AI-Based Fire and Smoke Detection with Real-Time Alerts

AI-Based Fire and Smoke Detection with Real-Time Alerts AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Cloud Dashboard

AI-Based Fire and Smoke Detection with Real-Time Alerts AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Cloud Dashboard 1. Project Overview This project is an intelligent IoT-based fire and smoke monitoring system using an ESP32 microcontroller, environmental sensors, cloud platforms, and AI-powered automation workflows. The system continuously monitors: Smoke concentration Temperature Flame detection Air quality When abnormal conditions are detected, the ESP32 sends data to: Telegram for instant alerts Google Sheets for logging ThingSpeak dashboard for cloud visualization n8n automation server for AI-based workflows and voice notifications The system can: Detect fire/smoke in real-time Send AI-generated voice alerts Store sensor history Predict power consumption trends Trigger smart automations Enable future AI-based emergency response systems 2. Key Features Core Features ✅ Real-time fire detection ✅ Smoke monitoring ✅ ESP32 WiFi connectivity ✅ Telegram instant alerts ✅ AI voice notifications ✅ Google Sheets logging ✅ ThingSpeak cloud dashboard ✅ n8n workflow automation ✅ Agentic IoT automation ✅ AI power consumption prediction ✅ Remote monitoring dashboard 3. System Architecture ┌──────────────────┐ │ Smoke Sensor MQ2 │ └────────┬─────────┘ │ ┌────────▼─────────┐ │ Flame Sensor │ └────────┬─────────┘ │ ┌────────▼─────────┐ │ DHT11/DHT22 │ │ Temp Sensor │ └────────┬─────────┘ │ ┌────────▼─────────┐ │ ESP32 Controller │ └────────┬─────────┘ │ WiFi ┌────────────────┼─────────────────┐ │ │ │ ▼ ▼ ▼ Telegram Bot ThingSpeak n8n Automation │ │ │ ▼ ▼ ▼ Voice Alerts Cloud Dashboard Google Sheets 4. Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller MQ-2 Smoke Sensor 1 Smoke detection Flame Sensor Module 1 Fire detection DHT22 Sensor 1 Temperature monitoring Buzzer Module 1 Local alarm LED Indicator 2 Status indication Breadboard 1 Circuit assembly Jumper Wires Multiple Connections 5V Power Supply 1 Power source WiFi Router 1 Internet connection 5. Circuit Schematic Diagram ESP32 CONNECTIONS MQ2 Sensor VCC → 3.3V GND → GND AOUT → GPIO34 Flame Sensor VCC → 3.3V GND → GND DOUT → GPIO27 DHT22 VCC → 3.3V GND → GND DATA → GPIO4 Buzzer + → GPIO26 - → GND Red LED + → GPIO25 - → GND 6. Working Principle The ESP32 continuously reads data from: MQ2 smoke sensor Flame sensor DHT22 temperature sensor If: Smoke exceeds threshold Flame is detected Temperature becomes dangerous Then: Local buzzer activates Telegram alert is sent Voice notification generated Data uploaded to ThingSpeak Event logged in Google Sheets n8n AI workflow processes event 7. Flowchart START │ ▼ Initialize ESP32 │ ▼ Connect WiFi │ ▼ Read Sensor Data │ ▼ Smoke/Fire Detected? ┌────┴────┐ YES NO │ │ ▼ ▼ Activate Alarm Continue Monitoring │ ▼ Send Telegram Alert │ ▼ Trigger Voice Alert │ ▼ Upload to ThingSpeak │ ▼ Store in Google Sheets │ ▼ AI Analysis via n8n │ ▼ LOOP 8. ESP32 Source Code (Arduino IDE) #include #include #include "DHT.h" #define DHTPIN 4 #define DHTTYPE DHT22 #define MQ2_PIN 34 #define FLAME_PIN 27 #define BUZZER 26 #define LED 25 DHT dht(DHTPIN, DHTTYPE); const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String botToken = "YOUR_TELEGRAM_BOT_TOKEN"; String chatID = "YOUR_CHAT_ID"; String thingSpeakApi = "YOUR_THINGSPEAK_API_KEY"; void setup() { Serial.begin(115200); pinMode(FLAME_PIN, INPUT); pinMode(BUZZER, OUTPUT); pinMode(LED, OUTPUT); dht.begin(); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(1000); Serial.println("Connecting..."); } Serial.println("WiFi Connected"); } void sendTelegram(String message) { HTTPClient http; String url = "https://api.telegram.org/bot" + botToken + "/sendMessage?chat_id=" + chatID + "&text=" + message; http.begin(url); http.GET(); http.end(); } void