[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"academy-blogs-en-1-1-all-golang-ep118-ai-websocket-iot-real-time-all--*":3,"academy-blog-translations-f8qh3qq27wm0xc5":79},{"data":4,"page":67,"perPage":67,"totalItems":67,"totalPages":67},[5],{"alt":6,"collectionId":7,"collectionName":8,"content":9,"cover_image":10,"cover_image_path":11,"created":12,"created_by":13,"expand":14,"id":73,"keywords":74,"locale":49,"published_at":75,"scheduled_at":13,"school_blog":71,"short_description":76,"slug":77,"status":69,"title":6,"updated":78,"updated_by":13,"views":72},"EP.118 Building Real-time AI + WebSocket System for IoT Devices","sclblg987654321","school_blog_translations","\u003Cp>As IoT systems continue to evolve, simply displaying real-time data is no longer enough.\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Cp>Modern systems must now be able to:\u003C\u002Fp>\u003Cul>\u003Cli>Analyze sensor data automatically\u003C\u002Fli>\u003Cli>Detect anomalies instantly\u003C\u002Fli>\u003Cli>Predict potential failures in advance (Predictive Maintenance)\u003C\u002Fli>\u003Cli>Send alerts before devices actually break\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Cp>This article will walk you through how to build an AI-powered IoT system using Go + WebSocket for real-time data pipelines and AI\u002FML as the \"brain\" for analysis — ideal for Smart Factory, Smart City, Smart Energy, and Industrial IoT applications.\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Ch2>🧠 Architecture Overview: AI + WebSocket + IoT\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>The system consists of 5 main components:\u003C\u002Fp>\u003Col>\u003Cli>IoT Device\u003Cul>\u003Cli>Devices like ESP32, Raspberry Pi, or any WebSocket-compatible sensors\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fli>\u003Cli>WebSocket Server (Go)\u003Cul>\u003Cli>A real-time gateway for receiving sensor data and routing it to AI engine and dashboard\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fli>\u003Cli>AI\u002FML Engine\u003Cul>\u003Cli>Performs real-time anomaly detection and predictive analysis\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fli>\u003Cli>Alert System\u003Cul>\u003Cli>Sends instant notifications when risks are detected\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fli>\u003Cli>Monitoring Dashboard\u003Cul>\u003Cli>Visualizes sensor data and AI analysis via WebSocket\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fli>\u003C\u002Fol>\u003Cp> \u003C\u002Fp>\u003Ch2>📡 1. Recommended Sensor Data Structure\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cpre>\u003Ccode class=\"language-plaintext language-json\">{\n  \"device_id\": \"MOTOR-01\",\n  \"temperature\": 78.2,\n  \"vibration\": 0.92,\n  \"current\": 15.3,\n  \"timestamp\": 1734512000\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp> \u003C\u002Fp>\u003Cp>✅ Best practices:\u003C\u002Fp>\u003Cul>\u003Cli>Use numeric data\u003C\u002Fli>\u003Cli>Include timestamp\u003C\u002Fli>\u003Cli>Send data in streaming format\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Ch2>🔌 2. WebSocket Server (Go) for IoT + AI\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cpre>\u003Ccode class=\"language-plaintext language-go\">type SensorPayload struct {\n    DeviceID    string  `json:\"device_id\"`\n    Temperature float64 `json:\"temperature\"`\n    Vibration   float64 `json:\"vibration\"`\n    Current     float64 `json:\"current\"`\n    Timestamp   int64   `json:\"timestamp\"`\n}\n\nfunc handleIoT(conn *websocket.Conn) {\n    for {\n        var payload SensorPayload\n        if err := conn.ReadJSON(&payload); err != nil {\n            return\n        }\n\n        result := analyzeSensor(payload)\n        conn.WriteJSON(result)\n    }\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp> \u003C\u002Fp>\u003Ch2>⚠️ 3. Real-time Anomaly Detection\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>Example of basic anomaly logic:\u003C\u002Fp>\u003Cpre>\u003Ccode class=\"language-plaintext language-go\">func detectAnomaly(temp float64) bool {\n    const maxTemp = 85.0\n    return temp > maxTemp\n}\n\nfunc analyzeSensor(data SensorPayload) map[string]interface{} {\n    anomaly := detectAnomaly(data.Temperature)\n\n    return map[string]interface{}{\n        \"device_id\": data.DeviceID,\n        \"anomaly\":   anomaly,\n        \"message\":   \"Temperature anomaly detected\",\n    }\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp> \u003C\u002Fp>\u003Ch2>🔮 4. What is Predictive Maintenance?