AI Smart Railway Track Crack Detection Robot

AI Smart Railway Track Crack Detection Robot Agentic IoT using ESP32 + AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard

AI Smart Railway Track Crack Detection Robot Agentic IoT using ESP32 + AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard 1. Project Overview The AI Smart Railway Track Crack Detection Robot is an intelligent autonomous monitoring system designed to detect cracks and abnormalities in railway tracks using sensors and AI-based logic. The robot continuously scans railway tracks using ultrasonic and vibration sensors. The collected data is processed by an ESP32 microcontroller and transmitted to cloud services through Wi-Fi. The system integrates: ESP32-based IoT controller Crack detection sensors AI-powered anomaly prediction n8n automation workflows Telegram instant alerts Telegram voice notifications Google Sheets logging ThingSpeak cloud dashboard Agentic AI monitoring logic This project can help reduce railway accidents by detecting track faults early and automatically notifying railway authorities. 2. Key Features ✅ Real-time railway crack detection ✅ ESP32 Wi-Fi enabled IoT monitoring ✅ AI-based abnormality prediction ✅ Telegram instant alerts ✅ Telegram voice notifications ✅ Google Sheets automatic logging ✅ ThingSpeak cloud visualization ✅ Autonomous Agentic IoT workflow ✅ Cloud monitoring dashboard ✅ Low-power intelligent operation ✅ Expandable for GPS and camera AI 3. System Architecture Railway Track ↓ Sensors (Ultrasonic + Vibration + IR) ↓ ESP32 Controller ↓ Wi-Fi Cloud APIs / n8n ↓ ┌──────────────────────┐ │ Telegram Bot Alerts │ │ Voice Notifications │ │ Google Sheets Logs │ │ ThingSpeak Dashboard│ └──────────────────────┘ ↓ AI Prediction Engine ↓ Maintenance Decision Support 4. Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller Ultrasonic Sensor HC-SR04 1 Crack distance detection Vibration Sensor SW-420 1 Detect rail vibration anomalies IR Sensor Module 1 Track surface monitoring DC Gear Motors 2 Robot movement L298N Motor Driver 1 Motor control Robot Chassis 1 Mechanical platform Wheels 2 Robot mobility Li-ion Battery Pack 1 Power supply Voltage Regulator 1 Stable voltage Jumper Wires Multiple Connections Breadboard / PCB 1 Circuit assembly Buzzer 1 Local alert LED Indicators 2 Status indication Wi-Fi Router/Hotspot 1 Internet connectivity 5. Working Principle Robot moves along railway track. Ultrasonic sensor continuously measures surface gap. If abnormal distance is detected: Crack condition triggered. ESP32 sends sensor data to: n8n workflow ThingSpeak cloud Google Sheets n8n automation: Generates Telegram alerts Sends voice notifications AI logic predicts: Power consumption Sensor anomaly patterns Maintenance risk score 6. Circuit Schematic Diagram ESP32 Connections ESP32 Pin Connected Device GPIO 5 Ultrasonic Trigger GPIO 18 Ultrasonic Echo GPIO 19 Vibration Sensor GPIO 21 IR Sensor GPIO 25 Motor Driver IN1 GPIO 26 Motor Driver IN2 GPIO 27 Motor Driver IN3 GPIO 14 Motor Driver IN4 GPIO 2 Buzzer 5V Sensors VCC GND Common Ground 7. Flowchart START ↓ Initialize ESP32 & Wi-Fi ↓ Read Sensors ↓ Analyze Crack Condition ↓ Is Crack Detected? ┌───────────────┐ │ YES │ NO ↓ ↓ Send Alerts Continue Monitoring ↓ Upload to Cloud ↓ AI Prediction ↓ Store in Google Sheets ↓ Voice Notification ↓ Continue Monitoring 8. ESP32 Source Code (Arduino IDE) #include #include const char* ssid = "YOUR_WIFI_NAME"; const char* password = "YOUR_WIFI_PASSWORD"; #define TRIG_PIN 5 #define ECHO_PIN 18 #define BUZZER 2 String webhookURL = "YOUR_N8N_WEBHOOK_URL"; void setup() { Serial.begin(115200); pinMode(TRIG_PIN, OUTPUT); pinMode(ECHO_PIN, INPUT); pinMode(BUZZER, OUTPUT); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(1000); Serial.println("Connecting..."); } Serial.println("WiFi Connected"); } float getDistance() { digitalWrite(TRIG_PIN, LOW); delayMicroseconds(2); digitalWrite(TRIG_PIN, HIGH); delayMicroseconds(10); digitalWrite(TRIG_PIN, LOW); long duration = pulseIn(ECHO_PIN, HIGH); float distance = duration * 0.034 / 2; return distance; } void loop() { float distance = getDistance(); Serial.println(distance); if(distance > 15) { digitalWrite(BUZZER, HIGH); if(WiFi.status() == WL_CONNECTED) { HTTPClient http; http.begin(webhookURL); http.addHeader("Content-Type", "application/json"); String payload = "{\"crack\":\"DETECTED\",\"distance\":" + String(distance) + "}"; int httpResponseCode = http.POST(payload); Serial.println(httpResponseCode); http.end(); } } else { digitalWrite(BUZZER, LOW); } delay(3000); } 9. n8n Automation Workflow Workflow Functions The n8n workflow performs: Receives ESP32 webhook data Detects crack event Sends Telegram message Converts text to voice Logs to Google Sheets Updates AI prediction database n8n Workflow Structure Webhook Trigger ↓ IF Crack Detected ↓ ┌───────────────┬────────────────┬────────────────┐ ↓ ↓ ↓ Telegram Bot Google Sheets ThingSpeak API ↓ Text-to-Speech ↓ Telegram Voice Alert n8n Workflow JSON { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook" }, { "name": "IF Crack", "type": "n8n-nodes-base.if" }, { "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: /start /newbot Copy generated BOT TOKEN. Step 2: Get Chat ID Send a message to your bot. Open: Telegram API GetUpdates Example: https://api.telegram.org/bot/getUpdates Copy chat ID. 11. Telegram Voice Notification Automation Voice Alert Logic When crack detected: "Warning! Railway track crack detected. Immediate inspection required." n8n uses: Google TTS ElevenLabs API Telegram voice upload 12. Google Sheets Integration Create Google Sheet Example columns: Time Distance Crack Status Location Google Cloud Setup Enable: Google Sheets API Google Drive API Create: OAuth credentials Connect Google account in n8n. 13. ThingSpeak Cloud Dashboard Setup Create account on: ThingSpeak Create Channel Fields Field Data Field 1 Distance Field 2 Crack Status Field 3 AI Risk Score ESP32 Upload API Example String url = "http://api.thingspeak.com/update?api_key=YOUR_KEY&field1=" + String(distance); 14. AI Power Consumption Prediction Logic Objective Predict battery usage and optimize robot operation. AI Parameters Parameter Description Motor runtime Robot movement duration Sensor activity Number of readings Wi-Fi transmission Network usage Alert frequency Number of alerts Simple AI Formula Battery prediction: P=V×I Remaining battery estimation: Battery Life= Current Consumption Battery Capacity ​ AI Decision Logic IF battery < 20% Reduce sensor frequency Disable continuous movement Enable low-power mode 15. ThingSpeak AI Analytics ThingSpeak can: Visualize sensor graphs Generate anomaly trends Predict maintenance frequency Monitor robot uptime 16. Future Enhancements Advanced AI Features Computer Vision Add ESP32-CAM Crack image detection using CNN GPS Tracking Real-time robot location GSM Module SMS alerts without Wi-Fi Solar Charging Autonomous outdoor charging Edge AI TinyML on ESP32 Digital Twin Railway virtual monitoring system 17. Deployment Guide Railway Testing Procedure Step 1 Test sensors on small track model. Step 2 Calibrate crack threshold values. Step 3 Deploy on low-speed railway section. Step 4 Monitor cloud dashboard. Step 5 Train AI model using collected data. 18. Safety Considerations Use insulated battery enclosure Waterproof sensor casing Add emergency stop switch Ensure motor speed control Avoid live railway testing without permission 19. Advantages ✅ Low-cost monitoring ✅ Real-time automation ✅ Reduced human inspection ✅ Early fault detection ✅ Cloud-enabled analytics ✅ AI-assisted maintenance 20. Applications Railway safety systems Smart transportation Industrial track monitoring Metro rail maintenance Autonomous inspection robots 21. Project Outcome The system demonstrates how AI + IoT + Automation + Cloud Computing can modernize railway infrastructure using low-cost embedded hardware. The combination of: ESP32 n8n workflows Telegram automation Google Sheets logging ThingSpeak analytics AI prediction creates a complete Agentic Smart Railway Monitoring Ecosystem.

