Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO₂ and PM10 particles, with promising results and an achieved $R^2$ of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

In this paper we present a novel method of fusing of the sequences of images obtained from multimodal surveillance cameras and subject to distortions typical for visual sensor networks environment. The proposed fusion method uses the structural similarity measure (SSIM) to measure a level of noise in regions of a received image in order to optimize the selection of regions in the fused image. The region-based image fusion algorithm using the dual-tree complex wavelet transform (DT-CWT) is used to fuse the selected regions. The performance of the proposed method was extensively tested for a number of multimodal surveillance image sequences and proposed method outperformed the state-of-the-art algorithms, increasing significantly the quality of the fused image, both visually and in terms of the Petrovic image fusion metric.

Public air quality monitoring relies on expensive monitoring stations which are highly reliable and accurate but require significant maintenance and cannot be used to form a high spatial resolution measurement grid. Recent technological advances have enabled air quality monitoring that uses low-cost sensors. Being inexpensive and mobile, with wireless transfer support, such devices represent a very promising solution for hybrid sensor networks comprising public monitoring stations supported by many low-cost devices for complementary measurements. However, low-cost sensors can be influenced by weather and degradation, and considering that a spatially dense network would include them in large numbers, logistically adept solutions for low-cost device calibration are essential. In this paper, we investigate the possibility of a data-driven machine learning calibration propagation in a hybrid sensor network consisting of One public monitoring station and ten low-cost devices equipped with NO₂, PM₁₀, relative humidity, and temperature sensors. Our proposed solution relies on calibration propagation through a network of low-cost devices where a calibrated low-cost device is used to calibrate an uncalibrated device. This method has shown an improvement of up to 0.35/0.14 for the Pearson correlation coefficient and a reduction of 6.82 µg/m³/20.56 µg/m³ for the RMSE, for NO₂ and PM₁₀, respectively, showing promise for efficient and inexpensive hybrid sensor air quality monitoring deployments.

Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

Modern cities are densely populated spaces and number of people living in cities is increasing rapidly by years. The air monitoring stations exist in most of the cities to monitor air pollution. However, their number is insufficient having in mind the high cost of stations, as well as annual calibration cost. The potential solution is to use low-cost off-the-shelf sensors to monitor related air quality parameters, but they are not reliable due to the low accuracy, calibration issues, and short life cycle. In this paper, the methodology is proposed for calibration off-the-shelf air quality sensors using statistical algorithms and offset values from the official public measurement stations. The possibilities are analysed to improve the reliability of low-cost sensors by processing the obtained raw data. Special attention is devoted to the detection and elimination of short intervals when the raw results have the extraordinary high value-peaks and to the corresponding interpolation of these data. New algorithm for “peaks” detection and elimination is proposed and evaluated. Common Air Quality Index (CAQI) is calculated and evaluated in comparison with public monitoring station. It is shown that low-cost sensors could be used with high reliability as a complementary network to public monitoring stations.

This paper presents the environmental monitoring solution ekoNET, developed for a real-time monitoring of air pollution and other atmospheric condition parameters such as temperature, air pressure and humidity. The system is based on low-cost gas, PM and meteorological sensors providing cost-efficient, simple to deploy, use and maintain solution targeted for the usage within the Internet of Things domain of smart cities and smart enterprises. The paper gives an overview of the system architecture, encompassing the ekoNET device, back-end cloud infrastructure, data handling and visualization engine as well as the application-level components and modules. Furthermore, initial field trial data of twelve ekoNET devices is presented, enabling the overall system operation performance testing.