Development of a Multi-Fog Based Water Quality Monitoring System Using Bio-Sensing Platform

Water quality depends on various factors starting from climatic conditions to geographical location, chemical constitution to aquatic bio-diversity. Due to implicit relation between water quality to the quality of life environment and health, here, in this paper we propose an IoT inspired design in water sensing and classification. Using an illustrtative example, real-time water quality monitoring and learning algorithms are described, which can be implemented in an IoT network. We consider pisciculture breeding as a specific example to illustrate our point. The methodology may serve as a precursor for a large scale platform in which different water properties can be mapped and simultaneous classification and sensing of water properties are possible. A smart bio-material has been used as the data acquiring interface. In real-time such acquired data is stored and analysed in a fog device, which in turn, can help appropriate decision making and alarm setting, if any.