AI Smart Road Pothole Detection and Mapping System

AI Smart Road Pothole Detection and Mapping System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Road Pothole Detection and Mapping System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI Smart Road Pothole Detection and Mapping System using ESP32, Agentic IoT, n8n Automation, Telegram Voice Alerts, Google Sheets, and ThingSpeak Cloud Dashboard Objective The objective of this project is to: Detect road potholes automatically using sensors connected to ESP32. Collect pothole location data using GPS. Send real-time data to cloud platforms. Store pothole records in Google Sheets. Display pothole statistics on ThingSpeak Dashboard. Trigger AI-based notifications through Telegram. Generate voice alerts using AI automation. Predict power consumption and battery health using AI logic. Create a scalable smart-city road monitoring solution. 2. System Architecture Road Pothole │ ▼ MPU6050 Accelerometer │ ▼ ESP32 │ ├────────► ThingSpeak Dashboard │ ├────────► n8n Webhook │ │ │ ▼ │ AI Decision Agent │ │ │ ┌────────┼─────────┐ │ ▼ ▼ │ Google Sheets Telegram Bot │ │ │ ▼ │ Voice Notification │ ▼ GPS Location Data 3. Working Principle The accelerometer continuously monitors road vibrations. When: Acceleration > Threshold The system identifies a pothole event. ESP32 then: Reads GPS coordinates. Measures vibration intensity. Calculates pothole severity. Uploads data to: ThingSpeak n8n Webhook n8n performs: AI classification Data logging Voice generation Telegram notification Google Sheet storage 4. Components List Component Quantity ESP32 Dev Board 1 MPU6050 Accelerometer & Gyroscope 1 NEO-6M GPS Module 1 SIM800L GSM Module (Optional) 1 Buzzer 1 LED Indicator 1 Li-Ion Battery 1 TP4056 Charging Module 1 Voltage Regulator 1 Jumper Wires As required Breadboard / PCB 1 5. Hardware Connections MPU6050 → ESP32 MPU6050 ESP32 VCC 3.3V GND GND SDA GPIO21 SCL GPIO22 GPS NEO-6M → ESP32 GPS ESP32 VCC 3.3V GND GND TX GPIO16 RX GPIO17 Buzzer Buzzer ESP32 + GPIO25 - GND LED LED ESP32 Anode GPIO26 Cathode GND 6. Circuit Schematic Diagram MPU6050 +----------+ | SDA SCL | +----|--|--+ | | | | GPIO21 GPIO22 ESP32 +-------------+ | | GPS TX -->| GPIO16 | GPS RX <--| GPIO17 | BUZZER -->| GPIO25 | LED ----->| GPIO26 | | | +-------------+ | | WiFi Internet | ▼ ThingSpeak + n8n 7. Flowchart START │ ▼ Initialize ESP32 │ ▼ Connect WiFi │ ▼ Read MPU6050 Data │ ▼ Acceleration > Threshold? │ ┌┴────────────┐ │ │ NO YES │ │ ▼ ▼ Continue Read GPS Monitoring │ ▼ Calculate Severity │ ▼ Send Data to Cloud │ ▼ Trigger n8n │ ▼ AI Agent Analysis │ ▼ Telegram Voice Alert │ ▼ Store Google Sheet │ ▼ END 8. Pothole Severity Classification Severity Acceleration Value Low 1.0g – 1.5g Medium 1.5g – 2.5g High > 2.5g 9. ESP32 Source Code #include #include #include #include MPU6050 mpu; const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String webhookURL = "https://your-n8n-server/webhook/pothole"; float threshold = 1.5; void setup() { Serial.begin(115200); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(500); } Wire.begin(); mpu.initialize(); } void loop() { int16_t ax, ay, az; mpu.getAcceleration(&ax,&ay,&az); float vibration = sqrt(ax*ax+ay*ay+az*az)/16384.0; if(vibration > threshold) { sendData(vibration); } delay(1000); } void sendData(float value) { HTTPClient http; http.begin(webhookURL); http.addHeader("Content-Type", "application/json"); String payload = "{\"severity\":" + String(value) + "}"; http.POST(payload); http.end(); } 10. n8n Workflow Architecture Webhook │ ▼ AI Agent │ ├────► Google Sheets │ ├────► ThingSpeak Update │ ├────► OpenAI Analysis │ └────► Telegram Alert 11. n8n Workflow Steps Node 1: Webhook Method: POST Receive: { "severity": 2.8, "latitude": 17.3850, "longitude": 78.4867 } Node 2: AI Agent Prompt: Analyze pothole severity. If severity > 2.5 Category = Critical If severity > 1.5 Category = Medium Else Category = Low Node 3: Google Sheets Columns: Date Time Latitude Longitude Severity Category Status Node 4: Telegram Notification Message: ⚠️ Pothole Detected Location: 17.3850,78.4867 Severity: Critical Immediate inspection required. 12. Example n8n Workflow JSON { "nodes": [ { "name": "Webhook" }, { "name": "AI Agent" }, { "name": "Google Sheets" }, { "name": "Telegram" } ] } 13. Telegram Bot Setup Step 1 Open Telegram Search: @BotFather Create bot: /newbot Step 2 Copy Bot Token. Example: 123456:ABCDEF Step 3 Add token in n8n Telegram node. 14. Voice Notification Automation AI Voice Message Message generated: Warning. Critical pothole detected. Location latitude 17.3850 longitude 78.4867. Municipal inspection required. Workflow AI Agent │ ▼ Text to Speech │ ▼ MP3 File │ ▼ Telegram Send Audio 15. Google Sheets Integration Create Sheet: Pothole_Database Columns: Timestamp Latitude Longitude Severity Category Action Connect Google Account in n8n. Use: Append Row Node. 16. ThingSpeak Dashboard Setup Create channel on: ThingSpeak Fields: Field Purpose Field1 Severity Field2 Latitude Field3 Longitude Field4 Power Consumption Field5 Pothole Count Visualization Charts: Severity Trend GPS Heatmap Daily Pothole Count Power Usage Trend 17. AI Power Consumption Prediction Logic Inputs Battery Voltage WiFi Usage Sensor Sampling Rate GPS Activity Formula P=V×I Where: P = Power V = Voltage I = Current AI Rule Engine IF Battery < 20% Reduce Sampling Rate Disable GPS Continuous Mode Send Battery Alert Predicted States Battery Status >80% Healthy 50-80% Normal 20-50% Warning <20% Critical 18. AI Agent Decision Logic Input: Severity + Location + Historical Data AI Agent evaluates: 1. Repeated pothole? 2. High traffic area? 3. Severity level? 4. Repair priority? Priority Score Priority = (Severity × 50%) + (Traffic Density × 30%) + (Repeat Count × 20%) 19. ThingSpeak Data Format Example: field1=2.8 field2=17.3850 field3=78.4867 field4=1.2 field5=45 HTTP Request: https://api.thingspeak.com/update?api_key=YOURKEY&field1=2.8 20. Advanced Future Enhancements Computer Vision Pothole Detection Add: ESP32-CAM Edge AI Models: YOLOv8 Nano MobileNet SSD GIS Mapping Integrate: OpenStreetMap Google Maps API Display: Pothole clusters Maintenance zones Smart City Dashboard Features: Heatmaps AI Analytics Municipal Alerts Maintenance Scheduling Predictive Maintenance Use: Historical pothole data Rainfall data Traffic data Predict: Road Failure Probability before pothole formation. 21. Deployment Guide Phase 1: Prototype ESP32 MPU6050 GPS WiFi Phase 2: Pilot Install on: Municipal vehicles Buses Garbage trucks Phase 3: Smart City Scale Deploy: 100+ Nodes Central Cloud Dashboard AI Maintenance Management 22. Expected Outputs ✅ Real-time pothole detection ✅ GPS-based pothole mapping ✅ AI severity classification ✅ Telegram text alerts ✅ Telegram voice alerts ✅ Google Sheets logging ✅ ThingSpeak cloud visualization ✅ AI power management ✅ Smart-city ready deployment ✅ Fully scalable Agentic IoT architecture This architecture is suitable for final-year engineering projects, smart-city research, municipal road monitoring, and AIoT deployments with ESP32, n8n, Telegram automation, Google Sheets, and cloud analytics.

