Water pollution caused by human activities poses a serious global threat to human health. Sensor technologies enabling water monitoring are an important tool that can help facing this problem. In this work, we propose an embedded IoT-ready system based on a proprietary sensor technology for the detection and recognition of six water contaminants. The system architecture is composed of two layers: (i) a sensing layer based on the SENSIPLUS chip, a proprietary Micro-Analytical Sensing Platform with six interdigitated electrodes metalized through different materials; and (ii) a data collection, communication, and classification layer with both hardware and software components. Being classification the most computationally and resource intensive operation, we evaluated nine machine learning solutions of different complexity and analyzed the trade-off between recognition accuracy, processing time, and memory usage to find a solution suitable to be implemented on an edge node. The highest average accuracy of 95.4% was achieved with K-nearest neighbor classification without constraints on processing time and memory usage, which confirms the potentiality of the system. When such constraints are taken into consideration, the best performance dropped to 86.4% offered by Multi Layer Perceptron.
An IoT-ready solution for automated recognition of water contaminants
Bria A.;Cerro G.;Ferdinandi M.;Marrocco C.;Molinara M.
2020-01-01
Abstract
Water pollution caused by human activities poses a serious global threat to human health. Sensor technologies enabling water monitoring are an important tool that can help facing this problem. In this work, we propose an embedded IoT-ready system based on a proprietary sensor technology for the detection and recognition of six water contaminants. The system architecture is composed of two layers: (i) a sensing layer based on the SENSIPLUS chip, a proprietary Micro-Analytical Sensing Platform with six interdigitated electrodes metalized through different materials; and (ii) a data collection, communication, and classification layer with both hardware and software components. Being classification the most computationally and resource intensive operation, we evaluated nine machine learning solutions of different complexity and analyzed the trade-off between recognition accuracy, processing time, and memory usage to find a solution suitable to be implemented on an edge node. The highest average accuracy of 95.4% was achieved with K-nearest neighbor classification without constraints on processing time and memory usage, which confirms the potentiality of the system. When such constraints are taken into consideration, the best performance dropped to 86.4% offered by Multi Layer Perceptron.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
