TY - GEN
T1 - Air quality monitoring and prediction system using machine-to-machine platform
AU - Kadri, Abdullah
AU - Shaban, Khaled Bashir
AU - Yaacoub, Elias
AU - Abu-Dayya, Adnan
PY - 2012/11
Y1 - 2012/11
N2 - This 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.
AB - This 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.
KW - Air quality monitoring and prediction
KW - Artificial neural network
KW - Machine-to-Machine communication
UR - https://www.scopus.com/pages/publications/84869063080
U2 - 10.1007/978-3-642-34478-7_62
DO - 10.1007/978-3-642-34478-7_62
M3 - Conference contribution
AN - SCOPUS:84869063080
SN - 9783642344770
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 508
EP - 517
BT - Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
T2 - 19th International Conference on Neural Information Processing, ICONIP 2012
Y2 - 12 November 2012 through 15 November 2012
ER -