Abstract
Abstract
Water quality monitoring plays a vital role in environmental protection, public health, agriculture, aquaculture and industrial automation. Conventional laboratory-based water analysis methods are often time-consuming, expensive and unsuitable for continuous real-time monitoring. This paper proposes an intelligent Internet of Things (IoT)-based multi-parameter water quality assessment and classification system integrated with fuzzy logic for real-time applications. The proposed system utilizes multiple sensors, including pH, turbidity, Total Dissolved Solids (TDS) and temperature sensors, to continuously monitor water category. An ESP32 microcontroller is employed for real-time data acquisition, processing, wireless communication and intelligent decision-making. To improve classification reliability under uncertain and dynamic environmental conditions, a fuzzy logic-based inference system is incorporated into the proposed framework. The fuzzy controller evaluates the sensor parameters simultaneously using adaptive membership functions and rule-based reasoning to classify water into different usability categories such as drinkable, washable, farming, aquarium, and non-usable. The classified results are displayed locally through an OLED display and remotely monitored through IoT cloud connectivity. Experimental validation was conducted using multiple real-world water samples collected from domestic, agricultural, pond, industrial and contaminated water sources. The obtained results demonstrate that the proposed fuzzy logic-based system provides improved classification accuracy, better adaptability, enhanced noise tolerance and reliable real-time monitoring performance compared to conventional threshold-based approaches. The developed system is portable, low-cost, energy-efficient and suitable for deployment in remote and industrial environments. The proposed intelligent framework offers a promising solution for next-generation smart water quality monitoring and environmental management systems.