\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>Predicting when a device is likely to fail, using:\u003C\u002Fp>\u003Cul>\u003Cli>Historical sensor data\u003C\u002Fli>\u003Cli>Pattern recognition\u003C\u002Fli>\u003Cli>AI\u002FML models (e.g., time-series, LSTM, gradient boosting)\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Cp>📌 Examples:\u003C\u002Fp>\u003Cul>\u003Cli>Increasing vibration → potential bearing failure\u003C\u002Fli>\u003Cli>Rising temperature → risk of overheating\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Ch2>🤖 5. Connecting AI\u002FML Models to WebSocket\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>Typical architecture:\u003C\u002Fp>\u003Cul>\u003Cli>AI model runs as a microservice (e.g., Python + FastAPI)\u003C\u002Fli>\u003Cli>WebSocket server (Go) sends data via HTTP\u002FgRPC\u003C\u002Fli>\u003Cli>Receives analysis result → forwards to dashboard\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Cpre>\u003Ccode class=\"language-plaintext language-go\">func callAIService(data SensorPayload) (string, error) {\n    return \"Maintenance required in 24 hours\", nil\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp> \u003C\u002Fp>\u003Ch2>🚨 6. Sending Real-time Alerts via WebSocket\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>Example alert message:\u003C\u002Fp>\u003Cpre>\u003Ccode class=\"language-plaintext language-json\">{\n  \"type\": \"ALERT\",\n  \"device_id\": \"MOTOR-01\",\n  \"severity\": \"HIGH\",\n  \"action\": \"Inspect motor within 24 hours\"\n}\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp> \u003C\u002Fp>\u003Cp>WebSocket is ideal for sending instant alerts more efficient than REST or polling.\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Ch2>📊 7. Dashboard & Visualization\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>Dashboard can:\u003C\u002Fp>\u003Cul>\u003Cli>Display live sensor graphs\u003C\u002Fli>\u003Cli>Highlight anomalies\u003C\u002Fli>\u003Cli>Show prediction timelines\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Cp>Frontend: React \u002F Vue \u002F Angular → use WebSocket client (no polling = faster + lower bandwidth)\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Ch2>🔐 8. Security for AI + IoT Systems\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>Important security practices:\u003C\u002Fp>\u003Cul>\u003Cli>✅ Device authentication (Token \u002F JWT)\u003C\u002Fli>\u003Cli>✅ Rate limit per device\u003C\u002Fli>\u003Cli>✅ Sensor data validation\u003C\u002Fli>\u003Cli>✅ Access control for AI results\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Ch2>🚀 Challenge: Try It Yourself!\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>🧪 Mini Project Idea:\u003C\u002Fp>\u003Cul>\u003Cli>Simulate device sending temperature every second\u003C\u002Fli>\u003Cli>WebSocket server detects anomalies\u003C\u002Fli>\u003Cli>Trigger alert if threshold exceeded\u003C\u002Fli>\u003Cli>Save data to DB for future ML training\u003C\u002Fli>\u003C\u002Ful>\u003Cp> \u003C\u002Fp>\u003Cp>This is the foundation for real-world Smart Factory and Predictive Maintenance systems.\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Chr>\u003Cp> \u003C\u002Fp>\u003Ch2>🔮 Coming Up Next: EP.119 Real-time Collaborative Document Editing\u003C\u002Fh2>\u003Cp> \u003C\u002Fp>\u003Cp>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 💬\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Cp>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! 🚀\u003C\u002Fp>\u003Cp> \u003C\u002Fp>\u003Cdiv class=\"raw-html-embed\">\u003Cdiv style=\"margin:0 0 6px 0; font-weight:700;\">Read more:\u003C\u002Fdiv>\n\u003Cul style=\"list-style:none; padding:0; margin:0; line-height:1.4;\">\n  \u003Cli style=\"margin:0;\">\u003Ca href=\"\u002Fen\u002Fblogs\u002Fcategories\u002FGolang\" title=\"Golang The Series\">Golang The Series\u003C\u002Fa>\u003C\u002Fli>\n  \u003Cli style=\"margin:0;\">\u003Ca href=\"\u002Fen\u002Fblogs\u002Fcategories\u002FJS2GO\" title=\"JS2GO\">JS2GO\u003C\u002Fa>\u003C\u002Fli>\n  \u003Cli style=\"margin:0;\">\u003Ca href=\"\u002Fen\u002Fblogs\u002Fcategories\u002FTailwind%20CSS\" title=\"Tailwind CSS\">Tailwind CSS\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\u003C\u002Fdiv>\u003Cp> \u003C\u002Fp>\u003Cdiv class=\"raw-html-embed\">\n  \u003Cp style=\"margin:0 0 6px 0;\">\u003Cstrong>Follow Us:\u003C\u002Fstrong>\u003C\u002Fp>\n  \u003Cul style=\"list-style:none; padding:0; margin:0; line-height: 0.4;\">\n    \u003Cli style=\"display:flex; 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