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CAVISAP: Context-Aware Visualization of Air Pollution with IoT Platforms
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Air pollution is a severe issue in many big cities due to population growth and the rapid development of the economy and industry. This leads to the proliferating need to monitor urban air quality to avoid personal exposure and to make savvy decisions on managing the environment. In the last decades, the Internet of Things (IoT) is increasingly being applied to environmental challenges, including air quality monitoring and visualization. In this thesis, we present CAVisAP, a context-aware system for outdoor air pollution visualization with IoT platforms. The system aims to provide context-aware visualization of three air pollutants such as nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5) in Melbourne, Australia and Skellefteå, Sweden. In addition to the primary context as location and time, CAVisAP takes into account users’ pollutant sensitivity levels and colour vision impairments to provide personalized pollution maps and pollution-based route planning. Experiments are conducted to validate the system and results are discussed.

Place, publisher, year, edition, pages
2019.
Keywords [en]
context-aware, data visualization, air pollution, Internet of Things, IoT
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-76082OAI: oai:DiVA.org:ltu-76082DiVA, id: diva2:1353255
External cooperation
Deakin University, Melbourne, Australia
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level
Presentation
2019-06-17, 20:42 (English)
Supervisors
Examiners
Available from: 2019-10-03 Created: 2019-09-21 Last updated: 2019-10-03Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
  • en-GB
  • en-US
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  • nn-NO
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  • Other locale
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Output format
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