AI Smart Refrigerator Monitoring and Food Expiry Detection

AI Smart Refrigerator Monitoring & Food Expiry Detection System ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Refrigerator Monitoring & Food Expiry Detection System ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview This project creates an intelligent refrigerator monitoring system that: ✅ Monitors refrigerator temperature and humidity ✅ Detects food expiry dates ✅ Predicts future power consumption using AI logic ✅ Stores data in Google Sheets ✅ Visualizes data in ThingSpeak Dashboard ✅ Sends Telegram alerts ✅ Generates Voice Notifications through Telegram ✅ Uses n8n automation as the workflow engine ✅ Uses ESP32 as the IoT edge device ✅ Can be extended into a fully Agentic AI Refrigerator Assistant 2. System Architecture +----------------+ | Refrigerator | +-------+--------+ | v +----------------+ | ESP32 | | DHT22 Sensor | | RFID/Manual | | Entry System | +-------+--------+ | WiFi MQTT/HTTP | v +----------------+ | ThingSpeak | | Cloud Dashboard| +-------+--------+ | v +----------------+ | n8n Workflow | +-------+--------+ | +----------------+ | | v v +--------------+ +--------------+ | Telegram Bot | | Google Sheet | +--------------+ +--------------+ | v +----------------+ | Voice Alerts | +----------------+ | v +----------------+ | AI Prediction | +----------------+ 3. Features Monitoring Refrigerator Temperature Humidity Door Open Duration Power Consumption Food Management Food Name Added Date Expiry Date Days Remaining Alerts High Temperature Food Expiry Power Consumption Anomaly Door Left Open Cloud Features Historical Data Dashboard Analytics AI Prediction 4. Hardware Components List Component Quantity ESP32 Dev Board 1 DHT22 Temperature Humidity Sensor 1 Reed Switch Door Sensor 1 RFID RC522 (optional) 1 RFID Tags 5 OLED Display 0.96" 1 Buzzer 1 Relay Module 1 ACS712 Current Sensor 1 Jumper Wires Several Breadboard 1 5V Adapter 1 5. Pin Connections DHT22 DHT22 ESP32 VCC 3.3V GND GND DATA GPIO4 Reed Switch Reed Switch ESP32 One Side GPIO15 Other Side GND Buzzer Buzzer ESP32 + GPIO18 - GND ACS712 Current Sensor ACS712 ESP32 OUT GPIO34 VCC 5V GND GND 6. Working Principle Step 1 ESP32 reads: Temperature Humidity Door Status Current Consumption every 30 seconds. Step 2 ESP32 sends data to: ThingSpeak n8n Webhook using HTTP requests. Step 3 n8n processes incoming data. Checks: Temperature > Threshold? Door Open Too Long? Power Consumption High? Food Expiry Near? Step 4 If abnormal: Telegram Message Telegram Voice Alert Google Sheets Entry generated automatically. 7. Flowchart START | v Initialize ESP32 | Connect WiFi | Read Sensors | Send to ThingSpeak | Send to n8n | Check Rules | +----No----+ | | | Continue | Yes | Send Telegram Alert | Generate Voice Alert | Store in Google Sheet | Repeat 8. ESP32 Source Code #include #include #include "DHT.h" #define DHTPIN 4 #define DHTTYPE DHT22 DHT dht(DHTPIN, DHTTYPE); const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String webhookURL = "https://your-n8n-domain/webhook/fridge"; String thingSpeakAPI = "YOUR_THINGSPEAK_WRITE_KEY"; void setup() { Serial.begin(115200); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(1000); } dht.begin(); } void loop() { float temp = dht.readTemperature(); float hum = dht.readHumidity(); if(WiFi.status()==WL_CONNECTED) { HTTPClient http; String url = "https://api.thingspeak.com/update?api_key=" + thingSpeakAPI + "&field1=" + String(temp) + "&field2=" + String(hum); http.begin(url); http.GET(); http.end(); HTTPClient webhook; webhook.begin(webhookURL); webhook.addHeader( "Content-Type", "application/json"); String payload = "{\"temp\":" + String(temp) + ",\"humidity\":" + String(hum) + "}"; webhook.POST(payload); webhook.end(); } delay(30000); } 9. ThingSpeak Setup Create Account Create ThingSpeak account. Create new channel. Fields: Field1 Temperature Field2 Humidity Field3 Door Status Field4 Power Copy Write API Key Channels → API Keys → Write API Key Paste into ESP32 code. 10. Google Sheets Setup Create Sheet: Date Time Temperature Humidity Power Door Food Item Expiry Date Status Example: Date Temp Food Expiry 12-05-2026 4°C Milk 15-05-2026 11. Telegram Bot Setup Step 1 Open Telegram Search: BotFather Create Bot: / newbot Get: BOT TOKEN Step 2 Get Chat ID Open: https://api.telegram.org/botTOKEN/getUpdates Save Chat ID. 12. n8n Workflow Design Node 1 Webhook POST /fridge Node 2 IF Node Condition: {{$json.temp > 8}} Node 3 Telegram Node Message: ⚠ Refrigerator Temperature High Current: {{$json.temp}} °C Node 4 Google Sheets Node Append Row Date Time Temperature Humidity Node 5 Text-To-Speech Node Input: Warning. Refrigerator temperature is high. Please check immediately. Generate MP3. Node 6 Telegram Send Voice Attach generated MP3. 13. n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook" }, { "name":"IF" }, { "name":"Telegram" }, { "name":"Google Sheets" } ] } Import and customize. 14. Food Expiry Detection Logic Google Sheet Example: Food Expiry Date Milk 15-May Eggs 20-May Yogurt 18-May n8n Daily Scheduler: Every Day 8AM Formula: daysRemaining = expiryDate - currentDate Conditions <=3 days Send Alert. Telegram: Milk expires in 2 days. 15. AI Food Expiry Prediction Advanced model considers: Temperature variation Humidity Storage duration Food category Door opening frequency Prediction: Expected Remaining Shelf Life Example: Milk Original: 7 days Predicted: 5 days because of frequent temperature spikes. 16. AI Power Consumption Prediction Input Features Temperature Compressor Runtime Door Open Count Humidity Historical Power Usage Model Linear Regression y = a + bx Where y = predicted power x = usage factors or Random Forest More accurate Training Dataset Date Power Temperature Door Count Prediction Output Tomorrow Expected Usage: 1.8 kWh 17. Voice Notification Automation Workflow: ESP32 ↓ n8n ↓ OpenAI/TTS Engine ↓ Generate Voice ↓ Telegram Voice Message Example Voice: Attention. Milk will expire in 2 days. Please consume it soon. 18. AI Agent Features The AI Agent can answer: What food expires today? How much power did fridge consume? Why is temperature rising? Suggest grocery items. Agent accesses: ThingSpeak Google Sheets Historical Records through APIs. 19. Future Enhancements Computer Vision ESP32-CAM Detect: Milk Eggs Fruits Vegetables using object detection. QR Code Inventory Each food item has QR code. Scan when inserted. Automatic inventory update. Voice Assistant Voice Commands: What expires today? How much milk is left? Mobile App Flutter App Features: Dashboard Notifications Analytics Inventory 20. Deployment Guide Local Testing Connect sensors. Upload ESP32 code. Verify serial monitor. Test ThingSpeak updates. Test n8n webhook. Cloud Deployment Deploy n8n on: Raspberry Pi Docker VPS Cloud VM Recommended: 2 CPU 4GB RAM Security Use: HTTPS Webhook Authentication Encrypted Tokens Firewall Rules 21. Expected Outputs Dashboard Temperature: 4.2°C Humidity: 68% Power: 1.5 kWh Door: Closed Telegram Alert ⚠ Warning Milk expires tomorrow. Voice Alert Attention. Milk expires tomorrow. AI Prediction Power tomorrow: 1.8 kWh Confidence: 92% 22. Project Outcome This system combines: ESP32 Edge Computing IoT Sensor Monitoring Agentic AI Decision Making n8n Workflow Automation Telegram Messaging & Voice Alerts Google Sheets Data Logging ThingSpeak Analytics Food Expiry Intelligence Predictive Maintenance The result is a complete Industry 4.0 smart refrigerator solution suitable for academic projects, final-year engineering projects, smart-home deployments, and IoT/AI portfolio demonstrations.

