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Real-time air quality measurements using mobile platforms
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Sevusu, Parveen. Real-time air quality measurements using mobile platforms. Retrieved from https://doi.org/doi:10.7282/T3B859TB

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TitleReal-time air quality measurements using mobile platforms
NameSevusu, Parveen (author); Iftode, Liviu (chair); Nath, Badri (internal member); Ganapathy, Vinod (internal member); Carlton, Ann Marie (internal member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-01 (degree)
SubjectComputer Science, Air quality--Monitoring, Air pollution, Detectors, Mobile computing
Extent1 online resource (ix, 77 p. : ill.)
DescriptionAir pollution poses a serious threat to our health and quality of life. Measuring pollution in the air we breathe and sharing the results with our peers is an important step in increasing social awareness for creating a clean environment. Usually, pollution measurements are conducted using expensive monitors at fixed locations. These measurements fail to provide accurate real-time pollution information in most of the highly polluted roads. It is desirable to have access to real time fine-grained measurements to be able to quickly analyze and identify alarming levels of pollutants. Pervasiveness of smart phones with internet connectivity and increased availability of personal air quality sensors provide a unique opportunity to develop air pollution conscious community of users for collecting and sharing real time air pollution data. In this thesis, we propose air quality monitoring through mobile sensors, which are low-power, low-cost, designed to sample air pollutants such as carbon mono-oxide, nitrogen oxide, sulphur dioxide, environmental temperature, humidity and air pressure and communicate via Bluetooth with a smartphone. We built an iOS mobile application that makes use of location services available on the mobile phones, to record GPS co-ordinates along with air pollution readings. We built a mobile to cloud replication model, data exchange protocol and outlier detection for anomalous sensor readings. We also employed spatial database queries to optimize location based pollution data sharing and visualization of pollution data overlays on mobile map displays. We evaluated our mobile pollution-sensing model against stationary NJ DEP monitor and studied spatial granularity of pollution data.
NoteM.S.
NoteIncludes bibliographical references
Noteby Parveen Sevusu
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T3B859TB
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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