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.