In an increasingly connected world, electronic devices and machines are becoming smarter and more complex. However, one of the major challenges that industries and consumers continue to face is unexpected device failures, often caused by subtle faults that go undetected until a breakdown occurs. These failures result in operational downtime, safety risks, and increased maintenance costs. Traditional monitoring systems either require constant human supervision or rely heavily on cloud-based analytics, which introduce latency, privacy concerns, and dependence on internet connectivity.

SmartEdge is a groundbreaking solution designed to overcome these limitations by leveraging the power of Edge AI and Tiny Machine Learning (TinyML). It is a compact, energy-efficient, and intelligent module that can be integrated into a wide range of electronic devices to perform real-time anomaly detection directly on the device—without the need for cloud processing.

At the heart of SmartEdge lies a lightweight AI model trained to recognize patterns in sensor data such as vibration, temperature, audio, and pressure. The module continuously monitors these inputs and detects any deviations from normal behavior, instantly flagging anomalies. The AI model is deployed on microcontrollers such as Arduino Nano 33 BLE Sense, Raspberry Pi Pico, or STM32, allowing for ultra-low power consumption and fast inference times. These capabilities make SmartEdge suitable for remote locations, battery-operated systems, and mission-critical devices where cloud access may be limited or non-existent.

The SmartEdge module is designed with versatility in mind. It supports a range of sensors and can be customized for specific use cases in industrial machinery, consumer electronics, wearables, healthcare devices, and agricultural equipment. For example, in a manufacturing plant, SmartEdge can detect abnormal vibrations in motors or gearboxes and trigger alerts before mechanical failure occurs. In medical devices, it can monitor patient vitals and detect irregularities that may require immediate attention.

The project utilizes a streamlined workflow: data is collected from the sensors, an ML model is trained using tools like TensorFlow, scikit-learn, or Edge Impulse, and the trained model is then converted to a lightweight format (e.g., TensorFlow Lite for Microcontrollers) for on-device deployment. An intuitive interface and alert system—via LEDs, buzzers, or wireless communication—makes it easy for users to understand and respond to anomalies.

SmartEdge offers several competitive advantages:

• Operates without internet connectivity
• Low-latency, real-time decision-making
• Lower cost compared to cloud-based systems
• Enhanced data privacy
• Scalable across industries and environments

By bringing intelligence to the edge, SmartEdge represents the next frontier in device reliability, safety, and automation. It bridges the gap between AI and embedded systems and empowers industries to shift from reactive to predictive maintenance. With this innovation, SmartEdge contributes to building smarter, more resilient technologies for the future.

This project is submitted under the Electronics category in the Create the Future Design Contest 2025, aiming to demonstrate the transformative impact of combining AI/ML with embedded edge computing.