AI Smart Parking System with Empty Slot Detection and Mobile App

AI Smart Parking System with Empty Slot Detection and Mobile App Agentic IoT using ESP32 + AI + n8n + Telegram Voice Alerts + Google Sheets + ThingSpeak

AI Smart Parking System with Empty Slot Detection and Mobile App Agentic IoT using ESP32 + AI + n8n + Telegram Voice Alerts + Google Sheets + ThingSpeak 1. Project Overview This project is an AI-powered Smart Parking Management System that detects empty parking slots using sensors connected to an ESP32. The system uploads parking data to the cloud, automates workflows using n8n, sends Telegram notifications with voice alerts, stores logs in Google Sheets, and visualizes data on ThingSpeak dashboards. The system can: Detect occupied/empty parking slots Display available parking spaces on web/mobile dashboard Send instant Telegram alerts Generate AI-based parking predictions Store historical parking data Trigger voice notifications Predict power usage and parking trends Work as an Agentic IoT automation system 2. System Architecture Ultrasonic/IR Sensors ↓ ESP32 ↓ WiFi ThingSpeak Cloud ↓ n8n ↙ ↓ ↘ Telegram AI Logic Google Sheets Alerts Analysis Data Logging 3. Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller IR Sensors / Ultrasonic Sensors 4 Vehicle detection OLED Display (Optional) 1 Display slot status Buzzer 1 Local alerts LEDs 4 Slot indication Breadboard 1 Connections Jumper Wires Several Wiring 5V Power Supply 1 Power source WiFi Router 1 Internet connectivity 4. Parking Slot Logic Sensor State Slot Status HIGH Empty LOW Occupied 5. Circuit Schematic Diagram IR Sensor 1 → GPIO 13 IR Sensor 2 → GPIO 12 IR Sensor 3 → GPIO 14 IR Sensor 4 → GPIO 27 LED1 → GPIO 18 LED2 → GPIO 19 LED3 → GPIO 21 LED4 → GPIO 22 Buzzer → GPIO 23 VCC → 5V GND → GND 6. Flowchart START ↓ Initialize ESP32 ↓ Connect WiFi ↓ Read Sensors ↓ Check Empty Slots ↓ Upload Data to ThingSpeak ↓ Trigger n8n Webhook ↓ Send Telegram Alert ↓ Store Data in Google Sheets ↓ Run AI Prediction ↓ Repeat 7. ESP32 Source Code (Arduino IDE) #include #include const char* ssid = "YOUR_WIFI_NAME"; const char* password = "YOUR_WIFI_PASSWORD"; String apiKey = "THINGSPEAK_API_KEY"; #define S1 13 #define S2 12 #define S3 14 #define S4 27 void setup() { Serial.begin(115200); pinMode(S1, INPUT); pinMode(S2, INPUT); pinMode(S3, INPUT); pinMode(S4, INPUT); WiFi.begin(ssid, password); while(WiFi.status() != WL_CONNECTED) { delay(1000); Serial.println("Connecting..."); } Serial.println("WiFi Connected"); } void loop() { int slot1 = digitalRead(S1); int slot2 = digitalRead(S2); int slot3 = digitalRead(S3); int slot4 = digitalRead(S4); int emptySlots = 0; if(slot1 == HIGH) emptySlots++; if(slot2 == HIGH) emptySlots++; if(slot3 == HIGH) emptySlots++; if(slot4 == HIGH) emptySlots++; if(WiFi.status() == WL_CONNECTED) { HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(slot1) + "&field2=" + String(slot2) + "&field3=" + String(slot3) + "&field4=" + String(slot4) + "&field5=" + String(emptySlots); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } delay(15000); } 8. ThingSpeak Cloud Dashboard Setup Create Channel Create account in ThingSpeak Create new channel Add fields: Field Description Field1 Slot1 Field2 Slot2 Field3 Slot3 Field4 Slot4 Field5 Empty Slots Copy Write API Key Paste into ESP32 code 9. n8n Automation Workflow Features Receives data from ThingSpeak Sends Telegram notifications Converts alerts into voice messages Updates Google Sheets Performs AI prediction logic Install n8n n8n Official Website 10. n8n Workflow JSON { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook", "parameters": { "path": "parking-data", "httpMethod": "POST" } }, { "name": "Telegram", "type": "n8n-nodes-base.telegram", "parameters": { "text": "Parking Slot Updated" } }, { "name": "Google Sheets", "type": "n8n-nodes-base.googleSheets", "parameters": { "operation": "append" } } ] } 11. Telegram Bot Setup Create Bot Open Telegram Search for Telegram Search: @BotFather Create bot using: /newbot Copy BOT TOKEN Get Chat ID Send message to your bot then open: https://api.telegram.org/bot/getUpdates 12. Telegram Voice Notification Automation Workflow ESP32 → n8n → Text-to-Speech → Telegram Voice Message Example Voice Alert "Attention! Only two parking slots are available." TTS Services You can use: Google Text-to-Speech ElevenLabs 13. Google Sheets Integration Setup Steps Create sheet in Google Sheets Columns: | Timestamp | Slot1 | Slot2 | Slot3 | Slot4 | Empty Slots | Connect Google account in n8n Use Append Row operation 14. AI Power Consumption Prediction Logic The AI logic predicts: Peak parking usage Low-usage hours ESP32 power consumption trends Expected occupancy patterns Prediction Formula Using moving average: P avg ​ = n P 1 ​ +P 2 ​ +P 3 ​ +⋯+P n ​ ​ Where: P avg ​ = average power P n ​ = sensor power readings Occupancy Prediction O t ​ = n ∑ i=1 n ​ S i ​ ​ Where: O t ​ = predicted occupancy S i ​ = slot occupancy values 15. AI Agent Features The Agentic IoT system can: Analyze parking availability Automatically notify users Predict congestion Trigger maintenance alerts Generate smart reports Recommend optimal parking usage 16. Web Dashboard Features Dashboard Displays Total parking slots Empty slots Occupied slots Real-time sensor status Historical graphs AI predictions 17. Mobile App Features You can create mobile app using: MIT App Inventor Flutter Blynk IoT Platform 18. Advanced Enhancements Additions AI Camera Detection Use: ESP32-CAM YOLO Object Detection License Plate Recognition Use: OCR OpenCV Cloud Database Use: Firebase MongoDB GPS Parking Navigation Guide drivers to empty slots QR Ticket System Automatic billing system 19. Deployment Guide Hardware Deployment Install sensors in parking area Use waterproof casing Ensure stable WiFi coverage Software Deployment Flash ESP32 code Configure ThingSpeak API Deploy n8n workflow Connect Telegram bot Test notifications 20. Testing Procedure Test Expected Result Vehicle enters Slot occupied Vehicle exits Slot empty Empty slot count Updates live Telegram alert Received instantly Google Sheet Data appended ThingSpeak graph Updated 21. Real-Time Notification Examples Telegram Text Alert 🚗 Parking Update: Available Slots: 2 Occupied Slots: 2 Voice Alert "Parking area almost full. Only one slot remaining." 22. Advantages of This Project Smart city ready Low-cost implementation Real-time monitoring AI-driven automation Scalable architecture Cloud enabled Mobile accessible 23. Applications Shopping malls Smart cities Colleges Hospitals Airports Offices Residential parking 24. Future Scope Edge AI deployment Solar-powered ESP32 Machine learning analytics Multi-floor parking management Face recognition entry Autonomous vehicle integration 25. Conclusion This AI Smart Parking System combines: ESP32 IoT hardware AI analytics Cloud dashboards n8n automation Telegram alerts Voice notifications Google Sheets logging to create a complete smart parking ecosystem suitable for modern smart-city applications.