AI Smart Power Factor Correction with Load Prediction

AI Smart Power Factor Correction with Load Prediction ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Power Factor Correction with Load Prediction ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI-Powered Smart Power Factor Correction System with Load Prediction using ESP32, n8n Automation, Telegram Voice Alerts, Google Sheets Logging, and ThingSpeak Cloud Dashboard Project Objective Develop an intelligent energy monitoring and power factor correction system that: Measures Voltage, Current, Power, Energy, and Power Factor. Automatically switches capacitor banks for power factor correction. Uses AI-based prediction to forecast future power consumption. Sends voice alerts through Telegram. Stores historical data in Google Sheets. Visualizes real-time data on ThingSpeak. Uses n8n as the automation and AI orchestration platform. Supports future Agentic AI decision-making. 2. System Architecture ┌────────────────────┐ │ Electrical Load │ └──────────┬─────────┘ │ Voltage & Current │ ┌─────────▼────────┐ │ PZEM004T │ │ Energy Meter │ └─────────┬────────┘ │ UART ┌─────────▼────────┐ │ ESP32 │ │ Data Collection │ └─────────┬────────┘ │ WiFi ┌──────────────────┼─────────────────┐ │ │ │ ▼ ▼ ▼ ThingSpeak n8n Workflow Google Sheets │ ▼ AI Prediction Engine │ ▼ Telegram Bot │ Voice Alerts ▼ Power Factor Control Relay Bank 3. Features Monitoring Voltage Current Active Power Apparent Power Reactive Power Power Factor Energy Consumption Automation Automatic capacitor switching AI load forecasting Telegram alerts Voice notifications Cloud ThingSpeak Dashboard Google Sheets Storage Historical Analytics AI Features Consumption Prediction Anomaly Detection Peak Demand Forecasting Future Agentic Actions 4. Components Required Component Quantity ESP32 Dev Board 1 PZEM-004T v3 Energy Meter 1 ZMPT101B Voltage Sensor (optional) 1 SCT013 Current Sensor (optional) 1 5V Relay Module 4 Capacitor Banks 4 Capacitors (2µF,4µF,8µF,16µF) As required Power Supply 5V 1 WiFi Router 1 Breadboard/PCB 1 Jumper Wires Multiple Telegram Bot 1 ThingSpeak Account 1 Google Account 1 n8n Server 1 5. Power Factor Correction Theory Power Factor: PF= Apparent Power Real Power ​ Ideal PF: 0.95 to 1.00 If PF drops: PF < 0.90 Capacitor bank is switched ON. Example: PF = 0.72 Relay 1 ON PF = 0.65 Relay 1 + Relay 2 ON PF = 0.55 Relay 1 + Relay 2 + Relay 3 ON 6. Circuit Connections ESP32 ↔ PZEM004T PZEM ESP32 TX GPIO16 RX GPIO17 VCC 5V GND GND Relay Module Relay ESP32 Relay1 GPIO25 Relay2 GPIO26 Relay3 GPIO27 Relay4 GPIO14 Capacitor Banks Relay1 → 2uF Relay2 → 4uF Relay3 → 8uF Relay4 →16uF Connected parallel to load. 7. Circuit Schematic AC LOAD │ ┌──▼──┐ │PZEM │ └──┬──┘ │ ▼ ESP32 │ ┌──┼───────────────┐ │ │ │ │ │ ▼ ▼ ▼ ▼ ▼ R1 R2 R3 R4 WiFi │ │ │ │ ▼ ▼ ▼ ▼ Capacitor Bank 8. Flowchart START │ ▼ Connect WiFi │ ▼ Read PZEM Data │ ▼ Calculate PF │ ▼ PF < 0.90 ? ┌──Yes──┐ ▼ ▼ Enable No Action Capacitor │ ▼ Send Data │ ▼ ThingSpeak │ ▼ n8n Webhook │ ▼ AI Prediction │ ▼ Store in Sheets │ ▼ Send Telegram Alert │ ▼ Repeat 9. ESP32 Source Code Libraries Install: PZEM004Tv30 WiFi HTTPClient ArduinoJson Main Code #include #include #include PZEM004Tv30 pzem(Serial2,16,17); const char* ssid="YOUR_WIFI"; const char* pass="PASSWORD"; String webhookURL = "https://n8n-server/webhook/power"; #define RELAY1 25 #define RELAY2 26 #define RELAY3 27 #define RELAY4 14 void setup() { Serial.begin(115200); pinMode(RELAY1,OUTPUT); pinMode(RELAY2,OUTPUT); pinMode(RELAY3,OUTPUT); pinMode(RELAY4,OUTPUT); WiFi.begin(ssid,pass); while(WiFi.status()!=WL_CONNECTED) { delay(500); } } void loop() { float voltage=pzem.voltage(); float current=pzem.current(); float power=pzem.power(); float pf=pzem.pf(); if(pf<0.90) { digitalWrite(RELAY1,HIGH); } if(pf<0.80) { digitalWrite(RELAY2,HIGH); } if(pf<0.70) { digitalWrite(RELAY3,HIGH); } if(pf<0.60) { digitalWrite(RELAY4,HIGH); } HTTPClient http; http.begin(webhookURL); http.addHeader("Content-Type", "application/json"); String payload="{\"voltage\":" +String(voltage)+ ",\"current\":" +String(current)+ ",\"power\":" +String(power)+ ",\"pf\":" +String(pf)+"}"; http.POST(payload); http.end(); delay(30000); } 10. ThingSpeak Setup Create channel. Fields: Field1 Voltage Field2 Current Field3 Power Field4 PF Field5 Energy Field6 Predicted Load Get: Write API Key Channel ID ESP32 sends data every 30 seconds. Example URL: https://api.thingspeak.com/update Parameters: api_key=XXXX field1=230 field2=5 field3=1100 field4=0.92 11. Google Sheets Integration Create Sheet: Timestamp Voltage Current Power PF Energy Prediction Status n8n Google Sheet Node Action: Append Row Every incoming ESP32 record gets stored. 12. Telegram Bot Setup Open Telegram. Search: BotFather Create bot: / newbot Receive: BOT TOKEN Get Chat ID. Save both. 13. Voice Alert System Telegram supports voice files. n8n workflow: Incoming Data │ ▼ Function Node │ ▼ Text-to-Speech │ ▼ Telegram Send Audio Example message: Warning. Power factor has dropped to 0.68 Capacitor bank activated. Predicted load increase within 30 minutes. 14. AI Load Prediction Logic Dataset Historical records: Time Voltage Current Power Energy PF Prediction Inputs Last 24 Hours Features: Hour Day Power Current Energy Prediction Output Next 30 min load Next 1 hour load Next 24 hour load Simple AI Model Linear Regression Predicted_Load = a+b(power)+c(current)+d(hour) Advanced AI Use: XGBoost Random Forest LSTM Prophet 15. n8n Workflow Design Webhook Trigger │ ▼ Data Processing │ ▼ AI Agent Node │ ├─────────► ThingSpeak │ ├─────────► Google Sheets │ ├─────────► Telegram Text │ └─────────► Telegram Voice 16. Example n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook" }, { "name":"Function" }, { "name":"Google Sheets" }, { "name":"Telegram" } ] } In actual deployment export the workflow from n8n after configuration. 