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Evolutionary Computation to Implement an IoT-Based System for Water Pollution Detection

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Abstract

The problem of detecting pollutants in water with non-invasive and low-cost sensors is an open question. In this paper, we propose a system for the detection and classification of pollutants based on the improvement of a previous proposal, focused on geometric cones. The solution is based on a classifier suitable to be implemented aboard the so-called Smart Cable Water (SCW) sensor, a multi-sensor based on SENSIPLUS® technology developed by Sensichips s.r.l. The SCW endowed with six interdigitated electrodes is a smart-sensor covered by specific sensing materials that allow differentiating between different water contaminants. Using the PCA or LDA decomposition, we obtain a data compression that makes data suitable for the “edge computing” paradigm with a reduction from a 10-dimensional space to a 3-dimensional space. We defined an ad-hoc classifier to distinguish contaminants represented by points in the 3-dimensional space. We used an evolutionary algorithm to learn the classifier’s parameters. Finally, we compared the performance of our system with that achieved by the old classification system based only on PCA, as well as those achieved by other machine learning algorithms. The proposed system achieved the best accuracy of 87%, outperforming the other state-of-the-art systems compared. The novelty of the system proposed lies in the usage of an evolutionary algorithm for the optimization of the parameters of a novel PCA-based classification algorithm for the detection of water pollutants.

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Notes

  1. Note that this strategy ensures that the best individuals found along the evolutionary process are not lost.

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Acknowledgements

The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement SYSTEM No. 787128. The authors are solely responsible for it and that it does not represent the opinion of the Community and that the Community is not responsible for any use that might be made of information contained therein. The authors gratefully acknowledge Sensichips s.r.l. for the support during the experimental phases. This work was also supported by MIUR (Minister for Education, University and Research, Law 232/216, Department of Excellence).

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Authors and Affiliations

  1. Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio, Via G. Di Biasio, 43, 03043, Cassino, FR, Italy

    Claudio De Stefano, Luigi Ferrigno, Francesco Fontanella, Luca Gerevini & Mario Molinara

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  1. Claudio De Stefano
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  2. Luigi Ferrigno
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  3. Francesco Fontanella
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  4. Luca Gerevini
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  5. Mario Molinara
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Correspondence to Francesco Fontanella.

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This article is part of the topical collection “Applications of bioinspired computing (to real world problems)” guest edited by Aniko Ekart, Pedro Castillo and Juanlu Jiménez-Laredo.

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De Stefano, C., Ferrigno, L., Fontanella, F. et al. Evolutionary Computation to Implement an IoT-Based System for Water Pollution Detection. SN COMPUT. SCI. 3, 111 (2022). https://doi.org/10.1007/s42979-021-00986-x

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