AI) techniques (using Convolutional and Deep Neural Networks (CNN and DNN)) to predict 24-h-ahead readings is the goal of this article in order to be able to provide early warnings to the populations of hazards areas. We have evaluated four different neural network architectures: Multi-Linear prediction (with a dense Multi-Linear Neural Network (NN)), Multi-Dense network prediction, Multi-Convolutional network prediction, and Multi-Long Short-Term Memory (LSTM) network prediction. To perform the training of the prediction of the readings, we have prepared a significant dataset that is analyzed and processed for training and testing, achieving an estimation error for most of the predicted parameters of around 7.2% on average, with the best option being the Multi-LSTM network in the forthcoming 24 h. It is worth mentioning that some pollutants achieved lower estimation errors, such as CO2 with 0.1%, PM10 with 2.4% (as well as PM2.5 and PM1.0), and NO2 with 6.7%.

There is clear evidence of the effects of air pollution on health. In this paper, we present an innovative application designed to assess Air Quality (AQ) exposure based on the World Health Organization’s AQ Guidelines, analysing pollutants and their concentrations independently. Our aim is to provide this information to citizens based on their health profile (medical history or requirements) before and during outdoor trips of their choice, both walking and cycling, empowering them to proactively make informed personal decisions about their route choices and identifying potentially unhealthy travel environments. For this purpose, we have access to official data from AQ monitoring stations that are updated periodically every 10 min. Then, by using spatial interpolation techniques (with Ordinary Kriging), we estimate each pollutant over a grid superimposed on the city map. Once the pollutants have been mapped on each route, they are analysed in order to consider the different alternatives for deciding and planning changes in speed or trajectory. We evaluated the application in the city of Valencia (Spain) as a use case under different scenarios, and showed the results to assess exposure to pollution on the routes of citizens.

The increase and diversity of low-cost air quality (AQ) sensors, as well as their flexibility and low power consumption, offers us the opportunity to integrate them into broad AQ wireless sensor networks, with the aim of enabling real-time monitoring and higher spatial sampling density of pollution in all parts of cities. Considering that the vast majority of the population lives in cities and the increase in respiratory/allergic problems in a large part of the population, it is of great interest to offer services and applications to improve their quality of life by avoiding pollution exposure in their movements in the open air. In the ECO4RUPA project, we focus on this kind of service, proposing an inclusive and intelligent routing ecosystem carried out using a network of low-cost AQ sensors with the support of 5G communications along with official AQ monitoring stations, using spatial interpolation techniques to enhance its spatial resolution. The goal of this service is to calculate healthy walking and/or cycling routes according to the particular citizen’s profile and needs. We provide and analyse the results of the proposed route planner under different scenarios (different timetables, congestion road traffic, and routes) and different user profiles, with a special interest in citizens with asthma and pregnant women, since both have special needs. In summary, our approach can lead to an approximately average reduction in pollution exposure of 17.82% while experiencing an approximately average increase in distance travelled of 9.8%.

Urban parks are a rich source of microbial diversity, as they are heavily used by city residents. In this study, we sampled a Mediterranean park and were able to isolate bacteria that have the ability to inhibit the growth of control microorganisms. Out of the 560 bacteria we tested, many displayed antibacterial activity, particularly against Salmonella sp. and K. pneumoniae. These results suggest that the microorganisms in the park are in close proximity to the human population. Additionally, the isolated bacteria demonstrated diverse enzymatic activities, possibly as a response to the environmental substances available to them, which could aid in the degradation of different compounds of interest. The study of the spatial distribution of soil parameters and the inhibition against relative-safe pathogens in an urban park in València (Spain) demonstrated a higher proportion of isolates in certain areas. These spatial data maps can help researchers understand the behaviors of bacterial populations on a regional level, which can assist in the creation of novel antimicrobial agents and promote advancements in public health.

Aquaponic health is a very important in the food industry field, as currently there is a huge amount of fishing farms, and the demands are growing in the whole world. This work examines the process of developing an innovative aquaponics health monitoring system that incorporates high-tech back-end innovation sensors to examine fish and crop health and a data analytics framework with a low-tech front-end approach to feedback actions to farmers. The developed system improves the state-of-the-art in terms of aquaponics life cycle monitoring metrics and communication technologies, and the energy consumption has been reduced to make a sustainable system.

