scholarly journals Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas

2020 ◽  
Vol 12 (5) ◽  
pp. 776
Author(s):  
Marzena Banach ◽  
Rafał Długosz ◽  
Jolanta Pauk ◽  
Tomasz Talaśka
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Smart Cities ◽  
Real Data ◽  
High Amplitude ◽  
Base Station ◽  
Pollution Monitoring ◽  
Process Data ◽  
Signal Processing Algorithm ◽  
Air Pollution Monitoring

This paper proposes novel solutions for the application of air pollution monitoring systems in so called ‘smart cities’. A possibility of the implementation of a relatively dense network of wireless air pollution sensors that can collect and process data in real time was the motive behind our research and investigations. We discuss the concept of the wireless sensor network, taking into account the structure of the urban development in cities and we present a novel signal processing algorithm that may be used to control the communication scheme between particular sensors and an external network. We placed a special emphasis on the computational complexity to facilitate the implementation directly at the transistor level of particular sensors. The algorithm was verified using real data obtained from air pollution sensors installed in Krakow, Poland. To ensure sufficient robustness of the variability of input data, we artificially added high amplitude noise to the real data we obtained. This paper demonstrates the performance of the algorithm. This algorithm allows for the reduction of the noise amplitude by 23 dB and enables a reduction of the number of wireless communication sessions with a base station (BS) by 70%–80%. We also present selected measurement results of a prototype current-mode digital-to-analogue converter to be used in the sensors, for signal resolutions up to 7 bits.

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

Design and Improvement of Air Pollution Monitoring System based on IoT Network for Smart Cities

2020 ◽  
Vol 48 (1) ◽  
pp. 03-04
Author(s):  
Yvon Blanchard
Keyword(s):  
Air Pollution ◽  
Urban Communities ◽  
Monitoring System ◽  
Smart Cities ◽  
Physical World ◽  
Pollution Monitoring ◽  
Dynamic Capacity ◽  
Air Pollution Monitoring ◽  
Ecological Boundary

Ecological boundary observing has become significant worry in present day megalopolis because of transformation and progression. Presently, air pollution is a major issue for individual’s wellbeing in urban communities that experienced the more feature, for example, the traffic, modern, or backwoods fire or contaminated skies. The planned framework utilizes IOT which gives an affordable and a viable framework to screen air effluence level specifically territory. IOT engages tremendous extent of elements and physical world subtleties. For offer intriguing administrations, to trade and impart data, IOT installs availability with dynamic capacity among gadgets can be utilized. The methodology of framework characterizes a modified structure of IOT pedestal checking gadgets which decide the degrees of poisonous in gaspresent over air.

scholarly journals Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Oscar Alvear ◽  
Nicola Roberto Zema ◽  
Enrico Natalizio ◽  
Carlos T. Calafate
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Pollution Monitoring ◽  
Mobility Models ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Air Pollution Monitoring ◽  
Acceptable Alternative ◽  
Rural Environments ◽  
Time Bounds

Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.

Lidar system for air-pollution monitoring over urban areas

1997 ◽  
Author(s):  
Irina V. Moskalenko ◽  
Djolinard A. Shecheglov ◽  
Nikolai A. Molodtsov
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Pollution Monitoring ◽  
Lidar System ◽  
Air Pollution Monitoring

scholarly journals Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks

2020 ◽  
Vol 12 (24) ◽  
pp. 4142
Author(s):  
Jovan Kalajdjieski ◽  
Eftim Zdravevski ◽  
Roberto Corizzo ◽  
Petre Lameski ◽  
Slobodan Kalajdziski ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Data Augmentation ◽  
Class Imbalance ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Weather Data ◽  
Generative Adversarial Networks ◽  
Class Imbalance Problem ◽  
Air Pollution Monitoring

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem.

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