Abstract
Predicting the time and place where concentrations of pollutants will be the highest is critical for air quality monitoring- and early-warning systems in urban areas. Much of the research effort in this area is focused only on improving air pollution prediction algorithms, disregarding valuable environmental- and user-based context. In this paper we apply context-aware computing concepts in the MyAQI system, to develop an integral air quality monitoring and prediction application, that shifts the focus towards the individual needs of each end-user, without neglecting the benefits of the latest air pollution forecasting algorithms. We design and describe a novel context and situation reasoning model, that considers external environmental context, along with user based attributes, to feed into the prediction model. We demonstrate the adaptability and customizability of the design and the accuracy of the prediction technique in the implementation of the responsive MyAQI web application. We test the implementation with different user profiles and show the results of the system’s adaptation. We demonstrate the prediction model’s accuracy, when using extended context for 4 air quality monitoring stations in the Melbourne Region in Victoria, Australia.
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Acknowledgment
This research was funded by the PERCCOM Erasmus Mundus Joint Masters Program of the European Union [10]. Part of this study has been carried out in the scope of the project bIoTope, which is co-funded by the European Commission under Horizon-2020 program, contract number H2020-ICT- 2015/688203-bIoTope. The research was also supported by Deakin University, Australia. Air pollution data in the city of Melbourne was freely obtained from Victoria EPA API (http://sciwebsvc.epa.vic.gov.au/aqapi/).
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Schürholz, D., Zaslavsky, A., Kubler, S. (2019). Context- and Situation Prediction for the MyAQI Urban Air Quality Monitoring System. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2019 2019. Lecture Notes in Computer Science(), vol 11660. Springer, Cham. https://doi.org/10.1007/978-3-030-30859-9_7
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DOI: https://doi.org/10.1007/978-3-030-30859-9_7
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