Antibiotic misuse is a public health problem due to the appearance of resistant strains in almost all human pathogens, making infectious diseases more difficult to treat. The search for solutions requires the development of new antimicrobials as well as novel strategies, including increasing social awareness of the problem. The Small World Initiative (SWI) and the Tiny Earth (TE) network are citizen science programs pursuing the discovery of new antibiotics from soil samples and the promotion of scientific culture. Both programs aim to bring scientific culture and microbiological research closer to pre-university students through a crowdsourcing strategy and a Service Learning (SL) educational approach, with a 2-fold objective: to encourage students to pursue careers in science and to involve them in the discovery of soil microorganisms producing new antimicrobials. SWI and TE projects were put into practice in Spain under the common name MicroMundo. MicroMundo@Valencia was implemented at the Universitat de València (UV) during the academic years 2017–2018 and 2018–2019. It trained 140 university students to disseminate this initiative into 23 high/secondary schools, and one primary school, involving about 900 people (teachers and students) as researchers. A total of 7,002 bacterial isolates were obtained from 366 soil samples and tested for antibiosis at UV and high/secondary school centers. About 1 or 7% of them produced inhibition halos for the Escherichia coli or Bacillus cereus target strains, respectively. Geolocation of sampling sites by an application developed ad hoc and Kriging analysis also allowed detection of soil foci of antibiotic-producing bacteria. Evaluation of the project by university, high/secondary, and primary school students revealed their strong positive perception and their increased interest in science, as a consequence of acquiring new scientific and pedagogical concepts and skills that they were able to pass on to other classmates, younger students, or relatives. To further expand the dissemination of the project in the Valencian Community, diverse extramural activities deemed to include a gender perspective and aimed at different age groups, were also carried out, obtaining very satisfactory results, increasing sensitivity and awareness to the global antibiotic crisis.

The acoustic evaluation of indoor environments is common in the application of virtual acoustics. In addition, in the study of cultural heritage buildings, it is a useful tool, but it is not so common when describing the acoustic environment in intangible cultural heritage events and even in outdoor environments. In this paper, the acoustic environment of the Water Tribunal of the Plain of Valencia (Spain) is studied. It is analyzed from a soundscape perspective, characterizing the sound source and evaluating it within relation to the environment and evaluating its subjective response. With the research carried out, it has been obtained, on the one hand, a complete study of the acoustics of the environment of the Water Tribunal and, on the other, an enhancement of the Valencian tangible and intangible heritage.

In recent years, Wireless Acoustic Sensor Networks (WASN) have been widely applied to different acoustic fields in outdoor and indoor environments. Most of these applications are oriented to locate or identify sources and measure specific features of the environment involved. In this paper, we study the application of a WASN for room acoustic measurements. To evaluate the acoustic characteristics, a set of Raspberry Pi 3 (RPi) has been used. One is used to play different acoustic signals and four are used to record at different points in the room simultaneously. The signals are sent wirelessly to a computer connected to a server, where using MATLAB we calculate both the impulse response (IR), and different acoustic parameters, such as the Speech Intelligibility Index (SII). In this way, the evaluation of room acoustic parameters with asynchronous IR measurements two different applications has been explored. Finally, the network features have been evaluated to assess the effectiveness of this system.

