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AuthorKadri, Abdullah
AuthorShaban, Khaled Bashir
AuthorYaacoub, Elias
AuthorAbu-Dayya, Adnan
Available date2022-12-21T10:01:45Z
Publication Date2012
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-642-34478-7_62
URIhttp://hdl.handle.net/10576/37490
AbstractThis paper presents an ambient air quality monitoring and prediction system. The system consists of several distributed monitoring stations that communicate wirelessly to a backend server using machine-to-machine communication protocol. Each station is equipped with gas- eous and meteorological sensors as well as data logging and wireless communication capabilities. The backend server collects real time data from the stations and converts it into information delivered to users through web portals and mobile applications. In addition to manipulating the real time information, the system is able to predict futuristic concentration values of gases by applying artificial neural networks trained by historical and collected data by the system. The system has been implemented and four solar-powered stations have been deployed over an area of 1 km 2. Data over four months has been collected and artificial neural networks have been trained to predict the average values of the next hour and the next eight hours. The results show very accurate prediction. 2012 Springer-Verlag.
Languageen
SubjectAir quality monitoring and prediction
Artificial neural network
Machine-to-Machine communication
TitleAir quality monitoring and prediction system using machine-to-machine platform
TypeConference Paper
Pagination508-517
Issue NumberPART 4
Volume Number7666 LNCS
dc.accessType Abstract Only


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