Aug 2026· International Conference on Information Security and Cryptology· pp. 483-489· 0 citations· 9 references
Abstract
Water pollution has become one of the most serious dangers in recent years, since drinking water has been contaminated and polluted. Polluted water can cause a variety of diseases in humans and animals, affecting the ecosystem's life cycle. Early detection of water contamination allows for the implementation of appropriate controls and the avoidance of dangerous circumstances. Smart solutions for water pollution monitoring are becoming increasingly important as sensors, communication, and Internet of Things (IoT) technology advance. The research offers a low-cost and efficient IoT-based smart water quality monitoring system that monitors water quality parameters. Three water samples are used to evaluate the constructed model, and the parameters are sent to the cloud server for additional processing. The solar panel is used to recharge the battery and power the sensor node when there is not enough power. A lithium battery and solar panel work together to provide steady energy storage and continuous operation even in low light. Monitoring the safety of the drinking water supply is the main objective. To avoid the presence of impurities or pollutants that might damage human health, the system will keep an eye on the quality of the water. It should offer accurate and reliable data on a range of water quality characteristics, including temperature, turbidity, pH, and pollutant amounts, which may be useful for drinking, fishing, irrigation, and aquatic life.
Water quality monitoring is es-sential for ensuring safe drinking water and protecting public health. With in-creasing pollution and improper waste management, the quality of drinking wa-ter is being affected in many regions. Tra-ditional methods of water testing rely on manual sampling and laboratory analy-sis, which...
Akshya Sri S, R. Parameswari· International Journal of Sci...· 0 citations
In Malaysia, the degradation of water bodies due to rapid urbanisation, agricultural runoff, and industrial waste highlight the urgent need for continuous, efficient water quality monitoring. However, current manual-based sampling methods are time-consuming, labour-intensive, and insufficient for real-time assessment,...
N. A. Makin, A. Azahari, A. M. Firdaus et al.· Journal of Engineering and T...· 0 citations
Water quality is the most important factor for aquaculture and drinking purposes. Water pollution is a major
threat that affects the aquatic life and human’s health. Conventional water quality methods use visual and physical testing
to evaluates the probability of collected water sample. The solution of this problem is...
S. Aruna, Sujatha Kuna, Divya Sri Jami et al.· International Journal of Inn...· 0 citations
Introduction: A significant portion of the population still lacks access to drinking water and relies on non-conventional sources such as wells and springs. In these cases, information on water quality and availability is difficult and expensive to obtain. The solutions available in the local market do not offer equita...
An end-to-end Internet of Things framework designed for real-time water quality monitoring and predictive pollution modeling and a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed.
Parvathy Krishna V, G. S, Sahala Mehrin et al.· International Journal of Tec...· 0 citations
How the Internet of Things (IoT-based) sensing and artificial intelligence analytics may be combined in order to facilitate real-time monitoring and optimization in industrial treatment trains, including pretreatment and biological treatment systems, membranes, and zero-liquid-discharge (ZLD) systems is reviewed.