Abstract
The paper presents a concept of an air pollution monitoring system with prediction abilities, based on wireless smart sensors, that takes into account local conditions (microclimate) prevailing in particular areas of the city. In most cases reported in the literature, artificial neural networks (ANNs) are used to predict future pollution levels. In existing solutions of this type, ANNs are trained with generalized datasets common for larger areas, e.g., cities. Our investigations show, however, that conditions may strongly differ even between particular streets in the city, which may impact prediction quality. This results from varying density of urban development, different levels of insolation, airiness, amounts of greenery, etc. As a result, with similar values of ANN input signals, such as current pollution levels, temperature, pressure, etc., the results of the prediction may differ significantly from reality. For this reason, we propose an innovative solution, in which particular sensors are equipped with miniaturized low-power ANNs, trained with datasets gathered directly from their closest environment, without a need for the obtaining of such data from a base station. This may simplify the installation and maintenance process of a network of such sensors. In a further part of this work, we dealt with solutions that enable the reduction of the computational complexity of ANNs in the case of their implementation on specialized integrated circuits. We propose replacing the most complex mathematical operations used in the learning algorithm with simpler solutions. A prototype chip containing the main blocks of such an ANN was also designed.
Highlights
One of significant problems in urban areas is a growing number of traffic-related emissions [1], which may harm air quality and impact human health
We present a concept of an air pollution monitoring system with the possibility of predicting pollution levels in a specific time horizon
We present a solution in which such individual training sets are created automatically, without the need to contact the base station of the wireless sensor network (WSN)
Summary
One of significant problems in urban areas is a growing number of traffic-related emissions [1], which may harm air quality and impact human health. From a technical point of view, the process of pollution monitoring is a relatively straightforward task. It needs a sensor network installed in a given area. Particular sensors in such a network operate in parallel, measuring levels of selected pollutants and other relevant parameters. The sensors send the readings to a base station for further processing. On the basis of gathered data, the base station creates a Remote Sens.
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