A Novel Evolutionary Approach for IoT-Based Water Contaminant Detection

Nowadays, the problem of pollution in water is a very serious issue to be faced and it is really important to be able to monitoring it with non-invasive and low-cost solutions, like those offered by smart sensor technologies. In this paper, we propose an improvement of an our innovative classification system, based on geometrical cones, to detect and classify pollutants, belonging to a given set of substances, spilled into waste water. The solution is based on an ad-hoc classifier that can be implemented aboard the Smart Cable Water (SCW) sensor, based on SENSIPLUS technology developed by Sensichips s.r.l. The SCW is a smart-sensor endowed with six interdigitated electrodes, covered by specific sensing materials that allow detecting between different water contaminants. In order to develop an algorithm suitable to apply the “edge computing” paradigm we first compress the input data from a 10-dimensional space to a 3-D space by using the PCA decomposition techniques. Then we use an ad-hoc classifier to classify between the different contaminants in the transformed space. To learn the classifier’s parameters we used the evolutionary algorithms. The obtained results have been compared with the old classification system and other, more classical, machine learning approaches.