Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner

Area of Science:

• Agricultural technology
• Environmental monitoring
• Resource management

Background:

• Increasing global nutritional needs and natural resource degradation necessitate digital transformation in agriculture.
• Water wastage from issues like broken irrigation and negligence is a significant problem.
• Advancements in microcontrollers, low-power radios, and software tools enable efficient monitoring solutions.

Purpose of the Study:

• To design and implement an experimental water usage reporting system with Edge Artificial Intelligence (Edge AI) capabilities.
• To monitor water consumption and detect/report misuse events efficiently.
• To reduce power and network bandwidth requirements for water monitoring.

Main Methods:

• Integration of Internet of Things (IoT), Edge Computing (EC), and Machine Learning (ML).
• Development of a compact automated detection mechanism leveraging Edge AI.
• Focus on reduced data transmission from network edges to the cloud.

Main Results:

• Successful deployment of an experimental water usage reporting system.
• Drastic reduction in the energy footprint due to minimized data transfer.
• Demonstration of a compact, automated water misuse detection mechanism.

Conclusions:

• The developed Edge AI system effectively addresses water waste in agriculture.
• Combining IoT, EC, and ML offers a scalable and energy-efficient solution for resource monitoring.
• The system presents a viable approach for preserving natural resources in the face of growing demands.