sendThingSpeak(float temp, int smoke) { HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + thingSpeakApi + "&field1=" + String(temp) + "&field2=" + String(smoke); http.begin(url); http.GET(); http.end(); } void loop() { float temperature = dht.readTemperature(); int smoke = analogRead(MQ2_PIN); int flame = digitalRead(FLAME_PIN); Serial.println(smoke); if (smoke > 2500 || flame == 0 || temperature > 50) { digitalWrite(BUZZER, HIGH); digitalWrite(LED, HIGH); sendTelegram("🔥 FIRE ALERT DETECTED!"); sendThingSpeak(temperature, smoke); delay(5000); } else { digitalWrite(BUZZER, LOW); digitalWrite(LED, LOW); } delay(2000); } 9. n8n Automation Workflow Workflow Features The n8n automation: Receives webhook data Checks fire thresholds Generates AI response Sends Telegram message Creates voice alert Logs to Google Sheets Updates dashboards n8n Workflow Steps Webhook Trigger Parse Sensor Data AI Decision Node Telegram Notification Text-to-Speech Node Google Sheets Append Emergency Alert Routing Sample n8n Workflow JSON { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook" }, { "name": "Telegram", "type": "n8n-nodes-base.telegram" }, { "name": "Google Sheets", "type": "n8n-nodes-base.googleSheets" } ] } 10. Telegram Bot Setup Step 1: Create Bot Open Telegram and search: Telegram Search for: BotFather Commands: /newbot Copy: Bot Token Step 2: Get Chat ID Send message to your bot. Open: https://api.telegram.org/bot/getUpdates Copy: Chat ID 11. Google Sheets Integration Requirements Google Cloud Project Google Sheets API Service Account Steps Create Google Sheet Enable Sheets API Generate credentials JSON Connect Google Sheets node in n8n Map: Time Smoke Level Temperature Alert Status 12. ThingSpeak Cloud Dashboard Setup Using: ThingSpeak Official Platform Steps Create account Create channel Add fields: Temperature Smoke Fire Status Copy Write API Key Paste into ESP32 code 13. AI Power Consumption Prediction Logic The AI agent estimates power usage based on: Sensor sampling rate WiFi transmission frequency Alarm activation duration CPU active time Formula P=V×I Where: P = Power V = Voltage I = Current Prediction Model predicted_power = (sensor_reads * 0.02) + (wifi_transmissions * 0.15) + (alarm_usage * 0.4) AI can optimize: Sleep intervals Upload timing Sensor polling frequency 14. Voice Notification Automation Workflow Fire Detected ↓ n8n Receives Data ↓ AI Generates Alert Text ↓ Text-to-Speech Conversion ↓ Telegram Voice Message ↓ Emergency Notification Example Voice Alert “Warning! Fire and smoke detected in the monitored area. Please take immediate action.” 15. Cloud Dashboard Features Dashboard Displays Real-time temperature Smoke graph Alert history AI prediction status Device online/offline Notification logs 16. Future Enhancements AI Enhancements Machine learning fire prediction Computer vision smoke detection AI camera integration Edge AI analytics Hardware Enhancements GSM backup alerts Solar-powered ESP32 Battery backup Multi-room deployment Software Enhancements Mobile app MQTT architecture Firebase integration Voice assistant support 17. Deployment Guide Suitable Locations Smart homes Industries Warehouses Laboratories Server rooms Smart buildings 18. Advantages ✅ Low cost ✅ Real-time monitoring ✅ AI-enabled automation ✅ Cloud accessible ✅ Expandable architecture ✅ Remote alerts ✅ Energy efficient 19. Applications Smart Home Safety Industrial Fire Detection Warehouse Monitoring Forest Fire Early Warning Smart Cities Data Center Protection 20. Conclusion This project combines: IoT ESP32 AI automation Cloud monitoring Real-time emergency alerts to create an intelligent fire and smoke detection ecosystem capable of proactive safety monitoring and smart emergency response. The integration of: ESP32 n8n workflows Telegram voice alerts Google Sheets ThingSpeak AI-based automation