Wednesday, 27 May 2026
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
Tuesday, 26 May 2026
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.
AI-Based Smart Farming Robot for Seed Sowing and Weed Detection
AI-Based Smart Farming Robot for Seed Sowing and Weed Detection
With Agentic IoT, ESP32, n8n Automation, AI Agent, Telegram Voice Alerts, Google Sheets & ThingSpeak Cloud Dashboard
This project combines:
Smart Farming Robotics
AI-Based Weed Detection
ESP32 IoT Automation
Cloud Monitoring
Telegram Voice Notifications
n8n Workflow Automation
Google Sheets Logging
ThingSpeak Dashboard Analytics
Proposed Final Project Title
“AI-Powered Smart Farming Robot using ESP32, Agentic IoT, n8n Automation, Telegram Voice Alerts, Google Sheets, and ThingSpeak Cloud Dashboard”
Alternative titles:
“Agentic AI Smart Agriculture Robot with ESP32 and n8n Automation”
“IoT-Based Autonomous Seed Sowing and Weed Detection Robot”
“AI-Powered ESP32 Farming Robot with Telegram Voice Notifications”
“Smart Agriculture System using AI, ESP32, n8n, and Cloud IoT”
“Autonomous Farming Robot with AI Agent and Real-Time IoT Monitoring”
System Overview
The robot performs:
Farming Operations
Automatic seed sowing
Weed detection using AI camera
Obstacle avoidance
Smart navigation
IoT Operations
Sensor monitoring
Real-time cloud updates
Mobile notifications
Voice alerts
Data analytics
AI Agent Functions
Intelligent decision making
Automated workflow triggering
Predictive farming alerts
Crop monitoring assistance
Complete Technology Stack
Module Technology
Microcontroller Espressif Systems ESP32
AI Processing Raspberry Pi / Jetson Nano
Automation n8n
Notifications Telegram Bot
Cloud Dashboard ThingSpeak
Data Storage Google Sheets
AI Detection YOLO / TensorFlow
Programming Python + Embedded C
Communication Wi-Fi / MQTT / HTTP
Voice Alerts Telegram TTS API
High-Level Architecture
Sensors + Camera
↓
ESP32
↓
Wi-Fi Communication
↓
n8n Server
↙ ↓ ↘
Telegram Sheets ThingSpeak
Alerts Logs Dashboard
↓
AI Agent
↓
Smart Decision Making
Hardware Components
Core Components
ESP32 Dev Board
Raspberry Pi
Camera Module
Ultrasonic Sensor
Soil Moisture Sensor
Temperature Sensor
Humidity Sensor
Motor Driver L298N
DC Motors
Servo Motor
Seed Hopper
Battery Pack
Optional Advanced Modules
GPS Module
Solar Panel
Relay Module
Water Pump
Weed Sprayer
Working Modules
1. Smart Seed Sowing System
The robot:
Moves automatically
Measures spacing
Drops seeds accurately
Spacing formula:
d=v×t
Where:
d = seed spacing
v = robot speed
t = dispensing interval
2. AI Weed Detection
Camera captures field images.
AI model identifies:
Crop plants
Weeds
Workflow:
Capture image
Run AI model
Detect weed
Send alert
Spray/remove weed
3. ESP32 IoT Communication
ESP32 sends:
Soil moisture
Temperature
Humidity
Weed detection status
Robot GPS location
Using:
HTTP API
MQTT protocol
Wi-Fi
4. n8n Automation Workflow
Using n8n automation:
Workflow Example
ESP32 Sensor Data
↓
Webhook Trigger
↓
Condition Check
↓
Send Telegram Alert
↓
Update Google Sheets
↓
Store in ThingSpeak
↓
AI Agent Analysis
5. Telegram Voice Alert System
When:
Weed detected
Soil dry
Obstacle found
Battery low
Telegram bot sends:
Text alert
Voice notification
Example:
“Warning! Weed detected in Row 3.”
6. Google Sheets Logging
All farm data stored automatically:
Time Soil Moisture Weed Status Temperature
10:00 45% Detected 30°C
Benefits:
Easy analytics
Historical tracking
Farm monitoring
7. ThingSpeak Cloud Dashboard
Real-time graphs:
Soil moisture
Temperature
Humidity
Weed events
Robot activity
Cloud dashboard features:
Remote monitoring
Mobile access
Data visualization
AI Agentic Features
The AI agent can:
Predict irrigation needs
Detect abnormal sensor behavior
Suggest farming actions
Trigger automation workflows
Generate alerts intelligently
Example:
“Soil moisture critically low. Irrigation recommended.”
IoT Communication Flow
ESP32 → WiFi → n8n → Telegram
↓
Google Sheets
↓
ThingSpeak
↓
AI Agent
Suggested n8n Nodes
Inside n8n:
Webhook Node
HTTP Request Node
Telegram Node
Google Sheets Node
IF Condition Node
AI/OpenAI Node
Schedule Trigger Node
ThingSpeak API Node
Example Telegram Alerts
Text Alert
⚠ Weed detected in Zone 2
🌱 Seed sowing completed
🔋 Battery low
💧 Soil moisture low
Voice Alert
"Attention Farmer. Soil moisture is below threshold."
