Sensors 2008, 8(6), 3601-3623; doi:10.3390/s8063601
Article

Air Pollution Monitoring and Mining Based on Sensor Grid in London

1 Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2BW, United Kingdom 2 Department of Physics, Imperial College London, 180 Queens Gate, London SW7 2BW, United Kingdom
* Author to whom correspondence should be addressed.
Received: 1 April 2008; in revised form: 22 May 2008 / Accepted: 23 May 2008 / Published: 1 June 2008
(This article belongs to the Special Issue Sensors for Urban Environmental Monitoring)
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Abstract: In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a twolayer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.
Keywords: urban air pollution; sensor network; grid; distributed data mining.

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MDPI and ACS Style

Ma, Y.; Richards, M.; Ghanem, M.; Guo, Y.; Hassard, J. Air Pollution Monitoring and Mining Based on Sensor Grid in London. Sensors 2008, 8, 3601-3623.

AMA Style

Ma Y, Richards M, Ghanem M, Guo Y, Hassard J. Air Pollution Monitoring and Mining Based on Sensor Grid in London. Sensors. 2008; 8(6):3601-3623.

Chicago/Turabian Style

Ma, Yajie; Richards, Mark; Ghanem, Moustafa; Guo, Yike; Hassard, John. 2008. "Air Pollution Monitoring and Mining Based on Sensor Grid in London." Sensors 8, no. 6: 3601-3623.

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