17. Agentic AI Extension AI Agent receives: PF Voltage Current Historical Trends Weather Time Agent decides: Increase Capacitor Decrease Capacitor Peak Warning Maintenance Alert Example: Predicted PF drop in 20 min Activate 8uF capacitor now. 18. Telegram Alert Examples Normal System Healthy PF = 0.97 Load = 1.1 kW Warning PF Low PF = 0.75 Capacitor Activated Critical PF = 0.52 Maximum Capacitor Bank Active Immediate inspection required 19. Future Enhancements AI LSTM Forecasting Reinforcement Learning Predictive Maintenance Load Classification Cloud MQTT Broker AWS IoT Azure IoT Hub Google Cloud IoT Hardware 3-Phase Monitoring Automatic Capacitor Bank Panel Industrial PLC Integration Mobile App Flutter Dashboard React Native Dashboard AI Chat Assistant 20. Deployment Guide Phase 1 Build hardware. Verify: Voltage readings Current readings PF readings Phase 2 Configure: WiFi ThingSpeak Telegram Phase 3 Deploy n8n. Recommended options: Docker VPS Raspberry Pi Phase 4 Connect: ESP32 → n8n n8n → Sheets n8n → Telegram n8n → ThingSpeak Phase 5 Train AI Model Collect: 1–4 weeks data Train prediction model and integrate it into n8n or a Python microservice. Final Outcome This project becomes a complete Industry 4.0 Smart Energy Management System capable of: Real-time electrical monitoring Automatic power factor correction AI-based load forecasting Agentic decision-making Cloud analytics Google Sheets logging ThingSpeak visualization Telegram text and voice alerts Scalable industrial deployment using ESP32 and n8n automation.

AI Smart Electric Vehicle Charging Station Management System

AI Smart Electric Vehicle Charging Station Management System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Electric Vehicle Charging Station Management System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI Smart Electric Vehicle Charging Station Management System using ESP32, Agentic IoT, n8n Automation, Telegram Voice Alerts, Google Sheets, and ThingSpeak Objective Develop an intelligent EV charging station monitoring and management system that: Monitors charging voltage, current, power, and energy consumption. Predicts future power demand using AI. Sends real-time alerts through Telegram. Generates voice notifications automatically. Stores charging logs in Google Sheets. Visualizes data on ThingSpeak cloud dashboards. Uses n8n as an automation and AI orchestration platform. Supports future expansion into multiple charging stations. 2. System Architecture EV Charger │ ▼ Current & Voltage Sensors │ ▼ ESP32 Controller │ ├────────► ThingSpeak Dashboard │ ├────────► n8n Webhook │ │ │ ▼ │ AI Agent Logic │ │ │ ┌─────────┴─────────┐ │ ▼ ▼ │ Google Sheets Telegram Bot │ │ │ ▼ │ Voice Notification │ ▼ Cloud Monitoring 3. Features Real-Time Monitoring Voltage Monitoring Current Monitoring Power Calculation Energy Consumption Tracking AI Agent Features Charging load prediction Peak demand forecasting Anomaly detection Usage pattern analysis Automation Data logging Alert generation Voice message generation Cloud dashboard updates 4. Components List Component Quantity ESP32 Dev Board 1 ACS712 Current Sensor 1 ZMPT101B Voltage Sensor 1 Relay Module 1 OLED Display 0.96" 1 EV Charging Socket 1 5V Power Supply 1 Jumper Wires Several Breadboard/PCB 1 WiFi Router 1 5. Circuit Connections ACS712 Current Sensor ACS712 ESP32 VCC 5V GND GND OUT GPIO34 ZMPT101B Voltage Sensor ZMPT101B ESP32 VCC 5V GND GND OUT GPIO35 Relay Module Relay ESP32 IN GPIO26 VCC 5V GND GND OLED Display (I2C) OLED ESP32 SDA GPIO21 SCL GPIO22 VCC 3.3V GND GND 6. Circuit Schematic Diagram +----------------+ | ESP32 | | | Voltage Sensor--| GPIO35 | Current Sensor--| GPIO34 | Relay ----------| GPIO26 | OLED SDA -------| GPIO21 | OLED SCL -------| GPIO22 | +----------------+ | WiFi | +-------------+-------------+ | | ThingSpeak n8n Server | +----------------+----------------+ | | | Telegram Bot Google Sheets AI Agent 7. Flowchart Start │ ▼ Initialize ESP32 │ Connect WiFi │ Read Sensors │ Calculate Power │ Upload ThingSpeak │ Send Data to n8n │ AI Analysis │ Store in Sheets │ Alert Required? │ ┌──Yes───┐ ▼ ▼ Telegram Continue Voice Alert │ ▼ Loop 8. Working Principle Step 1 ESP32 reads: Voltage from ZMPT101B Current from ACS712 Step 2 Calculate power: P=V×I Example: Voltage = 230V Current = 10A Power = 230 × 10 = 2300 W Step 3 Calculate Energy E=P×t Example: 2300W × 2h = 4.6 kWh Step 4 ESP32 sends data to: ThingSpeak n8n Webhook Step 5 n8n processes data Save logs Trigger AI analysis Send notifications 9. ESP32 Source Code #include #include const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String webhookURL = "https://your-n8n-server/webhook/evstation"; int voltagePin = 35; int currentPin = 34; void setup() { Serial.begin(115200); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(500); Serial.print("."); } } void loop() { float voltage = analogRead(voltagePin)*(3.3/4095.0)*100; float current = analogRead(currentPin)*(3.3/4095.0)*30; float power = voltage*current; if(WiFi.status()==WL_CONNECTED) { HTTPClient http; http.begin(webhookURL); http.addHeader("Content-Type", "application/json"); String payload="{"; payload+="\"voltage\":"+String(voltage)+","; payload+="\"current\":"+String(current)+","; payload+="\"power\":"+String(power); payload+="}"; http.POST(payload); http.end(); } delay(15000); } 10. ThingSpeak Setup Create Channel Sign up at ThingSpeak. Create New Channel. Add Fields: Field1 = Voltage Field2 = Current Field3 = Power Field4 = Energy Save Channel. Copy Write API Key. ESP32 Upload URL https://api.thingspeak.com/update Example: field1=230 field2=10 field3=2300 field4=4.5 11. Google Sheets Integration Create columns: Timestamp Voltage Current Power Energy Prediction Example: 2026-05-30 10:15 230 10 2300 4.6 2500 12. Telegram Bot Setup Create Bot Open Telegram. Search for: Telegram Open: BotFather Send: /newbot Enter Bot Name. Copy Token. Example: 123456:ABCDEFxxxx Get Chat ID Send message to your bot. Open: https://api.telegram.org/botTOKEN/getUpdates Copy Chat ID. 13. n8n Workflow Architecture Webhook │ ▼ Code Node │ ▼ AI Agent │ ┌─┴────────────┐ ▼ ▼ Google Sheet Telegram Alert 14. n8n Workflow Detailed Steps Node 1 Webhook Node POST /webhook/evstation Receives: { "voltage":230, "current":10, "power":2300 } Node 2 Function Node const power = $json.power; let status = "Normal"; if(power > 2500) { status = "High Load"; } return [{ json:{ power:power, status:status } }] Node 3 Google Sheets Node Append Row. Map: Timestamp Voltage Current Power Status Node 4 Telegram Node Message: ⚡ EV Charging Alert Power: {{$json.power}} Status: {{$json.status}} 15. n8n Workflow JSON { "name":"EV Station Workflow", "nodes":[ { "name":"Webhook" }, { "name":"AI Analysis" }, { "name":"Google Sheets" }, { "name":"Telegram" } ] } This is a simplified structure. In production, export the workflow directly from n8n after configuration. 