AI Smart Irrigation System with Weather Prediction and Soil Analysis

AI Smart Irrigation System with Weather Prediction and Soil Analysis AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Irrigation System with Weather Prediction and Soil Analysis AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview This project is an intelligent IoT-based smart irrigation system using an ESP32 microcontroller integrated with: Soil moisture sensing Weather prediction logic AI-based irrigation decisions n8n automation workflows Telegram alerts + voice notifications Google Sheets logging ThingSpeak cloud dashboard Agentic AI automation behavior The system automatically: Monitors soil moisture Predicts irrigation need Controls water pump Sends voice/text alerts Logs sensor data to cloud Learns water usage patterns Reduces water wastage 2. System Features Core Features Real-time soil moisture monitoring Automatic irrigation pump control Temperature & humidity monitoring Rain/weather prediction support AI-based irrigation scheduling Remote cloud monitoring AI + Automation Features Agentic AI irrigation decisions Predictive water consumption analysis Telegram voice notifications Smart alerts using n8n workflows Cloud analytics dashboard Historical data storage 3. Hardware Components List Component Quantity ESP32 Dev Board 1 Capacitive Soil Moisture Sensor 1 DHT11/DHT22 Sensor 1 Relay Module 5V 1 Mini Water Pump 1 Breadboard 1 Jumper Wires Several 5V Power Supply 1 ThingSpeak Account 1 Telegram Bot 1 Google Account 1 n8n Cloud/Self-hosted 1 4. Working Principle The system continuously reads: Soil moisture Temperature Humidity The ESP32: Sends data to ThingSpeak Sends webhook data to n8n AI logic decides irrigation status Relay activates water pump Notifications sent to Telegram Data logged to Google Sheets 5. System Architecture [Soil Sensor] ----\ [DHT Sensor] ------> ESP32 ---> WiFi ---> n8n Workflow | +--> ThingSpeak Dashboard | +--> Google Sheets | +--> Telegram Bot | +--> AI Decision Engine 6. Circuit Schematic Diagram SOIL SENSOR VCC -> 3.3V GND -> GND AOUT -> GPIO34 DHT11 VCC -> 3.3V GND -> GND DATA -> GPIO4 RELAY MODULE VCC -> 5V GND -> GND IN -> GPIO26 WATER PUMP Connected through Relay 7. Flowchart START | Read Sensors | Check Moisture Level | Is Soil Dry? / \ YES NO | | Turn ON Pump | | Send Alert | | Upload Data | | Log to Sheets | | Repeat Loop 8. ESP32 Source Code (Arduino IDE) #include #include #include "DHT.h" #define DHTPIN 4 #define DHTTYPE DHT11 #define SOIL_PIN 34 #define RELAY_PIN 26 const char* ssid = "YOUR_WIFI_NAME"; const char* password = "YOUR_WIFI_PASSWORD"; String thingspeakApiKey = "YOUR_THINGSPEAK_API_KEY"; DHT dht(DHTPIN, DHTTYPE); void setup() { Serial.begin(115200); pinMode(RELAY_PIN, OUTPUT); digitalWrite(RELAY_PIN, HIGH); dht.begin(); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(1000); Serial.println("Connecting..."); } Serial.println("WiFi Connected"); } void loop() { int soilValue = analogRead(SOIL_PIN); float temp = dht.readTemperature(); float hum = dht.readHumidity(); Serial.print("Soil: "); Serial.println(soilValue); bool soilDry = soilValue > 2500; if (soilDry) { digitalWrite(RELAY_PIN, LOW); } else { digitalWrite(RELAY_PIN, HIGH); } if(WiFi.status()== WL_CONNECTED){ HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + thingspeakApiKey + "&field1=" + String(soilValue) + "&field2=" + String(temp) + "&field3=" + String(hum); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } delay(15000); } 9. AI Irrigation Prediction Logic The AI logic estimates irrigation need based on: Soil moisture trend Temperature Humidity Time of day Weather forecast Basic AI Decision Formula I=w 1 ​ M+w 2 ​ T−w 3 ​ H+w 4 ​ W Where: I = Irrigation score M = Moisture deficit T = Temperature H = Humidity W = Weather prediction factor If: I>Threshold → Pump ON 10. n8n Workflow Logic Workflow Modules Webhook Trigger HTTP Request Node IF Condition Telegram Node Google Sheets Node Text-to-Speech API ThingSpeak Update 11. Sample n8n Workflow JSON { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook" }, { "name": "IF Soil Dry", "type": "n8n-nodes-base.if" }, { "name": "Telegram Alert", "type": "n8n-nodes-base.telegram" }, { "name": "Google Sheets", "type": "n8n-nodes-base.googleSheets" } ] } 12. Telegram Bot Setup Step 1 — Create Bot Open Telegram and search: Telegram Search: @BotFather Commands: /start /newbot Copy: BOT TOKEN Step 2 — Get Chat ID Send a message to your bot. Open: https://api.telegram.org/bot/getUpdates Copy: chat_id 13. Telegram Voice Notification Automation n8n can generate voice alerts using: Google Text-to-Speech ElevenLabs API gTTS Example Voice Message: Warning! Soil moisture is low. Irrigation pump activated automatically. 14. Google Sheets Integration Create a sheet: Time Soil Moisture Temperature Humidity Pump Status n8n appends rows automatically. Useful for: Analytics AI training Water usage reports 15. ThingSpeak Cloud Dashboard Setup Create account at: ThingSpeak Create Channel Fields Field Purpose Field1 Soil Moisture Field2 Temperature Field3 Humidity Field4 Pump Status Dashboard Widgets Gauge chart Line graph Real-time analytics Historical trends 16. Weather Prediction Integration Use: OpenWeather API Tomorrow.io API ESP32/n8n checks: Rain probability Temperature forecast Humidity forecast If rain expected: Skip irrigation 17. AI Power Consumption Prediction The system predicts pump power usage. Power Formula P=V×I×t Where: P = Power consumption V = Voltage I = Current t = Runtime AI estimates: Daily energy use Monthly water consumption Cost optimization 18. Advanced Agentic AI Features AI Agent Can: Decide irrigation timing Delay watering during rain Learn soil behavior Optimize water usage Predict dry conditions Generate smart reports 19. Future Enhancements Hardware Upgrades Solar-powered irrigation Multiple zone irrigation pH sensor integration Water flow sensor ESP32-CAM monitoring AI Enhancements Machine learning irrigation prediction LSTM moisture forecasting Crop-specific irrigation AI Edge AI using TinyML Cloud Enhancements Mobile app dashboard Firebase integration AWS IoT Core MQTT broker system 20. Deployment Guide Step-by-Step Deployment Hardware Assemble circuit Connect sensors Upload ESP32 code Cloud Configure ThingSpeak Setup Telegram bot Create Google Sheet Import n8n workflow Testing Dry soil manually Verify pump activation Verify Telegram alert Verify dashboard update 21. Expected Output Dashboard Shows Soil moisture % Temperature Humidity Pump status Water usage trends Telegram Alerts AI Irrigation Alert: Soil Dry Detected Pump Activated Temperature: 32°C Humidity: 45% 22. Applications Smart agriculture Greenhouse automation Precision farming Garden automation Water conservation systems 23. Conclusion This AI-powered smart irrigation system combines: ESP32 IoT Cloud computing AI prediction Automation workflows Telegram voice alerts Real-time dashboards The project demonstrates a modern Agentic IoT architecture suitable for: Smart farming Research projects Final-year engineering projects

