scholarly journals Novel Solutions for Smart Cities—Creating Air Pollution Maps Based on Intelligent Sensors

2018 ◽  
Author(s):  
Marzena Banach ◽  
Tomasz Talaśka ◽  
Rafał Długosz
Keyword(s):  
Air Pollution ◽  
Smart Cities ◽  
Intelligent Sensors

scholarly journals Image-Based Automatic Watermeter Reading under Challenging Environments

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 434
Author(s):  
Qingqi Hong ◽  
Yiwei Ding ◽  
Jinpeng Lin ◽  
Meihong Wang ◽  
Qingyang Wei ◽  
...  
Keyword(s):  
Smart Cities ◽  
Rapid Development ◽  
Mobile Network ◽  
Spatial Layout ◽  
Single Image ◽  
Automatic Instrument ◽  
Intelligent Sensors ◽  
Fifth Generation ◽  
Instrument Reading ◽  
Network Technologies

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.

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 Air Quality Prediction in Smart Cities Using Machine Learning Technologies based on Sensor Data: A Review

2020 ◽  
Vol 10 (7) ◽  
pp. 2401 ◽  
Author(s):  
Ditsuhi Iskandaryan ◽  
Francisco Ramos ◽  
Sergio Trilles
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Smart Cities ◽  
Learning Technologies ◽  
Sensor Data ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Air Pollution Prediction ◽  
Pollution Prediction

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

scholarly journals How to Integrate the Data-based Strategies and Advanced Technologies into Efficient Air Pollution Management in Smart Cities?

2021 ◽  
Author(s):  
Seunghwan Myeong ◽  
Khurram Shahzad
Keyword(s):  
Air Pollution ◽  
Development Policy ◽  
Energy Use ◽  
Smart Cities ◽  
Energy Performance ◽  
Exploratory Research ◽  
The Public ◽  
Pollution Management ◽  
Urban Development Policy ◽  
Core Part

Air pollution contributes to a critical environmental problem in various towns and cities. With the emergence of the smart cities concept, appropriate methods to curb associated with exposure to pollutants must have been a portion of appropriate urban development policy. This study presents a technologically driven air quality solution in smart cities to advertise energy-efficient and cleaner sequestration in these areas. It aims to address the issue of how to integrate the data-based strategies and artificial intelligence into efficient public sector pollution management in smart cities as a core part of the smart city definition. Exploratory research has been used in 152 smart cities, and environmental experts contributed to this study. It further addresses the technical criteria for implementing such a framework that the public administration uses to prepare the renovation of public buildings, minimize energy use and costs, and link these smart police stations to monitor air pollution as a part of integrated cities. Such a digital transition in resource management will increase public governance's energy performance, a higher standard of operation, and a healthier environment.

A multidimensional human-centric framework for environmental intelligence: Air pollution and noise in smart cities

2020 ◽  
Author(s):  
Andreas Bardoutsos ◽  
Gabriel Filios ◽  
Ioannis Katsidimas ◽  
Thomas Krousarlis ◽  
Sotiris Nikoletseas ◽  
...  
Keyword(s):  
Air Pollution ◽  
Smart Cities

Air Quality Monitoring Using Internet of Things (IoT) in Smart Cities

2019 ◽  
pp. 317-333
Author(s):  
Gayatri Doctor ◽  
Payal Patel
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Internet Of Things ◽  
Environmental Monitoring ◽  
Real Time ◽  
Smart Cities ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Applications ◽  
Environmental Health Problem

Air pollution is a major environmental health problem affecting everyone. An air quality index (AQI) helps disseminate air quality information (almost in real time) about pollutants like PM10, PM2.5, NO2, SO2, CO, O3, etc. In the 2018 environmental performance index (EPI), India ranks 177 out of 180 countries, which indicates a need for awareness about air pollution and air quality monitoring. Out of the 100 smart cities in the Indian Smart City Mission, which is an urban renewal program, many cities have considered the inclusion of smart environment sensors or smart poles with environment sensors as part of their proposals. Internet of things (IoT) environmental monitoring applications can monitor (in near real time) the quality of the air in crowded areas, parks, or any location in the city, and its data can be made publicly available to citizens. The chapter describes some IoT environmental monitoring applications being implemented in some of the smart cities like Surat, Kakinada.

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