16. AI Power Consumption Prediction Logic Dataset Stored in Google Sheets: Date Time Power Energy Temperature Prediction Features Current Power Historical Power Time of Day Charging Duration Simple Prediction Formula Moving Average: Prediction= 5 P 1 ​ +P 2 ​ +P 3 ​ +P 4 ​ +P 5 ​ ​ Example: 2200 2300 2400 2500 2600 Prediction: 2400W Advanced AI Use: Linear Regression Random Forest XGBoost LSTM Neural Networks via Python or AI APIs connected through n8n. 17. Voice Notification Automation Trigger Condition Power > 2500W n8n Flow IF Node │ ▼ Generate Speech │ ▼ Telegram Send Voice Voice Message Warning. Electric vehicle charging load has exceeded the safe limit. Current load is 2600 watts. 18. AI Agent Responsibilities The AI agent can: Monitor Power Voltage Current Energy Decide Overload detection Peak demand prediction Charger fault detection Act Notify user Log event Disable relay if dangerous 19. Automatic Relay Protection If Power > 3000W Relay OFF Send Alert Store Incident Pseudo-code: if(power > 3000) { digitalWrite(RELAY,LOW); } 20. Cloud Dashboard Design Dashboard Widgets Gauge 1 Voltage 0–250V Gauge 2 Current 0–32A Gauge 3 Power 0–7000W Chart Daily Consumption Chart Weekly Consumption 21. Database Structure ChargingLogs ------------ id timestamp voltage current power energy status prediction 22. Future Enhancements AI Features Dynamic charging optimization Peak tariff avoidance Smart load balancing Vehicle identification Battery health estimation IoT Features RFID authentication QR-code charging access Solar integration OCPP protocol support Multi-station management Mobile App Flutter dashboard Real-time monitoring Push notifications Usage analytics 23. Deployment Guide Local Deployment ESP32 connected to Wi-Fi n8n running on PC or Raspberry Pi Google Sheets cloud logging ThingSpeak dashboard active Cloud Deployment Deploy n8n on: n8n Cloud AWS Google Cloud Microsoft Azure 24. Expected Output Voltage : 228V Current : 11A Power : 2508W Energy : 5.2kWh AI Prediction: 2700W in next 30 minutes Status: High Load Action: Telegram Voice Alert Sent Google Sheet Updated ThingSpeak Updated 25. Project Outcome This project demonstrates a complete Industry 4.0 and Smart EV Infrastructure solution combining: ESP32 Edge Computing IoT Cloud Monitoring AI Agent Decision-Making n8n Workflow Automation Telegram Voice Notifications Google Sheets Analytics ThingSpeak Visualization Predictive Energy Management The architecture is scalable from a single charging point to a city-wide EV charging network with centralized AI monitoring and automated control.

AI Smart Door Lock System Using Face and Fingerprint Recognition

AI Smart Door Lock System Using Face & Fingerprint Recognition AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Door Lock System Using Face & Fingerprint Recognition AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI Smart Door Lock System Using Face and Fingerprint Recognition with ESP32, n8n Automation, Telegram Voice Alerts, Google Sheets, and ThingSpeak Cloud Dashboard Objective Design and develop an intelligent smart door security system capable of: Face Recognition Authentication Fingerprint Authentication Smart Lock Control AI-based Access Decision Making Real-time Cloud Monitoring Telegram Voice Notifications Automated Logging AI Power Consumption Prediction Agentic IoT Automation using n8n 2. System Architecture +-------------------+ | Face Recognition | | ESP32-CAM | +---------+---------+ | v +-------------------+ | Authentication | | Decision Engine | +---------+---------+ | v +-------------------+ | Fingerprint | | Sensor R307 | +---------+---------+ | v +-------------------+ | ESP32 Controller | +---------+---------+ | +----------------------+ | | v v +----------------+ +----------------+ | Door Lock | | Cloud Services | | Relay + Solenoid| +----------------+ +-------+--------+ | | | v v Door Opens ThingSpeak Google Sheets Telegram Bot n8n AI Agent 3. Features Security Features Multi-Factor Authentication User must pass: Face Recognition Fingerprint Verification before door unlocks. Intruder Detection If: Unknown Face Invalid Fingerprint then: Capture image Send Telegram Alert Store evidence in cloud Voice Alert Telegram receives: "Warning! Unauthorized access attempt detected at Main Door." 4. Hardware Components List Component Quantity ESP32 Dev Board 1 ESP32-CAM 1 R307 Fingerprint Sensor 1 Relay Module 5V 1 Solenoid Door Lock 1 Buzzer 1 OLED Display (Optional) 1 PIR Motion Sensor 1 12V Adapter 1 LM2596 Buck Converter 1 Jumper Wires As Required Breadboard/PCB 1 Magnetic Door Sensor 1 5. Circuit Connections Fingerprint Sensor R307 ESP32 VCC ---------> 5V GND ---------> GND TX ----------> GPIO16 RX ----------> GPIO17 Relay Module Relay IN -----> GPIO26 Relay VCC ----> 5V Relay GND ----> GND Buzzer Buzzer + ----> GPIO25 Buzzer - ----> GND PIR Sensor OUT ----------> GPIO27 VCC ----------> 5V GND ----------> GND ESP32-CAM ESP32-CAM | WiFi | Cloud Face recognition runs on ESP32-CAM. 6. Complete Flowchart START | v Initialize WiFi | v Connect Cloud Services | v Wait for Person | v Capture Face | v Face Match? | YES ---------------- NO | | v v Scan Fingerprint Alert Intruder | | v | Fingerprint Match? | | | YES ----- NO | | | | v v | Unlock Alert | Door User | | | | v v | Log Data Log Data | | | | +--------+----------+ | v ThingSpeak | v Google Sheet | v Telegram | v END 7. ESP32 Source Code Structure Required Libraries WiFi.h HTTPClient.h ArduinoJson.h Adafruit_Fingerprint.h ESP32Servo.h WiFi Setup const char* ssid = "YOUR_WIFI"; const char* password = "PASSWORD"; Telegram Configuration String botToken="BOT_TOKEN"; String chatID="CHAT_ID"; ThingSpeak Configuration String apiKey="THINGSPEAK_WRITE_KEY"; Door Lock Pin #define LOCK_PIN 26 Unlock Function void unlockDoor() { digitalWrite(LOCK_PIN,HIGH); delay(5000); digitalWrite(LOCK_PIN,LOW); } Fingerprint