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.
The deteriorating water environment demands new approaches and technologies to achieve sustainable and smart management of urban water systems. Wireless sensor networks represent a promising technology for water quality monitoring and management. The use of wireless sensor networks facilitates the improvement of current centralized systems and traditional manual methods, leading to decentralized smart water quality monitoring systems adaptable to the dynamic and heterogeneous water distribution infrastructure of cities. However, there is a need for a low-cost wireless sensor node solution on the market that enables a cost-effective deployment of this new generation of systems. This paper presents the integration to a wireless sensor network and a preliminary validation in a wastewater treatment plant scenario of a low-cost water quality monitoring device in the close-to-market stage. This device consists of a nitrate and nitrite analyzer based on a novel ion chromatography detection method. The analytical device is integrated using an Internet of Things software platform and tested under real conditions. By doing so, a decentralized smart water quality monitoring system that is conceived and developed for water quality monitoring and management is accomplished. In the presented scenario, such a system allows online near-real-time communication with several devices deployed in multiple water treatment plants and provides preventive and data analytics mechanisms to support decision making. The results obtained comparing laboratory and device measured data demonstrate the reliability of the system and the analytical method implemented in the device.
Abstract:The concept of Smart Cities and the monitoring of environmental parameters is an area of research that has attracted scientific attention during the last decade. These environmental parameters are well-known as important factors in their affection towards people. Massive monitoring of this kind of parameters in cities is an expensive and complex task. Recent technologies of low-cost computing and low-power devices have opened researchers to a wide and more accessible research field, developing monitoring devices for deploying Wireless Sensor Networks. Gathering information from them, improved urban plans could be carried out and the information could help citizens. In this work, the prototyping of a low-cost acoustic sensor based on the Raspberry Pi platform for its use in the analysis of the sound field is described. The device is also connected to the cloud to share results in real time. The computation resources of the Raspberry Pi allow treating high quality audio for calculating acoustic parameters. A pilot test was carried out with the installation of two acoustic devices in the refurbishment works of a neighbourhood. In this deployment, the evaluation of these devices through long-term measurements was carried out, obtaining several acoustic parameters in real time for its broadcasting and study. This test has shown the Raspberry Pi as a powerful and affordable computing core of a low-cost device, but also the pilot test has served as a query tool for the inhabitants of the neighbourhood to be more aware about the noise in their own place of residence.
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