As IoT systems continue to evolve, simply displaying real-time data is no longer enough.

Modern systems must now be able to:

• Analyze sensor data automatically
• Detect anomalies instantly
• Predict potential failures in advance (Predictive Maintenance)
• Send alerts before devices actually break

This article will walk you through how to build an AI-powered IoT system using Go + WebSocket for real-time data pipelines and AI/ML as the "brain" for analysis — ideal for Smart Factory, Smart City, Smart Energy, and Industrial IoT applications.

🧠 Architecture Overview: AI + WebSocket + IoT

The system consists of 5 main components:

1. IoT Device
   • Devices like ESP32, Raspberry Pi, or any WebSocket-compatible sensors
2. WebSocket Server (Go)
   • A real-time gateway for receiving sensor data and routing it to AI engine and dashboard
3. AI/ML Engine
   • Performs real-time anomaly detection and predictive analysis
4. Alert System
   • Sends instant notifications when risks are detected
5. Monitoring Dashboard
   • Visualizes sensor data and AI analysis via WebSocket

📡 1. Recommended Sensor Data Structure

{
  "device_id": "MOTOR-01",
  "temperature": 78.2,
  "vibration": 0.92,
  "current": 15.3,
  "timestamp": 1734512000
}

✅ Best practices:

• Use numeric data
• Include timestamp
• Send data in streaming format

🔌 2. WebSocket Server (Go) for IoT + AI

type SensorPayload struct {
    DeviceID    string  `json:"device_id"`
    Temperature float64 `json:"temperature"`
    Vibration   float64 `json:"vibration"`
    Current     float64 `json:"current"`
    Timestamp   int64   `json:"timestamp"`
}

func handleIoT(conn *websocket.Conn) {
    for {
        var payload SensorPayload
        if err := conn.ReadJSON(&payload); err != nil {
            return
        }

        result := analyzeSensor(payload)
        conn.WriteJSON(result)
    }
}

⚠️ 3. Real-time Anomaly Detection

Example of basic anomaly logic:

func detectAnomaly(temp float64) bool {
    const maxTemp = 85.0
    return temp > maxTemp
}

func analyzeSensor(data SensorPayload) map[string]interface{} {
    anomaly := detectAnomaly(data.Temperature)

    return map[string]interface{}{
        "device_id": data.DeviceID,
        "anomaly":   anomaly,
        "message":   "Temperature anomaly detected",
    }
}

🔮 4. What is Predictive Maintenance?

Predicting when a device is likely to fail, using:

• Historical sensor data
• Pattern recognition
• AI/ML models (e.g., time-series, LSTM, gradient boosting)

📌 Examples:

• Increasing vibration → potential bearing failure
• Rising temperature → risk of overheating

🤖 5. Connecting AI/ML Models to WebSocket

Typical architecture:

• AI model runs as a microservice (e.g., Python + FastAPI)
• WebSocket server (Go) sends data via HTTP/gRPC
• Receives analysis result → forwards to dashboard

func callAIService(data SensorPayload) (string, error) {
    return "Maintenance required in 24 hours", nil
}

🚨 6. Sending Real-time Alerts via WebSocket

Example alert message:

{
  "type": "ALERT",
  "device_id": "MOTOR-01",
  "severity": "HIGH",
  "action": "Inspect motor within 24 hours"
}

WebSocket is ideal for sending instant alerts more efficient than REST or polling.

📊 7. Dashboard & Visualization

Dashboard can:

• Display live sensor graphs
• Highlight anomalies
• Show prediction timelines

Frontend: React / Vue / Angular → use WebSocket client (no polling = faster + lower bandwidth)

🔐 8. Security for AI + IoT Systems

Important security practices:

• ✅ Device authentication (Token / JWT)
• ✅ Rate limit per device
• ✅ Sensor data validation
• ✅ Access control for AI results

🚀 Challenge: Try It Yourself!

🧪 Mini Project Idea:

• Simulate device sending temperature every second
• WebSocket server detects anomalies
• Trigger alert if threshold exceeded
• Save data to DB for future ML training

This is the foundation for real-world Smart Factory and Predictive Maintenance systems.

🔮 Coming Up Next: EP.119 Real-time Collaborative Document Editing

In the next episode, we’ll guide you through building a “Google Docs-style” system where multiple users can edit the same document in real time powered by WebSocket, Conflict Resolution, and Sync algorithms 💬

If you’ve followed along this far, you’re ready to build full-scale AI-driven Real-time Systems. See you in the next article! 🚀