Abstract
Surface water quality monitoring is one of the important activities in the environmental monitoring domain and implies complex measurement activities in order to obtain physical, chemical and biological characteristics of the water. Some of these characteristics are able to be measured in the field but imply the utilization of specific water quality sensors that are used by operators as individually units or, preferably, are part of distributed water quality monitoring networks particularly when monitoring extensive areas.
Two concepts are nowadays associated with environment monitoring networks: smart sensing nodes and computational intelligence algorithms. Thus, different smart sensing nodes deliver data that are used by advanced processing units for different purposes, namely: (1) to evaluate the characteristics of water based on measurement channel indirect modeling; (2) to perform the short time and long term forecasting of these characteristics; (3) to detect pollution events and anomalous functioning; (4) to perform data recovering using intelligent algorithms such as neural network and adaptive neuro-fuzzy. The overall operation of the network is optimized if its nodes are provided with functionalities such as auto-identification, networking plug-and-play, auto-calibration, and fault detection.
IEEE 1451 family of standards define all aspects necessary not only to transform a sensor into a smart sensor, but also to interface or integrate sensors in networks. In the paragraphs that will follow, we propose the architecture of a smart sensing node suitable for a distributed water quality monitoring network that is IEEE 1451 compatible. The emphasis is placed on the identification of each sensor – which permits individual addressing - and on the algorithms for multivariable characteristics modeling that prove to be very useful for accurate direct digital readout of water quality parameters.
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Postolache, O., Girão, P.S., Pereira, J.M.D. (2011). Water Quality Assessment through Smart Sensing and Computational Intelligence. In: Mukhopadhyay, S.C., Lay-Ekuakille, A., Fuchs, A. (eds) New Developments and Applications in Sensing Technology. Lecture Notes in Electrical Engineering, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17943-3_10
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DOI: https://doi.org/10.1007/978-3-642-17943-3_10
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