The sensable city: A survey on the deployment and management for smart city monitoring
In last two decades, various monitoring systems have been designed and deployed in urban
environments, toward the realization of the so called smart cities. Such systems are based …
environments, toward the realization of the so called smart cities. Such systems are based …
Methodologies for cross-domain data fusion: An overview
Y Zheng - IEEE transactions on big data, 2015 - ieeexplore.ieee.org
Traditional data mining usually deals with data from a single domain. In the big data era, we
face a diversity of datasets from different sources in different domains. These datasets …
face a diversity of datasets from different sources in different domains. These datasets …
Deep air quality forecasting using hybrid deep learning framework
Air quality forecasting has been regarded as the key problem of air pollution early warning
and control management. In this article, we propose a novel deep learning model for air …
and control management. In this article, we propose a novel deep learning model for air …
Comparative analysis of machine learning techniques for predicting air quality in smart cities
Dealing with air pollution presents a major environmental challenge in smart city
environments. Real-time monitoring of pollution data enables local authorities to analyze the …
environments. Real-time monitoring of pollution data enables local authorities to analyze the …
Forecasting fine-grained air quality based on big data
In this paper, we forecast the reading of an air quality monitoring station over the next 48
hours, using a data-driven method that considers current meteorological data, weather …
hours, using a data-driven method that considers current meteorological data, weather …
Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality
The interpolation, prediction, and feature analysis of fine-gained air quality are three
important topics in the area of urban air computing. The solutions to these topics can provide …
important topics in the area of urban air computing. The solutions to these topics can provide …
Sparse mobile crowdsensing: challenges and opportunities
Sensing cost and data quality are two primary concerns in mobile crowd sensing. In this
article, we propose a new crowd sensing paradigm, sparse mobile crowd sensing, which …
article, we propose a new crowd sensing paradigm, sparse mobile crowd sensing, which …
A deep spatial-temporal ensemble model for air quality prediction
Air quality has drawn much attention in the recent years because it seriously affects people's
health. Nowadays, monitoring stations in a city can provide real-time air quality, but people …
health. Nowadays, monitoring stations in a city can provide real-time air quality, but people …
A big data-as-a-service framework: State-of-the-art and perspectives
Due to the rapid advances of information technologies, Big Data, recognized with 4Vs
characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as …
characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as …
Multitask air-quality prediction based on LSTM-autoencoder model
X Xu, M Yoneda - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
With the development of the data-driven modeling techniques, using the neural network to
simulate the transport process of atmospheric pollutants and constructing PM 2.5 time-series …
simulate the transport process of atmospheric pollutants and constructing PM 2.5 time-series …