Smart IoT and Machine Learning-based Framework for Water Quality Assessment and Device Component Monitoring

Area of Science:

• Environmental Science
• Water Resource Management
• Sensor Technology

Background:

• Increasing global population and rising temperatures exacerbate water scarcity, threatening human health and ecosystems.
• Traditional water quality and device management are resource-intensive, requiring significant manual effort and investment.
• Mismanagement of water resources poses serious risks to public health and environmental sustainability.

Purpose of the Study:

• To develop an Internet of Things (IoT)-based real-time framework for proactive water quality management.
• To integrate machine learning for analyzing water quality trends and device performance.
• To provide an efficient system for monitoring, alerting, and managing water parameters and smart devices.

Main Methods:

• Implementation of an IoT-based framework for real-time data acquisition from water quality sensors and smart devices.
• Utilization of machine learning models to analyze water quality trends and predict potential issues.
• Development of a monitoring and alerting system based on contamination and toxic parameter levels.

Main Results:

• The proposed IoT framework enables efficient real-time water quality monitoring and management.
• Machine learning models effectively analyze water quality trends and device performance data.
• The system demonstrates superior efficiency in managing and accessing water parameters compared to existing methods.

Conclusions:

• The developed IoT framework offers a significant advancement in water quality management and smart device monitoring.
• Real-time data analysis and proactive alerting are crucial for addressing water scarcity and ensuring water safety.
• This approach provides a scalable and efficient solution for optimizing water resource utilization and protecting public health.