Smart City Air Pollution Monitoring and Prediction: A Case Study of Skopje
Date Issued
2020-09-24
Author(s)
Risteska Stojkoska, Biljana
Kalajdjieski, Jovan
Korunoski, Mladen
Abstract
One of the key aspects of smart cities is the enhancement
of awareness of the key stakeholders as well as the general population
regarding air pollution. Citizens often remain unaware of the pollution in
their immediate surrounding which usually has strong correlation with
the local environment and micro-climate. This paper presents an Internet of Things based system for real-time monitoring and prediction of
air pollution. First, a general layered management model for an Internet of Things based holistic framework is given by defining its integral
levels and their main tasks as observed in state-of-the-art solutions. The
value of data is increased by developing a suitable data processing subsystem. Using deep learning techniques, it provides predictions for future
pollution levels as well as times to reaching alarming thresholds. The
sub-system is built and tested on data for the city of Skopje. Although
the data resolution used in the experiments is low, the results are very
promising. The integration of this module with an Internet of Things infrastructure for sensing the air pollution will significantly improve overall
performance due to the intrinsic nature of the techniques employed.
of awareness of the key stakeholders as well as the general population
regarding air pollution. Citizens often remain unaware of the pollution in
their immediate surrounding which usually has strong correlation with
the local environment and micro-climate. This paper presents an Internet of Things based system for real-time monitoring and prediction of
air pollution. First, a general layered management model for an Internet of Things based holistic framework is given by defining its integral
levels and their main tasks as observed in state-of-the-art solutions. The
value of data is increased by developing a suitable data processing subsystem. Using deep learning techniques, it provides predictions for future
pollution levels as well as times to reaching alarming thresholds. The
sub-system is built and tested on data for the city of Skopje. Although
the data resolution used in the experiments is low, the results are very
promising. The integration of this module with an Internet of Things infrastructure for sensing the air pollution will significantly improve overall
performance due to the intrinsic nature of the techniques employed.
Subjects
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