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 two-layer 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.
Highlights
Transport has a significant impact upon the environment in which we live. These impacts can be divided under four broad headings: local air quality, climate change, noise and watercourse pollution [1], while the clean air is vital to human health
These include developing and extending existing e-Science Grid, sensor units, communication and modeling technologies to enable the integrating of data from heterogeneous fixed and mobile environmental sensors grids in real time to provide dynamic estimates of pollutants and hazard concentrations; demonstrating how these can be usefully correlated with a wide range of other complementary dynamic data, such as weather, health or traffic data
We have provided an overview of the urban air pollution analysis within MoDisNet project, describing the network framework, the GUSTO sensor technology, the mobile sensor grid architecture and the distributed data mining algorithm
Summary
Transport has a significant impact upon the environment in which we live. In general, these impacts can be divided under four broad headings: local air quality, climate change, noise and watercourse pollution [1], while the clean air is vital to human health. Developing a sensor network over a target region will face a lot of challenges These include developing and extending existing e-Science Grid, sensor units, communication and modeling technologies to enable the integrating of data from heterogeneous fixed and mobile environmental sensors grids in real time to provide dynamic estimates of pollutants and hazard concentrations; demonstrating how these can be usefully correlated with a wide range of other complementary dynamic data, such as weather, health or traffic data. We present the system architecture to meet the demands of the project as well as the sensor unit itself This is followed by the simulation platform design and the networking performance simulation as well as the real-time pollution data analysis scenarios. We conclude the paper with a summary of the research and a discussion of future work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