AI Smart Helmet for Accident Detection and Rider Safety

AI Smart Helmet for Accident Detection and Rider Safety ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard

AI Smart Helmet for Accident Detection and Rider Safety ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard 1. Project Overview This project is an AI-powered Smart Helmet System designed to improve rider safety using: ESP32 Crash detection sensors Helmet wearing detection Alcohol detection GPS tracking Cloud IoT dashboard n8n AI automation Telegram voice alerts Google Sheets logging ThingSpeak monitoring The helmet continuously monitors rider conditions and accident events. If an accident occurs: ESP32 detects crash/fall GPS location captured Data uploaded to ThingSpeak n8n automation triggered Telegram voice + text alerts sent Emergency contact notified Data stored in Google Sheets AI predicts battery/power consumption patterns 2. Features Core Features ✅ Accident detection ✅ Helmet wearing detection ✅ Alcohol detection ✅ Rider motion monitoring ✅ GPS live location ✅ Emergency SOS alerts ✅ Telegram voice notifications ✅ Google Sheets logging ✅ ThingSpeak cloud dashboard ✅ AI-based power usage prediction ✅ Real-time IoT monitoring 3. System Architecture Helmet Sensors ↓ ESP32 Controller ↓ WiFi / Internet ↓ ThingSpeak Cloud ↓ n8n Automation Server ↓ ├── Telegram Bot Alerts ├── Telegram Voice Alerts ├── Google Sheets Logging └── AI Agent Processing 4. Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller MPU6050 Accelerometer + Gyroscope 1 Accident/fall detection MQ3 Alcohol Sensor 1 Alcohol detection GPS Module NEO-6M 1 Live location IR Sensor 1 Helmet wear detection Buzzer 1 Local alarm LED Indicators 2 Status indication Push Button 1 Emergency SOS 18650 Battery 1 Portable power TP4056 Charging Module 1 Battery charging Jumper Wires — Connections Helmet 1 Mounting platform 5. Working Principle Accident Detection The MPU6050 detects: Sudden impact High acceleration Abnormal tilt angle If threshold exceeds: Impact > 2.5g OR Tilt angle > 60° then accident event triggered. Helmet Detection IR sensor checks whether helmet is worn. If not worn: Buzzer activates Engine relay can remain OFF Alcohol Detection MQ3 detects alcohol concentration. If alcohol level exceeds threshold: Warning alert generated Vehicle ignition can be disabled GPS Tracking GPS module continuously updates: Latitude Longitude Used in emergency alerts. 6. Circuit Connections ESP32 Pin Mapping Module ESP32 Pin MPU6050 SDA GPIO21 MPU6050 SCL GPIO22 MQ3 Analog GPIO34 IR Sensor GPIO27 GPS TX GPIO16 GPS RX GPIO17 Buzzer GPIO25 LED GPIO26 SOS Button GPIO14 7. Circuit Schematic Diagram +------------------+ | ESP32 | | | MPU6050 SDA | GPIO21 | MPU6050 SCL | GPIO22 | MQ3 OUT ----| GPIO34 | IR Sensor --| GPIO27 | GPS TX -----| GPIO16 | GPS RX -----| GPIO17 | Buzzer -----| GPIO25 | LED --------| GPIO26 | SOS Button -| GPIO14 | +------------------+ 8. Flowchart START ↓ Initialize Sensors ↓ Connect WiFi ↓ Read Sensor Data ↓ Helmet Worn? ┌───────┴────────┐ NO YES ↓ ↓ Alert Check Alcohol ↓ Alcohol Detected? ┌─────┴─────┐ YES NO ↓ ↓ Warning Monitor MPU6050 ↓ Accident Detected? ┌────┴────┐ YES NO ↓ ↓ Send Cloud Data Loop ↓ Trigger n8n Workflow ↓ Telegram + Voice + Sheets ↓ END 9. ESP32 Source Code (Arduino IDE) #include #include #include #include #include #include MPU6050 mpu; TinyGPSPlus gps; HardwareSerial gpsSerial(1); const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String apiKey = "THINGSPEAK_API_KEY"; #define MQ3_PIN 34 #define IR_PIN 27 #define BUZZER 25 #define LED 26 float ax, ay, az; void setup() { Serial.begin(115200); pinMode(IR_PIN, INPUT); pinMode(BUZZER, OUTPUT); pinMode(LED, OUTPUT); Wire.begin(); mpu.initialize(); gpsSerial.begin(9600, SERIAL_8N1, 16, 17); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(500); Serial.print("."); } Serial.println("WiFi Connected"); } void loop() { mpu.getAcceleration(&ax, &ay, &az); float impact = sqrt(ax * ax + ay * ay + az * az) / 16384.0; int alcohol = analogRead(MQ3_PIN); int helmet = digitalRead(IR_PIN); while (gpsSerial.available()) { gps.encode(gpsSerial.read()); } double lat = gps.location.lat(); double lng = gps.location.lng(); if (helmet == LOW) { digitalWrite(BUZZER, HIGH); } if (alcohol > 2500) { Serial.println("Alcohol Detected"); } if (impact > 2.5) { digitalWrite(BUZZER, HIGH); digitalWrite(LED, HIGH); if (WiFi.status() == WL_CONNECTED) { HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(impact) + "&field2=" + String(lat, 6) + "&field3=" + String(lng, 6); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } } delay(2000); } 10. ThingSpeak Cloud Setup Create Channel Go to: ThingSpeak Create fields: Field Data Field 1 Impact Force Field 2 Latitude Field 3 Longitude Field 4 Alcohol Level Field 5 Helmet Status Copy: Write API Key Channel ID 11. n8n Automation Workflow Install n8n Use: n8n Official Website Workflow Logic ThingSpeak Webhook ↓ IF Accident Detected ↓ Generate AI Summary ↓ Telegram Message ↓ Telegram Voice Alert ↓ Google Sheets Logging 12. n8n Workflow JSON { "nodes": [ { "parameters": {}, "name": "Webhook", "type": "n8n-nodes-base.webhook", "typeVersion": 1, "position": [250, 300] }, { "parameters": { "chatId": "YOUR_CHAT_ID", "text": "🚨 Accident Detected!" }, "name": "Telegram", "type": "n8n-nodes-base.telegram", "typeVersion": 1, "position": [500, 300] } ], "connections": { "Webhook": { "main": [ [ { "node": "Telegram", "type": "main", "index": 0 } ] ] } } } 13. Telegram Bot Setup Step 1 — Create Bot Open: BotFather Telegram Bot Setup Commands: /newbot Copy: BOT TOKEN Step 2 — Get Chat ID Open: https://api.telegram.org/bot/getUpdates Copy chat ID. 14. Telegram Voice Notification Automation Method n8n converts alert text to speech using: Google TTS ElevenLabs API gTTS Python API Voice Message Example: Emergency Alert. Accident detected. Location shared to emergency contacts. 15. Google Sheets Integration Create spreadsheet columns: Timestamp Impact Latitude Longitude Alcohol Helmet In n8n: Google Sheets Node ↓ Append Row Useful for: Analytics Accident history AI training dataset 16. AI Power Consumption Prediction Logic Objective Predict remaining battery life. Inputs WiFi usage GPS activity Sensor sampling rate Alert frequency AI Formula Simple linear prediction: Battery Remaining=Battery Capacity−(WiFi+GPS+Sensor+Alert Power)×Time Advanced AI Future model: TinyML Edge AI LSTM battery forecasting 17. ThingSpeak Dashboard Widgets Add widgets: GPS location map Impact graph Helmet status Alcohol level Battery level 18. AI Agentic IoT Features AI Agent Responsibilities The AI agent can: ✅ Analyze accidents ✅ Predict dangerous driving ✅ Detect battery anomalies ✅ Send smart alerts ✅ Recommend charging times ✅ Generate rider safety reports 19. Future Enhancements Hardware GSM module Camera module Air quality sensor Heartbeat sensor Voice assistant AI Enhancements TinyML crash classification Rider fatigue detection Computer vision Edge AI processing Predictive maintenance 20. Deployment Guide Helmet Assembly Mount: MPU6050 at helmet center GPS on top side ESP32 rear compartment Battery in protected enclosure Power Management Use: 5V regulated supply Deep sleep mode Auto power shutdown Waterproofing Recommended: ABS enclosure Silicone seal Shockproof foam 21. Testing Procedure Test Cases Test Expected Result Helmet removed Buzzer ON Alcohol detected Warning Sudden fall Alert triggered GPS unavailable Retry Internet OFF Store locally 22. Estimated Cost Component Approx Cost ESP32 ₹500 MPU6050 ₹150 GPS Module ₹450 MQ3 Sensor ₹120 Battery ₹300 Miscellaneous ₹500 Total Estimated Cost ₹2000–₹3000 23. Applications Smart transportation Rider safety Fleet management Delivery services Emergency response systems Insurance telematics 24. Conclusion This project combines: IoT AI Cloud automation ESP32 embedded systems n8n workflows Telegram alerts Real-time monitoring to build a next-generation AI Smart Helmet Safety System capable of reducing accident response time and improving rider safety using intelligent automation. Useful Resources ESP32 Official Documentation Arduino IDE ThingSpeak Platform n8n Documentation Telegram Bot API Google Sheets API

AI Smart Health Monitoring System with Disease Prediction

AI Smart Health Monitoring System with Disease Prediction AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

