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A neural network approach to smarter sensor networks for water quality monitoring

O'Connor, Edel and Smeaton, Alan F. and O'Connor, Noel E. and Regan, Fiona (2012) A neural network approach to smarter sensor networks for water quality monitoring. Sensors 2012, 12 (4). pp. 4605-4632. ISSN 1424-8220

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Environmental monitoring is evolving towards large-scale and low-cost sensor networks operating reliability and autonomously over extended periods of time. Sophisticated analytical instrumentation such as chemo-bio sensors present inherent limitations because of the number of samples that they can take. In order to maximize their deployment lifetime, we propose the coordination of multiple heterogeneous information sources. We use rainfall radar images and information from a water depth sensor as input to a neural network (NN) to dictate the sampling frequency of a phosphate analyzer at the River Lee in Cork, Ireland. This approach shows varied performance for different times of the year but overall produces output that is very satisfactory for the application context in question. Our study demonstrates that even with limited training data, a system for controlling the sampling rate of the nutrient sensor can be set up and can improve the efficiency of the more sophisticated nodes of the sensor network.

Item Type:Article (Published)
Uncontrolled Keywords:multi-modal sensor networks; rainfall radar; chemical sensors; environmental monitoring; visual sensing
Subjects:Computer Science > Machine learning
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Official URL:
Copyright Information:©2012 MDPI
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:16908
Deposited On:23 Apr 2012 16:09 by Edel O'Connor. Last Modified 23 Apr 2012 16:09

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