CMOS-MEMS resonators have become a promising solution thanks to their miniaturization and on-chip integration capabilities. However, using a CMOS technology to fabricate microelectromechanical system (MEMS) devices limits the electromechanical performance otherwise achieved by specific technologies, requiring a challenging readout circuitry. This paper presents a transimpedance amplifier (TIA) fabricated using a commercial 0.35-µm CMOS technology specifically oriented to drive and sense monolithically integrated CMOS-MEMS resonators up to 50 MHz with a tunable transimpedance gain ranging from 112 dB to 121 dB. The output voltage noise is as low as 225 nV/Hz1/2—input-referred current noise of 192 fA/Hz1/2—at 10 MHz, and the power consumption is kept below 1-mW. In addition, the TIA amplifier exhibits an open-loop gain independent of the parasitic input capacitance—mostly associated with the MEMS layout—representing an advantage in MEMS testing compared to other alternatives such as Pierce oscillator schemes. The work presented includes the characterization of three types of MEMS resonators that have been fabricated and experimentally characterized both in open-loop and self-sustained configurations using the integrated TIA amplifier. The experimental characterization includes an accurate extraction of the electromechanical parameters for the three fabricated structures that enables an accurate MEMS-CMOS circuitry co-design.

Based on experimental data, this paper thoroughly investigates the impact of a gas fluid flow on the behavior of a MEMS resonator specifically oriented to gas sensing. It is demonstrated that the gas stream action itself modifies the device resonance frequency in a way that depends on the resonator clamp shape with a corresponding non-negligible impact on the gravimetric sensor resolution. Results indicate that such an effect must be accounted when designing MEMS resonators with potential applications in the detection of volatile organic compounds (VOCs). In addition, the impact of thermal perturbations was also investigated. Two types of four-anchored CMOS-MEMS plate resonators were designed and fabricated: one with straight anchors, while the other was sustained through folded flexure clamps. The mechanical structures were monolithically integrated together with an embedded readout amplifier to operate as a self-sustained fully integrated oscillator on a commercial CMOS technology, featuring low-cost batch production and easy integration. The folded flexure anchor resonator provided a flow impact reduction of 5× compared to the straight anchor resonator, while the temperature sensitivity was enhanced to −115 ppm/°C, an outstanding result compared to the −2403 ppm/°C measured for the straight anchored structure.

Although much work has been done since wireless sensor networks appeared, there is not a great deal of information available on real deployments that incorporate basic features associated with these networks, in particular multihop routing and long lifetimes features. In this article, an environmental monitoring application (Internet of Things oriented) is described, where temperature and relative humidity samples are taken by each mote at a rate of 2 samples/min and sent to a sink using multihop routing. Our goal is to analyse the different strategies to gather the information from the different motes in this context. The trade-offs between ‘sending always’ and ‘buffering locally’ approaches were analysed and validated experimentally, taking into account power consumption, lifetime, efficiency and reliability. When buffering locally, different options were considered such as saving in either local RAM or FLASH memory, as well different alternatives to reduce overhead with different packet sizes. The conclusion is that in terms of energy and durability, the best option is to reduce the overhead. Nevertheless, sending larger packets is not worthy when the probability of retransmission is high. If real-time monitoring is required, then sending always is better than buffering locally.

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.

The Ando-Beranek’s model, a linear version of Ando’s subjective preference theory, obtained by the authors in a recent work, was combined with Barron revised theory. An optimal volume region for each reverberation time was obtained for classical music in symphony orchestra concert halls. The obtained relation was tested with good agreement with the top rated halls reported by Beranek and other halls with reported anomalies.

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices.

Micro and nanoelectromechanical system (M/NEMS) devices constitute key technological building blocks to enable increased additional functionalities within integrated circuits (ICs) in the More-Than-Moore era, as described in the International Technology Roadmap for Semiconductors [...]

This paper presents the design, fabrication, and electrical characterization of an electrostatically actuated and capacitive sensed 2-MHz plate resonator structure that exhibits a predicted mass sensitivity of ~250 pg·cm−2·Hz−1. The resonator is embedded in a fully on-chip Pierce oscillator scheme, thus obtaining a quasi-digital output sensor with a short-term frequency stability of 1.2 Hz (0.63 ppm) in air conditions, corresponding to an equivalent mass noise floor as low as 300 pg·cm−2. The monolithic CMOS-MEMS sensor device is fabricated using a commercial 0.35-μm 2-poly-4-metal complementary metal-oxide-semiconductor (CMOS) process, thus featuring low cost, batch production, fast turnaround time, and an easy platform for prototyping distributed mass sensors with unprecedented mass resolution for this kind of devices.