Verification bool verifyFingerprint() { int id = finger.getImage(); if(id == FINGERPRINT_OK) { return true; } return false; } Send Telegram Alert void sendTelegram(String msg) { HTTPClient http; String url = "https://api.telegram.org/bot"+ botToken+ "/sendMessage?chat_id="+ chatID+ "&text="+msg; http.begin(url); http.GET(); http.end(); } Update ThingSpeak void updateThingSpeak( String status) { HTTPClient http; String url= "https://api.thingspeak.com/update?api_key="+ apiKey+ "&field1="+status; http.begin(url); http.GET(); http.end(); } 8. Face Recognition System ESP32-CAM Operation Step 1 Capture image. Step 2 Run face detection. Step 3 Compare with enrolled faces. Step 4 Send result to ESP32. KNOWN FACE | v ESP32 Unlock Request UNKNOWN FACE | v Telegram Alert 9. AI Agent Using n8n Purpose AI Agent analyzes: Entry patterns Intruder attempts Power consumption Door usage frequency n8n Workflow ESP32 Webhook | v Google Sheets | v OpenAI Node | v Decision Analysis | v Telegram Alert | v ThingSpeak Update 10. n8n Workflow Nodes Node 1: Webhook Receives data: { "user":"John", "status":"Authorized", "time":"2026-05-30 08:20" } Node 2: Google Sheets Store: Date Time User Status Node 3: AI Agent Prompt: Analyze today's door access records. Detect suspicious behavior. Predict energy consumption. Generate summary. Node 4: Telegram Send report. 11. Example n8n Workflow JSON { "nodes": [ { "name": "Webhook", "type": "n8n-nodes-base.webhook" }, { "name": "Google Sheets", "type": "n8n-nodes-base.googleSheets" }, { "name": "OpenAI", "type": "@n8n/n8n-nodes-langchain.openAi" }, { "name": "Telegram", "type": "n8n-nodes-base.telegram" } ] } This is a simplified template; in deployment you would configure credentials, mappings, and error handling. 12. Telegram Bot Setup Step 1 Open Telegram. Search: Telegram Step 2 Search: BotFather Step 3 Create Bot /newbot Step 4 Copy Bot Token. Example: 123456:ABCxyz Step 5 Get Chat ID https://api.telegram.org/botTOKEN/getUpdates 13. Voice Notification Automation Event Trigger Unauthorized Access ↓ n8n ↓ Text-to-Speech ↓ Telegram Voice Message Voice Message Script Alert! Unknown person detected at the main entrance. Please check immediately. n8n Flow Webhook | v AI Agent | v Google TTS | v Telegram Voice 14. Google Sheets Integration Columns: Timestamp User Face Status Fingerprint Result 08:30 John Match Match Granted ESP32 sends: { "user":"John", "face":"match", "finger":"match", "access":"granted" } to n8n webhook. n8n appends row automatically. 15. ThingSpeak Dashboard Setup Create Channel In ThingSpeak Create fields: Field1 = Door Status Field2 = Authorized Access Field3 = Unauthorized Access Field4 = Power Consumption Field5 = AI Risk Score Dashboard Widgets Gauge Door Status Line Chart Power Usage Counter Access Count Trend Graph Unauthorized Attempts 16. AI Power Consumption Prediction Data Inputs Lock Activations Camera Usage Time WiFi Uptime Fingerprint Scans Formula E=P×t Where: E = Energy (Wh) P = Power (W) t = Time (hours) Sample Dataset Day Power 1 5.2W 2 5.4W 3 5.8W 4 6.0W AI predicts future usage and detects abnormal spikes. 17. AI Risk Scoring Logic Known Face = 40 points Known Fingerprint = 40 points Normal Time Access = 20 points Total: 100 = Safe Risk Levels Score Status 80-100 Safe 50-79 Warning 0-49 Threat 18. Database Design Access Log Table Field ID Timestamp Face_ID Finger_ID Status Power RiskScore 19. Deployment Procedure Phase 1 Hardware Assembly Phase 2 Upload ESP32 Firmware Phase 3 Enroll Faces Phase 4 Enroll Fingerprints Phase 5 Configure Wi-Fi Phase 6 Create Telegram Bot Phase 7 Deploy n8n Workflow Phase 8 Connect Google Sheets Phase 9 Connect ThingSpeak Phase 10 Field Testing 20. Testing Scenarios Test 1 Known Face + Known Fingerprint Expected: Door Opens Telegram Log Cloud Update Test 2 Known Face + Wrong Fingerprint Expected: Access Denied Alert Sent Test 3 Unknown Face Expected: Buzzer ON Image Capture Telegram Voice Alert 21. Future Enhancements Face recognition using Edge AI models (TensorFlow Lite Micro) Liveness detection against photo spoofing Visitor QR-code access Mobile app control Cloud-based user management Voice assistant integration Battery backup and solar charging Multi-door enterprise deployment Predictive maintenance analytics AI anomaly detection using historical access logs 22. Expected Outcome The final system provides: ✅ Face Recognition Security ✅ Fingerprint Authentication ✅ Smart Door Unlocking ✅ ESP32-Based IoT Control ✅ n8n Agentic Automation ✅ Telegram Text & Voice Alerts ✅ Google Sheets Logging ✅ ThingSpeak Dashboard Monitoring ✅ AI Risk Assessment ✅ Power Consumption Prediction ✅ Cloud-Based Smart Access Management This architecture is suitable for academic projects, smart homes, offices, laboratories, hostels, and industrial access-control systems, while remaining low-cost and scalable.

AI Smart Baby Monitoring System with Cry and Motion Detection

AI Smart Baby Monitoring System with Cry and Motion Detection ESP32 + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Baby Monitoring System with Cry and Motion Detection ESP32 + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview This project is an AI-powered baby monitoring system that continuously monitors a baby's: Crying sounds Body movements Environmental conditions Sleep patterns The system uses: ESP32 as IoT Controller Sound Sensor for Cry Detection PIR Sensor for Motion Detection ThingSpeak Cloud Dashboard n8n Automation Workflow Telegram Bot Notifications Google Sheets Data Logging AI Agent Logic for Smart Decision Making Telegram Voice Alerts using Text-to-Speech The system can: ✅ Detect crying baby ✅ Detect excessive movement ✅ Send instant Telegram alerts ✅ Send voice notifications ✅ Log all events into Google Sheets ✅ Visualize live data on ThingSpeak ✅ Predict baby discomfort trends using AI logic 2. System Architecture +----------------------+ | Baby Room | +----------------------+ | | +----------------------+ | Sensors | |----------------------| | Sound Sensor | | PIR Motion Sensor | | Temperature Sensor | +----------------------+ | | +----------------------+ | ESP32 | +----------------------+ | WiFi | V +----------------------+ | ThingSpeak Cloud | +----------------------+ | | V +----------------------+ | n8n Server | +----------------------+ | | | | | | Telegram Google AI Agent Alert Sheets 3. Hardware Components List Component Quantity ESP32 Dev Board 1 Sound Sensor KY-037 1 PIR Motion Sensor HC-SR501 1 DHT22 Temperature Sensor 1 Buzzer 1 LED Indicator 1 Breadboard 1 Jumper Wires Several 5V Adapter 1 WiFi Network 1 4. Working Principle Cry Detection Sound sensor continuously monitors sound level. If Sound > Threshold | V Cry Detected ESP32 sends: { "event":"cry", "sound":92 } to ThingSpeak. Motion Detection PIR sensor detects movement. Motion = HIGH ESP32 sends: { "event":"motion", "movement":"active" } AI Agent Analysis n8n receives data. Rules: Cry + Motion = Baby Awake Cry + No Motion = Possible Discomfort No Cry + Motion = Restless Sleep No Cry + No Motion = Sleeping 5. Circuit Schematic Sound Sensor Sound Sensor -> ESP32 VCC -> 3.3V GND -> GND AO -> GPIO34 PIR Sensor PIR -> ESP32 VCC -> 5V GND -> GND OUT -> GPIO27 DHT22 DHT22 -> ESP32 VCC -> 3.3V DATA -> GPIO4 GND -> GND Buzzer Buzzer -> GPIO18 LED LED -> GPIO2 6. Pin Configuration #define SOUND_PIN 34 #define PIR_PIN 27 #define DHT_PIN 4 #define BUZZER_PIN 18 #define LED_PIN 2 7. Flowchart START | Initialize ESP32 | Connect WiFi | Read Sound Sensor | Read Motion Sensor | Read Temperature | Sound > Threshold ? | | YES NO | | Cry Event Continue | Send Data | Motion Detected ? | | YES NO | | Motion Continue | Upload ThingSpeak | Trigger n8n | Send Telegram Alert | Log Google Sheet | Repeat 8. ESP32 Source Code Install Libraries: WiFi.h HTTPClient.h DHT.h ThingSpeak.h Main Code #include #include #include #include char* ssid="YOUR_WIFI"; char* password="YOUR_PASSWORD"; unsigned long channelID = YOUR_CHANNEL_ID; const char* writeAPIKey="YOUR_API_KEY"; WiFiClient client; #define SOUND_PIN 34 #define PIR_PIN 27 #define DHT_PIN 4 DHT dht(DHT_PIN,DHT22); void setup() { Serial.begin(115200); pinMode(PIR_PIN,INPUT); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(500); } ThingSpeak.begin(client); dht.begin(); } void loop() { int soundLevel=analogRead(SOUND_PIN); int motion=digitalRead(PIR_PIN); float temp=dht.readTemperature(); ThingSpeak.setField(1,soundLevel); ThingSpeak.setField(2,motion); ThingSpeak.setField(3,temp); ThingSpeak.writeFields(channelID,writeAPIKey); delay(15000); } 9. ThingSpeak Setup Create account: ThingSpeak Official Platform Create Channel Fields: Field1 = Sound Level Field2 = Motion Status Field3 = Temperature Field4 = AI Risk Score Dashboard Widgets Add: Gauge Line Chart Motion Indicator Temperature Chart Risk Score Chart 10. Telegram Bot Setup Open Telegram Search: BotFather Telegram Bot Creation Guide Commands: /start /newbot Example: BabyMonitorBot Receive: BOT_TOKEN Get Chat ID: https://api.telegram.org/botTOKEN/getUpdates Save: CHAT_ID 11. n8n Setup Install n8n n8n Official Website Docker: docker run -it --rm \ -p 5678:5678 \ -v ~/.n8n:/home/node/.n8n \ docker.n8n.io/n8nio/n8n Open: http://localhost:5678 12. n8n Workflow Logic Webhook Trigger | V Read Sensor Data | IF Cry? | YES | Telegram Alert | Google Sheet | ThingSpeak Update | AI Analysis | Voice Alert 13. n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook", "type":"n8n-nodes-base.webhook" }, { "name":"IF Cry", "type":"n8n-nodes-base.if" }, { "name":"Telegram", "type":"n8n-nodes-base.telegram" }, { "name":"Google Sheets", "type":"n8n-nodes-base.googleSheets" } ] } Import this structure and configure credentials in n8n. 14. Google Sheets Integration Create Sheet: Baby Monitoring Log Columns: Timestamp Sound Motion Temperature Status Connect using: Google OAuth Credentials in n8n. Documentation: Google Sheets API Documentation 15. AI Agent Decision Engine Example rule engine: if sound > 80 and motion == 1: status = "Awake" elif sound > 80 and motion == 0: status = "Discomfort" elif sound < 30 and motion == 1: status = "Restless" else: status = "Sleeping" 16. AI Power Consumption Prediction Logic Track: Voltage Current Operating Hours WiFi Usage Formula: Power = Voltage × Current P=VI Prediction: daily_power = average_hourly_power * 24 monthly_power = daily_power * 30 AI Agent can estimate: Battery remaining Daily energy usage Maintenance interval 17. Voice Notification Automation Workflow: Cry Detected | V n8n | Google TTS | Generate MP3 | Telegram Send Voice Example message: Attention. Baby crying detected. Immediate check recommended. Useful services: Google Text-to-Speech API Telegram Send Voice Node Documentation: Google Cloud Text-to-Speech 18. AI Agent Enhancements You can integrate: OpenAI Platform Ollama Local AI Models LangChain Framework Advanced analysis: Analyze last 24 hours Detect: - Frequent crying - Sleep interruptions - Temperature abnormalities Generate daily report Example AI report: Baby Sleep Score: 82% Cry Events: 7 Motion Events: 24 Recommendation: Check room temperature. 19. Deployment Guide Stage 1 Build hardware. Stage 2 Upload ESP32 code. Stage 3 Verify WiFi connection. Stage 4 Create ThingSpeak Channel. Stage 5 Create Telegram Bot. Stage 6 Install n8n. Stage 7 Connect: ESP32 ↓ ThingSpeak ↓ n8n ↓ Telegram ↓ Google Sheets Stage 8 Test Events Clap near sensor → Cry event Move in front of PIR → Motion event Verify: Telegram notification Google Sheets entry ThingSpeak graph update 20. Future Enhancements AI Features Real cry classification using TinyML Baby face recognition Sleep quality prediction Fever prediction Abnormal behavior detection Hardware Upgrades ESP32-CAM MLX90614 IR thermometer Microphone array Battery backup OLED display Cloud Enhancements Mobile app Firebase integration AWS IoT integration Voice assistant support Multi-room monitoring Expected Project Outcome The final system becomes a complete Agentic AI Baby Monitoring Platform capable of: Real-time cry detection Motion monitoring Cloud analytics AI decision making Telegram text alerts Telegram voice alerts Google Sheets logging ThingSpeak dashboard visualization Power usage prediction Daily baby activity reporting This architecture is suitable for final-year engineering projects, IoT research prototypes, smart nursery deployments, and AI-enabled healthcare monitoring demonstrations.