1. Project Overview This project is an advanced AI-enabled Smart Health Monitoring System using: Espressif Systems ESP32 IoT cloud monitoring AI disease prediction logic Agentic automation using n8n Telegram voice notifications Google Sheets data logging ThingSpeak cloud analytics dashboard The system continuously monitors: Heart Rate SpO₂ (Blood Oxygen) Body Temperature ECG (optional) Blood Pressure (optional simulated) Motion/Fall Detection AI logic predicts possible diseases such as: Fever Hypoxia Tachycardia Bradycardia Stress Cardiac Risk When abnormal values are detected: ESP32 uploads sensor data to cloud n8n automation triggers AI logic Telegram bot sends: Text alert Voice alert Data stored in Google Sheets ThingSpeak dashboard visualizes health trends 2. System Architecture Sensors → ESP32 → WiFi → ThingSpeak Cloud ↓ n8n Webhook ↓ AI Prediction Logic ↓ ┌───────────────┴───────────────┐ ↓ ↓ Telegram Alerts Google Sheets Voice + Text Alerts Health Logs 3. Hardware Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller MAX30102 Pulse Oximeter 1 Heart rate + SpO₂ DS18B20 Temperature Sensor 1 Body temperature AD8232 ECG Sensor Optional ECG monitoring MPU6050 Optional Fall detection OLED Display SSD1306 1 Live display Breadboard 1 Prototyping Jumper Wires Several Connections USB Cable 1 Programming 5V Power Supply 1 Power source 4. Circuit Schematic Diagram ESP32 Connections MAX30102 MAX30102 ESP32 VIN 3.3V GND GND SDA GPIO21 SCL GPIO22 DS18B20 DS18B20 ESP32 VCC 3.3V GND GND DATA GPIO4 Use 4.7kΩ pull-up resistor between DATA and VCC. OLED Display OLED ESP32 VCC 3.3V GND GND SDA GPIO21 SCL GPIO22 5. Flowchart START ↓ Initialize Sensors ↓ Connect WiFi ↓ Read Sensor Data ↓ AI Health Analysis ↓ Abnormal? ┌───────┴────────┐ YES NO ↓ ↓ Send Alert Upload Data ↓ ↓ Telegram Bot ThingSpeak ↓ ↓ Google Sheets Logging ↓ Repeat Loop 6. ESP32 Source Code (Arduino IDE) Required Libraries Install from Arduino Library Manager: WiFi.h HTTPClient.h MAX30105 Adafruit SSD1306 OneWire DallasTemperature ESP32 Code #include #include #include const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String apiKey = "THINGSPEAK_API_KEY"; float temperature = 0; int heartRate = 0; int spo2 = 0; void setup() { Serial.begin(115200); WiFi.begin(ssid, password); while(WiFi.status() != WL_CONNECTED){ delay(1000); Serial.println("Connecting..."); } Serial.println("WiFi Connected"); } void loop() { // Simulated Sensor Values temperature = random(36, 39); heartRate = random(60, 130); spo2 = random(85, 100); Serial.println("Uploading Data"); if(WiFi.status()== WL_CONNECTED){ HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(temperature) + "&field2=" + String(heartRate) + "&field3=" + String(spo2); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } // AI Alert Condition if(temperature > 38 || spo2 < 90 || heartRate > 120){ sendAlert(); } delay(15000); } void sendAlert(){ HTTPClient http; String webhook = "YOUR_N8N_WEBHOOK_URL"; http.begin(webhook); http.addHeader("Content-Type", "application/json"); String json = "{"; json += "\"temperature\":" + String(temperature) + ","; json += "\"heartRate\":" + String(heartRate) + ","; json += "\"spo2\":" + String(spo2); json += "}"; int response = http.POST(json); Serial.println(response); http.end(); } 7. Disease Prediction Logic AI Rule-Based Prediction Condition Prediction Temp > 38°C Fever SpO₂ < 90% Respiratory Risk HR > 120 Tachycardia HR < 50 Bradycardia Temp + HR High Infection Risk ECG Abnormal Cardiac Alert AI Formula Risk Score=0.4(Temperature)+0.3(Heart Rate)+0.3(100−SpO 2 ​ ) Decision Threshold Risk Score > 75 → Critical Risk Score 50–75 → Moderate Risk Score < 50 → Normal 8. n8n Workflow Automation Use official website: n8n Official Website Workflow Nodes Webhook ↓ IF Node (Check Critical Values) ↓ Telegram Node ↓ Google Sheets Node ↓ Text-to-Speech API ↓ Telegram Voice Message n8n Workflow JSON { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook" }, { "name": "IF", "type": "n8n-nodes-base.if" }, { "name": "Telegram", "type": "n8n-nodes-base.telegram" }, { "name": "Google Sheets", "type": "n8n-nodes-base.googleSheets" } ] } 9. Telegram Bot Setup Use: Telegram Official Website Steps Open Telegram Search: Telegram BotFather Create new bot: /newbot Copy Bot Token Add token in n8n Telegram node 10. Google Sheets Integration Use: Google Sheets Sheet Columns Timestamp Temp HR SpO₂ Disease Prediction Integration Steps Create spreadsheet Enable Google API credentials Connect Google account in n8n Append sensor data automatically 11. ThingSpeak Cloud Dashboard Setup Use: ThingSpeak Official Website Setup Steps Create ThingSpeak account Create New Channel Add Fields: Temperature Heart Rate SpO₂ Copy: Write API Key Insert into ESP32 code Dashboard Widgets Live Temperature Graph Heart Rate Trend Oxygen Saturation Chart AI Risk Gauge 12. Voice Notification Automation Workflow Critical Alert ↓ n8n Trigger ↓ Generate TTS Audio ↓ Telegram Voice Message Example Voice Alert Warning. Patient oxygen level is critically low. Immediate medical attention required. 13. Advanced AI Features Future AI Enhancements Machine Learning Use: Random Forest SVM Neural Networks Deep Learning Predict: Heart disease Diabetes Sleep apnea Edge AI Deploy TinyML directly on ESP32. 14. Cloud Database Options Platform Purpose Firebase Realtime database MongoDB Atlas Medical records AWS IoT Enterprise IoT Azure IoT Hub Scalable monitoring 15. Security Features HTTPS encryption Token authentication Secure cloud APIs Patient data privacy Access control 16. Future Enhancements Hardware GPS tracking GSM alerts Camera monitoring Smartwatch integration Software AI chatbot doctor Mobile app Predictive analytics Remote doctor dashboard Multi-patient monitoring 17. Deployment Guide Local Deployment Arduino IDE upload Local WiFi Free cloud platforms Production Deployment Dedicated server MQTT broker SSL security Dockerized n8n Database backup 18. Applications Remote patient monitoring Elderly care ICU monitoring Smart hospitals Home healthcare Rural telemedicine 19. Final Output Features ✅ Real-time health monitoring ✅ AI disease prediction ✅ Telegram text alerts ✅ Telegram voice notifications ✅ Google Sheets logging ✅ ThingSpeak visualization ✅ ESP32 cloud IoT ✅ n8n intelligent automation ✅ Agentic AI workflows ✅ Future-ready architecture 20. Recommended Software Tools Software Purpose Arduino IDE ESP32 programming n8n Automation Postman API testing ThingSpeak Cloud dashboard Google Sheets Data logging 21. Conclusion This project combines: AI IoT Cloud computing Automation Healthcare analytics into a powerful next-generation smart healthcare ecosystem using ESP32 and Agentic AI automation. It is ideal for: Engineering final-year projects Research prototypes Healthcare startups Smart hospital systems Remote patient monitoring platforms