We analyzed experimentally the noise characteristics of fully integrated CMOS-MEMS resonators to determine the overall thermomechanical noise and its impact on the limit of detection at the system level. Measurements from four MEMS resonator geometries designed for ultrasensitive detection operating between 2-MHz and 8-MHz monolithically integrated with a low-noise CMOS capacitive readout circuit were analyzed and used to determine the resolution achieved in terms of displacement and capacitance variation. The CMOS-MEMS system provides unprecedented detection resolution of 11 yF·Hz−1/2 equivalent to a minimum detectable displacement (MDD) of 13 fm·Hz−1/2, enabling noise characterization that is experimentally demonstrated by thermomechanical noise detection and compared to theoretical model values.

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system.

The electronic nose (e-nose) detects volatile organic compounds (VOCs) in exhaled air. We hypothesized that the exhaled VOCs print is different in stable vs. exacerbated patients with chronic obstructive pulmonary disease (COPD), particularly if the latter is associated with airway bacterial infection, and that the e-nose can distinguish them.Smell-prints of the bacteria most commonly involved in exacerbations of COPD (ECOPD) were identified in vitro. Subsequently, we tested our hypothesis in 93 patients with ECOPD, 19 of them with pneumonia, 50 with stable COPD and 30 healthy controls in a cross-sectional case-controlled study. Secondly, ECOPD patients were re-studied after 2 months if clinically stable. Exhaled air was collected within a Tedlar bag and processed by a Cynarose 320 e-nose. Breath-prints were analyzed by Linear Discriminant Analysis (LDA) with "One Out" technique and Sensor logic Relations (SLR). Sputum samples were collected for culture.ECOPD with evidence of infection were significantly distinguishable from non-infected ECOPD (p = 0.018), with better accuracy when ECOPD was associated to pneumonia. The same patients with ECOPD were significantly distinguishable from stable COPD during follow-up (p = 0.018), unless the patient was colonized. Additionally, breath-prints from COPD patients were significantly distinguished from healthy controls. Various bacteria species were identified in culture but the e-nose was unable to identify accurately the bacteria smell-print in infected patients.E-nose can identify ECOPD, especially if associated with airway bacterial infection or pneumonia.

Wireless Sensor Networks (WSNs) are composed of spatially distributed autonomous sensor devices, named motes. These motes have their own power supply, processing unit, sensors and wireless communications However with many constraints, such as limited energy, bandwidth and computational capabilities. In these networks, at least one mote called a sink, acts as a gateway to connect with other networks. These sensor networks run monitoring applications and then the data gathered by these motes needs to be retrieved by the sink. When this sink is located in the far field, there have been many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications to reach the sink. In this paper, we conduct a thorough study of the related work and analyze the requirements to do CB. In order to implement these communications in real scenarios, we will consider if these requirements and the assumptions made are feasible from the point of view of commercial motes and their constraints. In addition, we will go a step further and will consider different alternatives, by relaxing these requirements, trying to find feasible assumptions to carry out these types of communications with commercial motes. This research considers the nonavailability of a central clock that synchronizes all motes in the WSN, and all motes have identical hardware. This is a feasibility study to do CB on WSN, using a simulated scenario with randomized delays obtained from experimental data from commercial motes.

EU Directive 49/2002 and Spanish law 37/2006 urge cities to develop strategic noise maps and action plans to evaluate noise exposure and to establish noise abatement procedures in critical areas. However, noise mapping involves costly and cumbersome measurement procedures that can become a real issue in practice. This paper describes a distributed noise monitoring system based on WASN (Wireless Acoustic Sensor Network) and the application of a geo-statistical methodology for statistical spatial-temporal prediction of noise levels in semi-open areas, such as a typical, small Mediterranean city (Algemesí, València, Spain). This methodology is applied to the study of the spatial evolution in time of the noise pollution. To this end, a spatial statistical model is developed by using the noise pollution measurements obtained over a set of points located at some strategic locations. The geo-statistical time model allows for estimating specific noise levels and characterizing the spatial-temporal variation of the noise pollution. The results show that the developed model provides a good approximation of the measurements obtained experimentally.