AI Smart Baby Monitoring System with Cry and Motion Detection

AI Smart Baby Monitoring System with Cry and Motion Detection AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Baby Monitoring System with Cry and Motion Detection AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview This project is an intelligent baby monitoring system that continuously monitors: Baby crying sounds Baby movement/motion Room temperature and humidity Activity patterns The system uses: ESP32 for edge sensing AI logic for event detection and prediction n8n for workflow automation Telegram Bot for instant voice alerts Google Sheets for data logging ThingSpeak for IoT dashboard visualization AI Agent for decision making and prediction 2. Objectives The system should: ✅ Detect baby crying ✅ Detect baby movement ✅ Send Telegram notifications ✅ Generate voice alerts ✅ Store historical data ✅ Visualize data on dashboard ✅ Predict high-activity periods ✅ Provide remote monitoring 3. System Architecture +------------------+ | Baby Room | +------------------+ | -------------------------------- | | Sound Sensor PIR Sensor (Cry Detection) (Motion Detection) | | ---------- ESP32 -------------- | | WiFi Internet | ----------------------------------- | | | | ThingSpeak n8n Server Google Sheet AI Agent | | | | ----------------------------------- | Telegram Bot | Voice Notification | Parent 4. Components List Main Controller Component Quantity ESP32 Dev Board 1 Sensors Component Quantity KY-038 Sound Sensor 1 PIR Motion Sensor HC-SR501 1 DHT22 Temperature Sensor 1 Output Devices Component Quantity LED Indicator 1 Buzzer 1 Communication Component Quantity WiFi Router 1 Software Arduino IDE n8n Telegram Bot Google Sheets ThingSpeak OpenAI API (optional AI agent) Google TTS API 5. Working Principle Cry Detection Sound sensor measures sound intensity. Sound > Threshold If: Sound Level > 2000 then: Baby Cry Event generated. Motion Detection PIR sensor detects movement. Motion = HIGH means baby movement detected. AI Decision Layer If: Cry + Motion occur together: Severity = HIGH If: Cry only Severity = MEDIUM If: Motion only Severity = LOW 6. Circuit Schematic Diagram ESP32 -------------------- GPIO34 <-- Sound Sensor AO GPIO27 <-- PIR OUT GPIO4 <-- DHT22 DATA GPIO2 --> LED GPIO15 --> Buzzer 3.3V --> DHT22 VCC 5V --> PIR VCC GND --> All GND 7. Pin Configuration ESP32 Pin Device GPIO34 Sound Sensor GPIO27 PIR Sensor GPIO4 DHT22 GPIO2 LED GPIO15 Buzzer 8. Flowchart START | Initialize ESP32 | Connect WiFi | Read Sensors | +----------------+ | Cry Detected ? | +----------------+ | YES | Send Alert | +------------------+ | Motion Detected? | +------------------+ | YES | High Priority Alert | Upload Data | Store in Sheet | Update Dashboard | AI Prediction | Repeat 9. ESP32 Source Code #include #include #include #define SOUND_PIN 34 #define PIR_PIN 27 #define DHTPIN 4 #define DHTTYPE DHT22 #define LED_PIN 2 #define BUZZER_PIN 15 const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String webhookURL = "https://your-n8n-server/webhook/baby-monitor"; DHT dht(DHTPIN, DHTTYPE); void setup() { Serial.begin(115200); pinMode(PIR_PIN, INPUT); pinMode(LED_PIN, OUTPUT); pinMode(BUZZER_PIN, OUTPUT); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(500); } dht.begin(); } void loop() { int soundLevel = analogRead(SOUND_PIN); int motion = digitalRead(PIR_PIN); float temp = dht.readTemperature(); float hum = dht.readHumidity(); String eventType="NORMAL"; if(soundLevel > 2000) { eventType="CRY"; } if(motion==HIGH) { eventType="MOTION"; } if(soundLevel > 2000 && motion==HIGH) { eventType="CRY_MOTION"; } if(eventType!="NORMAL") { digitalWrite(LED_PIN,HIGH); tone(BUZZER_PIN,1000); sendData(eventType,soundLevel,motion,temp,hum); delay(5000); } digitalWrite(LED_PIN,LOW); delay(1000); } void sendData(String eventType, int sound, int motion, float temp, float hum) { if(WiFi.status()==WL_CONNECTED) { HTTPClient http; http.begin(webhookURL); http.addHeader( "Content-Type", "application/json"); String payload = "{"; payload += "\"event\":\""+eventType+"\","; payload += "\"sound\":"+String(sound)+","; payload += "\"motion\":"+String(motion)+","; payload += "\"temp\":"+String(temp)+","; payload += "\"humidity\":"+String(hum); payload += "}"; http.POST(payload); http.end(); } } 10. Telegram Bot Setup Step 1 Open Telegram Search: @BotFather Create Bot: /newbot Example: BabyMonitorBot Get: BOT TOKEN Save token. Step 2 Get Chat ID Send message to bot. Visit: https://api.telegram.org/botTOKEN/getUpdates Copy: chat_id 11. n8n Workflow Design Workflow: Webhook | Function | IF Node | Telegram | Google Sheets | ThingSpeak | AI Agent 12. n8n Step-by-Step Node 1: Webhook Method: POST Path: baby-monitor Receives ESP32 data. Node 2: Function Node return [{ json:{ event:$json.event, severity: $json.event=="CRY_MOTION"? "HIGH": "MEDIUM" } }] Node 3: IF Node Condition: severity = HIGH Node 4: Telegram Node Message: 🚨 Baby Crying and Moving! Immediate attention required. 13. Voice Notification Automation Method 1 Google Text-To-Speech API Generate: Attention. Baby is crying and moving. Please check immediately. MP3 generated. n8n Telegram Send Audio Node: Telegram → Send Audio Audio File: generated_voice.mp3 Parent receives voice alert. 14. Google Sheets Integration Create Sheet: Baby Monitoring Logs Columns: | Timestamp | | Event | | Sound | | Motion | | Temp | | Humidity | | Severity | Google Sheets Node Operation: Append Row Mapping: Date Event Sound Motion Temp Humidity Severity 15. ThingSpeak Setup Create account: ThingSpeak Official Website Create Channel Fields: Field1 = Sound Field2 = Motion Field3 = Temperature Field4 = Humidity Get: WRITE API KEY Upload Example https://api.thingspeak.com/update? api_key=XXXX &field1=1500 &field2=1 &field3=30 &field4=60 16. AI Power Consumption Prediction Logic Purpose: Estimate future power usage. Features: Sensor Activity Count WiFi Usage Alert Frequency Operating Hours Dataset Example Activity Alerts Power 10 2 0.5Wh 50 10 1.2Wh 100 20 2.5Wh AI Formula Linear Regression: y=a+bx a b Where: y = Predicted Power x = Activity Count Prediction Example: Current Activity = 80 Predicted: 2.0 Wh 17. AI Agentic Layer The AI agent receives: { "event":"CRY_MOTION", "sound":2450, "motion":1, "temp":31, "humidity":58 } AI analyzes: Severity Frequency Trend Repeated crying pattern Response: Baby has cried 5 times in the last hour. Activity level is increasing. Recommend immediate check. 18. Advanced AI Features Pattern Analysis Detect: Frequent Crying Night Disturbances Abnormal Activity Predictive Alerts Example: Baby usually cries around 2 AM. AI sends early warning. Anomaly Detection If: No motion for long time or Continuous crying Generate emergency notification. 19. Complete n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook" }, { "name":"Function" }, { "name":"Telegram" }, { "name":"GoogleSheets" }, { "name":"ThingSpeak" } ] } In a real deployment, export the workflow from n8n after configuring credentials and node IDs. 20. Testing Procedure Test 1 Clap near microphone. Expected: Cry Alert Test 2 Move in front of PIR. Expected: Motion Alert Test 3 Cry + Motion Expected: High Priority Alert Voice Notification 21. Future Enhancements Computer Vision Add: ESP32-CAM Face Detection Sleep Monitoring Edge AI Use: TinyML TensorFlow Lite Micro For actual cry classification instead of simple sound threshold detection. Health Monitoring Add: Heart rate sensor Oxygen sensor Breathing sensor Mobile App Develop: Flutter App Android App iOS App 22. Deployment Guide Hardware Deployment Mount sensors near crib (not within baby's reach). Place microphone 1–2 meters away. Install PIR sensor with full crib coverage. Use a stable 5V/2A power supply. Connect ESP32 to a reliable Wi-Fi network. Software Deployment Upload ESP32 firmware. Configure Telegram Bot token. Configure n8n webhook URL. Connect Google Sheets credentials. Configure ThingSpeak API key. Test all alert paths. Enable automatic backups of logs. 23. Expected Outputs Telegram Alert 🚨 HIGH PRIORITY Baby Crying Detected Motion Detected Temperature: 30°C Humidity: 60% Please check immediately. Voice Alert Attention. Baby is crying and moving. Please check the baby immediately. Dashboard Live sound level graph Motion activity graph Temperature trend Humidity trend Alert history AI prediction chart 24. Project Outcomes This solution combines: ESP32 IoT Edge Computing Cry Detection Motion Detection Agentic AI Decision Making n8n Automation Telegram Voice Notifications Google Sheets Logging ThingSpeak Analytics Predictive AI Monitoring The result is a low-cost, scalable, cloud-connected smart baby monitoring platform suitable for homes, daycare centers, hospitals, and research environments.