AI Smart Garbage Monitoring and Collection System with Route Optimization

AI Smart Garbage Monitoring and Collection System with Route Optimization

AI Smart Garbage Monitoring and Collection System with Route Optimization An intelligent waste-management platform using an ESP32-based IoT node, AI-assisted analytics, cloud dashboards, automated workflows, and Telegram voice alerts. The system monitors garbage bin levels, predicts overflow, optimizes collection schedules, and sends real-time notifications. 1. Project Overview Objective Build a smart garbage monitoring system that: Detects garbage level in bins Monitors temperature and harmful gas Sends data to cloud dashboards Stores logs in Google Sheets Uses AI logic to predict overflow timing Sends Telegram text + voice alerts Supports route optimization for garbage trucks Automates workflows using n8n 2. System Architecture Hardware Layer ESP32 WiFi microcontroller Ultrasonic sensor for fill level Gas sensor for methane/ammonia Temperature sensor Optional GPS module Cloud Layer ThingSpeak cloud dashboard Google Sheets data logging Telegram bot notifications n8n automation workflows AI Layer Garbage fill prediction Pickup schedule estimation Route optimization logic 3. Components List Component Quantity Purpose ESP32 Dev Board 1 Main controller HC-SR04 Ultrasonic Sensor 1 Measure garbage level MQ-135 Gas Sensor 1 Detect harmful gases DHT11/DHT22 Sensor 1 Temperature & humidity Buzzer 1 Local alert LED Indicators 2 Status indicators Breadboard 1 Prototyping Jumper Wires Several Connections 5V Power Supply 1 Power source GPS Module NEO-6M (Optional) 1 Location tracking SIM800L (Optional) 1 GSM backup Garbage Bin Model 1 Physical implementation 4. Working Principle Step-by-Step Operation ESP32 reads garbage level using ultrasonic sensor. Gas sensor checks for harmful gases. Temperature sensor monitors heat/fire risk. ESP32 sends data to ThingSpeak. n8n fetches sensor data. AI logic predicts overflow timing. Google Sheets logs all records. Telegram bot sends alerts: Bin Full Fire Risk Toxic Gas Alert Collection Recommendation Voice alerts are generated automatically. Route optimization suggests best collection order. 5. Circuit Connections HC-SR04 → ESP32 HC-SR04 ESP32 VCC 5V GND GND TRIG GPIO 5 ECHO GPIO 18 MQ135 → ESP32 MQ135 ESP32 VCC 5V GND GND AO GPIO 34 DHT11 → ESP32 DHT11 ESP32 VCC 3.3V GND GND DATA GPIO 4 Buzzer Buzzer ESP32 + GPIO 23 - GND 6. Circuit Schematic Diagram +------------------+ | ESP32 | | | HC-SR04 TRIG --> GPIO5 | HC-SR04 ECHO --> GPIO18 | MQ135 Analog --> GPIO34 | DHT11 DATA ----> GPIO4 | Buzzer --------> GPIO23 | | | +------------------+ | WiFi Cloud | ------------------------------------------------ | | | | ThingSpeak Google Sheets Telegram n8n Dashboard Logs Alerts Workflow 7. System Flowchart START | Initialize Sensors | Connect WiFi | Read Sensor Data | Calculate Garbage Level | Check Thresholds | Send Data to ThingSpeak | Trigger n8n Workflow | Store in Google Sheets | AI Prediction Logic | Send Telegram Alerts | Voice Notification | Repeat 8. ESP32 Source Code (Arduino IDE) #include #include #include "DHT.h" #define TRIG_PIN 5 #define ECHO_PIN 18 #define MQ135_PIN 34 #define DHTPIN 4 #define DHTTYPE DHT11 #define BUZZER 23 const char* ssid = "YOUR_WIFI_NAME"; const char* password = "YOUR_WIFI_PASSWORD"; String apiKey = "YOUR_THINGSPEAK_API_KEY"; DHT dht(DHTPIN, DHTTYPE); void setup() { Serial.begin(115200); pinMode(TRIG_PIN, OUTPUT); pinMode(ECHO_PIN, INPUT); pinMode(BUZZER, OUTPUT); dht.begin(); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(1000); Serial.println("Connecting..."); } Serial.println("WiFi Connected"); } float getDistance() { digitalWrite(TRIG_PIN, LOW); delayMicroseconds(2); digitalWrite(TRIG_PIN, HIGH); delayMicroseconds(10); digitalWrite(TRIG_PIN, LOW); long duration = pulseIn(ECHO_PIN, HIGH); float distance = duration * 0.034 / 2; return distance; } void loop() { float distance = getDistance(); float binHeight = 30.0; float garbageLevel = ((binHeight - distance) / binHeight) * 100; int gasValue = analogRead(MQ135_PIN); float temp = dht.readTemperature(); Serial.print("Garbage Level: "); Serial.println(garbageLevel); if (garbageLevel > 80 || gasValue > 2500 || temp > 45) { digitalWrite(BUZZER, HIGH); } else { digitalWrite(BUZZER, LOW); } if (WiFi.status() == WL_CONNECTED) { HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(garbageLevel) + "&field2=" + String(gasValue) + "&field3=" + String(temp); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } delay(15000); } 9. ThingSpeak Cloud Dashboard Setup Using ThingSpeak Steps Create account Create New Channel Add fields: Garbage Level Gas Sensor Temperature Copy Write API Key Paste in ESP32 code Create: Gauge charts Line graphs Alerts 10. Google Sheets Integration Using: n8n Google Sheets Node Google Cloud API Sheet Columns Timestamp Bin ID Garbage % Gas Temp Status 11. Telegram Bot Setup Using Telegram BotFather Steps Open Telegram Search: /BotFather Create Bot: /newbot Copy Bot Token Example: 123456:ABCDEFxxxx Get Chat ID using: https://api.telegram.org/bot/getUpdates 12. Telegram Voice Alert Automation Example Voice Message Warning! Smart garbage bin number 5 is almost full. Immediate collection required. n8n Voice Generation Flow Workflow Logic ThingSpeak Trigger | Check Threshold | Generate AI Text | Convert Text to Speech | Send Telegram Voice Message 13. n8n Automation Workflow Using n8n Automation Features Trigger from ThingSpeak API AI prediction node Telegram notifications Google Sheets logging Voice synthesis automation Sample n8n Workflow JSON { "nodes": [ { "name": "ThingSpeak Trigger", "type": "httpRequest", "position": [200, 300] }, { "name": "Check Threshold", "type": "if", "position": [400, 300] }, { "name": "Telegram Alert", "type": "telegram", "position": [600, 300] }, { "name": "Google Sheets", "type": "googleSheets", "position": [800, 300] } ] } 14. AI Power Consumption Prediction Logic Purpose Predict: Battery usage Sensor activity load Communication power drain AI Logic Formula Power estimation: P=V×I Battery life: Battery Life= Current Consumption Battery Capacity ​ AI Prediction Strategy The system learns: Peak garbage hours Frequency of alerts Sensor activity patterns Then predicts: Next overflow time Energy-saving sleep intervals Efficient upload frequency 15. Route Optimization Logic Goal Reduce: Fuel consumption Travel time Overflow incidents Inputs GPS coordinates Bin fill levels Traffic data Collection priorities AI Logic Priority Score: Priority=0.6(Fill Level)+0.3(Gas Risk)+0.1(Temperature) Route optimization can use: Dijkstra Algorithm A* Pathfinding Google Maps API 16. Example Alert Messages Telegram Text Alert 🚨 Garbage Bin Alert Bin ID: BIN-04 Level: 92% Gas Risk: HIGH Action Required: Immediate Pickup Voice Alert Attention. Garbage bin four is critically full. Collection vehicle dispatch required immediately. 17. Future Enhancements AI Improvements Machine learning overflow prediction Seasonal waste pattern analysis Smart route clustering Hardware Enhancements Solar-powered bins Camera-based waste detection AI image classification RFID-based citizen tracking Software Enhancements Mobile app Web admin dashboard Firebase real-time database AI chatbot assistant 18. Deployment Guide Small Scale Apartment complexes Schools Campuses Medium Scale Smart city pilot Municipal wards Large Scale Entire city waste management AI fleet management integration 19. Advantages Reduces overflow Saves fuel costs Real-time monitoring Improves hygiene Supports smart cities Enables predictive maintenance 20. Expected Output The system provides: Real-time garbage status Cloud analytics Automated AI alerts Voice notifications Route planning recommendations Historical data analysis 21. Software & Platforms Used Platform Purpose Arduino IDE ESP32 programming ThingSpeak Cloud dashboard n8n Automation Telegram Notifications Google Sheets Data storage Google Maps API Route optimization 22. Conclusion The AI Smart Garbage Monitoring and Collection System combines IoT, cloud computing, automation, and AI analytics to modernize waste management. Using ESP32 sensors, n8n automation, Telegram voice alerts, Google Sheets logging, and ThingSpeak visualization, the system enables efficient, scalable, and intelligent garbage collection operations suitable for smart cities and sustainable urban development.

