Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect people's health and children's cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called Leq) to acquite an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of Leq, people can feel the noise differently according to its frequency characteristics. Thus, indexes which can express people's feelings by subjective measures are required. In this paper we analyze the suitability of using the psycho-acoustic metrics given by the Zwicker's model, instead of just only considering Leq. The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwicker's model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, while Tmote-Invent nodes do not confirm their suitablity due to their limited memory and calibration issues.
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.
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.
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.
Wireless Sensor Networks (WSNs) is a group of spatially dispersed autonomous sensor devices, named motes. These motes have a microcontroller, sensors, are powered by AA or AAA batteries, and mainly have the ability to communicate using the IEEE 802.15.4 standard. The motes communicate between them inside the WSN exchanging packets using a multi-hop routing. They use a very low amount of power (below 100 mW). This limits the maximum communication distance between motes within the WSN. Usually, one mote acts as a gateway to other networks and this mote is also called sink or simply Base Station (BS), and the data collected by the sensors of each mote are sent to this mote. The maximum distance between the BS and the nearest mote is below 100 m because of the power limitations of the motes. If the WSN-BS distance is above this boundary, the communication will surely fail. We propose a new technique in order to achieve a long range communication from the WSN, for instance to communicate to a Low Earth Orbit (LEO) satellite. Many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications are found, however the synchronization of clocks is an almost impossible task given the simplicity and cheapness of the architecture of the motes. To overcome this problem, we propose a new technique, named Stochastic Collaborative Beamforming (SCB), in which we take advantage of the synchronization errors of the clocks. In SCB, it is possible to obtain the adequate time delay that permits the interference or sufficient gain in the direction of the receiver. This gain is obtained from interfering independent signals coming from each mote of the WSN, using a repetition scheme. Although it does not get all the nominal gain that could be obtained in case of a perfect synchronization, it does get a sufficient gain to reach the BS with limited power consumption.Electronics 2018, 7, 417 2 of 19 installed and working, they transmit the information provided by the sensors to the Base Station (BS), also known as sink or gateway, which in turn can supply the information to an external server. WSNs thus allow monitoring of physical or environmental conditions, such as temperature, sound, humidity, pressure, noise, movement or pollutants. WSN were initially developed for military applications, such as battlefield surveillance. They are now used in a multitude of industrial and civil applications [2], in industrial process monitoring and control, machine monitoring, environmental parameter monitoring, traffic control, healthcare, and automation and habitat monitoring in home automation applications. These networks can also be deployed in areas of difficult access or under very adverse conditions, such as mountainous areas, jungles, deserts, mines, skyscrapers, etc. The duration of the batteries determines the duration of its correct operation which can be months or years, depending on the sensors consumption and the energy used by the transmissions ...
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