SentinelEdge AI

An Edge AI-powered industrial monitoring platform that detects machine anomalies in real time, predicts maintenance needs, and identifies potential cyber threats before they cause downtime.

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SentinelEdge AI

Inspiration

Industries depend on connected machines for production, but unexpected equipment failures and cyber threats can lead to downtime, financial losses, and safety risks. Many existing monitoring solutions rely heavily on cloud connectivity, which introduces latency and limits real-time decision-making. We wanted to create a solution that brings intelligence closer to the machines using Edge AI.

What it does

SentinelEdge AI is an Edge AI-powered industrial monitoring platform that continuously analyzes machine sensor data in real time. The platform detects abnormal machine behavior, predicts maintenance requirements, and identifies potential cyber threats before they become critical.

The system provides a live dashboard where users can monitor machine health, receive instant alerts, and make informed maintenance decisions without relying entirely on cloud processing.

Key Features

  • Real-time industrial machine monitoring
  • AI-based anomaly detection
  • Predictive maintenance recommendations
  • Cyber threat detection
  • Low-latency edge processing
  • Interactive monitoring dashboard
  • Live sensor data visualization

How we built it

We developed the frontend using React to provide an intuitive dashboard for monitoring industrial devices.

The backend was built with Node.js and Express, which handles sensor data collection and communication between different system components.

Machine learning techniques are used to analyze sensor readings and identify abnormal patterns that may indicate machine failures or suspicious activity. MongoDB stores monitoring data, alerts, and historical records for future analysis.

The entire workflow simulates an Edge AI environment where most decisions are performed locally before sending important information to the dashboard.

Challenges we ran into

  • Simulating realistic industrial sensor data
  • Designing meaningful anomaly detection logic
  • Building a responsive real-time dashboard
  • Balancing AI predictions with lightweight edge processing
  • Integrating multiple technologies into one system

Accomplishments that we're proud of

  • Developed a complete industrial monitoring prototype
  • Successfully implemented anomaly detection
  • Built an interactive dashboard for visualization
  • Designed a scalable architecture suitable for Industry 4.0 environments
  • Demonstrated how Edge AI can improve industrial monitoring

What we learned

This project improved our understanding of:

  • Edge AI architectures
  • Industrial IoT systems
  • Machine learning for anomaly detection
  • Real-time data processing
  • Full-stack application development

What's next for SentinelEdge AI

  • Deploy the solution on Raspberry Pi or industrial edge devices
  • Integrate real IoT sensors
  • Improve predictive maintenance using deep learning
  • Add digital twin visualization
  • Enable cloud synchronization for multi-factory monitoring
  • Support more industrial protocols and devices

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