MyAQI

Context-aware Outdoor Air Pollution Monitoring System

Document identifier: oai:DiVA.org:ltu-76721
Access full text here:10.1145/3365871.3365884
Keyword: Natural Sciences, Computer and Information Sciences, Media and Communication Technology, Naturvetenskap, Data- och informationsvetenskap, Medieteknik, Air Quality, Context-aware Computing, Internet of Things, Visualisation, Environmental Monitoring, Pervasive Mobile Computing, Distribuerade datorsystem
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

Air pollution is a growing global concern that affects the health and livelihood of millions of people worldwide. The advent of the Internet of Things (IoT) has made available a plethora of data sources that provide near real-time information on air pollution. Many studies and systems have taken advantage of data stemming from the IoT and have been dedicated to enhancing the monitoring and prediction of air quality, from a fairly analytical angle, often disregarding the user's perspective in processing and presenting this data. In this paper, we research and present a novel context-aware air quality monitoring and prediction system called My Air Quality Index (MyAQI). MyAQI takes into consideration user's context (e.g. health conditions, individual sensitivities and preferences) to tailor the visualisation and notifications. We propose a context model that is used to combine user's context with air pollution data to provide context-aware recommendations to the specific user. MyAQI also incorporates a prediction algorithm based on Long Short-Term Memory Neural Network (LSTM) to predict future air quality. MyAQI is implemented as a web-based application and has the capability to consume data from a wide range of data sources including IoT devices and open data sources (via Application Programming Interfaces (API)). We demonstrate the context-aware visualisation techniques implemented in MyAQI, which adapt to changing user's context, and validate the performance of the air quality prediction algorithm.

Authors

Daniel Schürholz

Deakin University, Melbourne, Australia
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Meruyert Nurgazy

Deakin University, Melbourne, Australia
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Arkady Zaslavsky

Deakin University, Melbourne, Australia
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Prem Prakash Jayaraman

Swinburne University, Melbourne, Australia
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Sylvain Kubler

Université de Lorraine, CRAN, UMR 7039 (CNRS), Campus Sciences, BP 70239, F-54506, Vandoeuvre-lès-Nancy, France
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Karan Mitra

Luleå tekniska universitet; Datavetenskap
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Saguna Saguna

Luleå tekniska universitet; Datavetenskap
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