A Neural Network Approach to Smarter Sensor Networks for Water Quality Monitoring

Autor: Edel O'Connor, Fiona Regan, Noel E. O'Connor, Alan F. Smeaton
Jazyk: angličtina
Rok vydání: 2012
Předmět:
rainfall radar
Computer science
Visual sensor network
Reliability (computer networking)
Real-time computing
0207 environmental engineering
02 engineering and technology
computer.software_genre
lcsh:Chemical technology
01 natural sciences
Biochemistry
Article
Analytical Chemistry
Image processing
visual sensing
Radar imaging
Environmental monitoring
Machine learning
lcsh:TP1-1185
Instrumentation (computer programming)
Electrical and Electronic Engineering
020701 environmental engineering
Instrumentation
environmental monitoring
Artificial neural network
010401 analytical chemistry
chemical sensors
6. Clean water
Atomic and Molecular Physics
and Optics
0104 chemical sciences
Key distribution in wireless sensor networks
13. Climate action
multi-modal sensor networks
Data mining
Water quality
Wireless sensor network
computer
Zdroj: Sensors, Vol 12, Iss 4, Pp 4605-4632 (2012)
Sensors (Basel, Switzerland)
Sensors; Volume 12; Issue 4; Pages: 4605-4632
ISSN: 1424-8220
Popis: 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.
Databáze: OpenAIRE
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