AI Smart Energy Meter with Power Consumption Prediction

AI Smart Energy Meter with Power Consumption Prediction ESP32 + Agentic AI IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak

1. Project Overview The AI Smart Energy Meter is an advanced IoT-based electricity monitoring system that measures real-time power consumption using an ESP32 microcontroller and uploads the data to cloud platforms for monitoring, analytics, and AI-based prediction. The system integrates: ESP32 Wi-Fi microcontroller Current & voltage sensing Cloud IoT dashboard AI power usage prediction n8n workflow automation Telegram voice alert notifications Google Sheets logging ThingSpeak cloud analytics This project demonstrates a complete Agentic AI IoT architecture, where the system can: Monitor electricity usage Predict future consumption Detect overload conditions Send smart alerts automatically Store historical data Trigger automation workflows 2. Objectives The main objectives are: Measure voltage, current, power, and energy consumption Upload live data to cloud platforms Predict future energy usage using AI logic Send Telegram notifications and voice alerts Store records in Google Sheets Automate workflows using n8n Create a scalable smart energy monitoring solution 3. Features Real-Time Monitoring Voltage monitoring Current monitoring Power calculation Energy consumption tracking IoT Cloud Dashboard Live cloud updates Graphical visualization Remote monitoring AI Prediction Predict next-hour/day consumption Detect abnormal energy usage Intelligent recommendations Telegram Alerts Instant notifications Voice warning messages Overload alerts Device status alerts Google Sheets Logging Automatic data storage Historical analytics Exportable records n8n Automation Workflow automation Event-based triggers Smart decision engine 4. Hardware Components Component Quantity ESP32 Dev Board 1 ACS712 Current Sensor 1 ZMPT101B Voltage Sensor 1 OLED Display (Optional) 1 Relay Module 1 Breadboard 1 Jumper Wires Several Power Supply 5V Wi-Fi Router 1 5. Software Requirements Software Purpose Arduino IDE ESP32 Programming n8n Workflow Automation Telegram Bot API Alerts ThingSpeak Cloud Dashboard Google Sheets API Data Logging Python/AI Logic Prediction Model 6. System Architecture Voltage/Current Sensors ↓ ESP32 ↓ Wi-Fi Internet ↓ ThingSpeak ↓ n8n ↙ ↓ ↘ Telegram AI Google Sheets Alerts Prediction Storage 7. Working Principle Step 1: Sensor Reading The ESP32 reads: Voltage from ZMPT101B Current from ACS712 Step 2: Power Calculation P=V×I Where: P = Power (Watts) V = Voltage I = Current Step 3: Energy Consumption E=P×t Where: E = Energy (Wh) t = Time Step 4: Upload to Cloud ESP32 sends data to: ThingSpeak n8n Webhook Step 5: AI Analysis n8n processes: Average usage Peak load Future prediction Abnormal pattern detection Step 6: Alerts If consumption exceeds threshold: Telegram message sent Voice alert generated Google Sheets updated 8. Circuit Connections ACS712 to ESP32 ACS712 ESP32 VCC 5V GND GND OUT GPIO34 ZMPT101B to ESP32 ZMPT101B ESP32 VCC 5V GND GND OUT GPIO35 Relay Module Relay ESP32 IN GPIO26 VCC 5V GND GND 9. Schematic Diagram (Text Format) AC Load | Current Sensor | Voltage Sensor | ESP32 / | \ WiFi Relay OLED | Internet | ThingSpeak | n8n / | \ Telegram AI GoogleSheet 10. ESP32 Arduino Code #include #include const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String apiKey = "THINGSPEAK_API_KEY"; float voltage = 230.0; float current = 0.5; float power; void setup() { Serial.begin(115200); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(1000); Serial.println("Connecting..."); } Serial.println("WiFi Connected"); } void loop() { current = analogRead(34) * (5.0 / 4095.0); power = voltage * current; if(WiFi.status()== WL_CONNECTED){ HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(voltage) + "&field2=" + String(current) + "&field3=" + String(power); http.begin(url); int httpCode = http.GET(); Serial.println(httpCode); http.end(); } Serial.print("Voltage: "); Serial.println(voltage); Serial.print("Current: "); Serial.println(current); Serial.print("Power: "); Serial.println(power); delay(15000); } 11. n8n Workflow Workflow Logic Webhook Trigger ↓ Receive ESP32 Data ↓ Check Power Threshold ↓ IF High Usage? ↙ ↘ YES NO ↓ ↓ Telegram Store Data Voice Alert Google Sheets 12. Telegram Bot Setup Steps Open Telegram Search BotFather Create bot using: /newbot Copy Bot Token Use token in n8n Telegram node 13. Voice Alert Message Example: ⚠ Warning! High electricity consumption detected. Current power usage is 1200 Watts. Please check connected appliances. 14. ThingSpeak Dashboard Fields Field Data Field 1 Voltage Field 2 Current Field 3 Power Graphs: Real-time power graph Daily consumption Peak usage trends 15. Google Sheets Integration Data stored automatically: Time Voltage Current Power 10:00 230 0.5 115 10:05 231 0.7 161 16. AI Prediction Module Prediction uses: Historical averages Peak-hour analysis Trend calculation Simple prediction formula: Prediction= 2 Previous Usage+Current Usage ​ Advanced versions can use: Linear Regression TensorFlow Lite TinyML on ESP32 17. Automation Scenarios Scenario 1 High power usage: Send Telegram alert Activate relay cutoff Scenario 2 Low power factor: Notify maintenance team Scenario 3 Abnormal spike: Store emergency event 18. Advantages Low-cost smart meter Remote monitoring Cloud-based analytics AI-enabled predictions Automation-ready Energy-saving system 19. Applications Smart homes Industries Energy management Hostels Offices Solar monitoring systems 20. Future Enhancements Mobile app MQTT communication Firebase integration Voice assistant support TinyML forecasting Solar energy optimization Multi-room monitoring 21. Conclusion This project demonstrates a modern AI-powered Agentic IoT energy monitoring system using ESP32, cloud computing, AI prediction, and workflow automation. By integrating: ESP32 n8n Telegram alerts Google Sheets ThingSpeak AI analytics the system becomes a scalable smart energy solution suitable for future smart cities and Industry 4.0 applications.