IoT-Based Real-Time Distributed Lakes Monitoring System

This study presents the design and implementation of an Internet of Things (IoT) based system for real-time monitoring of key physicochemical parameters in lake water, supporting water quality assessment and sustainable ecosystem management. Traditional water monitoring methods are often labor-intensive, infrequent, and lack spatial coverage. In contrast, the proposed system utilizes a network of sensors, including temperature sensors, pH sensors, dissolved oxygen (DO) sensors, turbidity sensors, and electrical conductivity (EC) sensors, to enable continuous, real-time data collection and analysis. The system utilizes low-power wireless communication technologies such as Wi-Fi for data transmission and integrates with cloud computing platforms for data storage, processing, and remote access. Real-time analysis and alert mechanisms enable early detection of pollution or unusual changes in water quality. The results obtained confirm the system’s effectiveness in improving monitoring accuracy and timeliness while significantly reducing the need for manual sampling. This solution provides a scalable, cost-effective, and automated approach to lake water monitoring, supporting data-driven decision-making for conservation and restoration efforts at any scale.