Software Requirements
Software Purpose
Arduino IDE ESP32 programming
Python AI processing
OpenCV Image processing
TensorFlow/YOLO Weed detection
n8n Workflow automation
ThingSpeak IoT cloud
Telegram Bot API Notifications
Advanced Features
Future Scope
AI crop disease detection
Drone monitoring
Autonomous irrigation
Weather prediction integration
Solar charging system
Edge AI inference
Advantages
Fully automated farming
Real-time monitoring
Reduced labor cost
Smart AI decision making
Cloud-connected agriculture
Remote alerts via Telegram
Data analytics support
Mini Project Flowchart
Start
↓
Read Sensors
↓
Move Robot
↓
Sow Seeds
↓
Capture Image
↓
Detect Weed
↓
Send IoT Data
↓
n8n Automation
↓
Telegram Alert
↓
Cloud Dashboard Update
↓
Repeat
Research Domains Covered
Artificial Intelligence
Robotics
IoT
Computer Vision
Embedded Systems
Cloud Computing
Precision Agriculture
Automation Engineering
Conclusion
This project creates a complete next-generation smart agriculture ecosystem by integrating:
AI weed detection
Autonomous seed sowing
ESP32 IoT communication
Agentic AI automation
n8n workflows
Telegram voice alerts
Google Sheets analytics
ThingSpeak cloud monitoring
It is highly suitable for:
Final year engineering projects
Agricultural IoT startups
Smart farming prototypes
Research publications
IEEE project demonstrations
I can also help you with:
Complete circuit diagram
ESP32 source code
n8n workflow JSON
Telegram bot setup
ThingSpeak API integration
AI weed detection model
Full IEEE paper
PPT presentation
Project abstract & synopsis
Block diagram image
Database schema
Cost estimation report
Mobile dashboard UI
GitHub project structure
Complete working architecture diagram
Thursday, 21 May 2026
Automatic Speed Control🚗Accident Avoidance System Using Ultrasonic, Alcohol and Eye Blink Sensors
Ultrasonic Sensor + Alcohol Detection + Eye Blink Monitoring + Smart Braking + Multi-Sensor Intelligence + IoT/AI Concepts.
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1. Project Abstract / Synopsis
2. Project Related Datasheets of Each Component
3. Project Sample Report / Documentation
4. Project Kit Circuit / Schematic Diagram
5. Project Kit Working Software Code
6. Project Related Software Compilers
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8. Project Kit Photos & Working Video links
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Child Safety Wearable Device with GPS Tracking & SMS/Call/Photo Alerts Using Arduino #diy #viral #ai
Child Safety Wearable Device with GPS Tracking & SMS/Call/Photo Alerts Using Arduino | AI-IoT Enabled Smart Child Safety Wearable with GPS Tracking, GSM Communication, and Camera-Based Emergency Alert System Using Arduino.
************************************************
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Title Name Along With You-Tube Video Link
🔌 CODE & CIRCUIT DIAGRAMS FOR SALE 🔧
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M1: +91 9491535690 M2: +91 7842358459
We Will Send Working Model Project KIT through DTDC / India Post / Blue Dart
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1. Project Abstract / Synopsis
2. Project Related Datasheets of Each Component
3. Project Sample Report / Documentation
4. Project Kit Circuit / Schematic Diagram
5. Project Kit Working Software Code
6. Project Related Software Compilers
7. Project Related Sample PPT’s
8. Project Kit Photos & Working Video links
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GPRS Based Automatic Agricultural Weather Station Monitoring with Smart Farming Using IoT Technology
GPRS Based Automatic Agricultural Weather Station Monitoring with Smart Farming Using IoT Technology | IoT Enabled Smart Agricultural Weather Monitoring and Precision Farming System | An IoT-Driven GPRS Enabled Smart Agricultural Weather Station for Precision Farming | Wireless Sensor Network Based Agro-Climatic Monitoring Using GPRS Communication | GPRS Based Automatic Agricultural Weather Station Using IoT | Smart Farming Project.
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M1: +91 9491535690 M2: +91 7842358459
We Will Send Working Model Project KIT through DTDC / India Post / Blue Dart
We Will Provide Project Soft Data through Google Drive
1. Project Abstract / Synopsis
2. Project Related Datasheets of Each Component
3. Project Sample Report / Documentation
4. Project Kit Circuit / Schematic Diagram
5. Project Kit Working Software Code
6. Project Related Software Compilers
7. Project Related Sample PPT’s
8. Project Kit Photos & Working Video links
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Tuesday, 19 May 2026
IoT Enabled Paralysis Patient Health System Using ESP32, Arduino, GSM, Sensors, SMS & Email Alerts
An IoT-Based Smart Paralysis Patient Health Monitoring and Emergency Alert System using ESP32, GSM, IoT Sensors, SMS & Email Notification | Smart IoT-Based Paralysis Patient Health Monitoring System Using ESP32 | Real-Time GSM SMS & Email Emergency Alert Healthcare Project.
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1. Project Abstract / Synopsis
2. Project Related Datasheets of Each Component
3. Project Sample Report / Documentation
4. Project Kit Circuit / Schematic Diagram
5. Project Kit Working Software Code
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1.Design and Implementation of an IoT-Based Paralysis Patient Health Monitoring System using ESP32.
2.Real-Time Smart Healthcare Monitoring and Emergency Alert System for Paralysis Patients.
3.IoT-Enabled Physiological Parameter Monitoring using ESP32 and GSM Technology.
4.An Intelligent Embedded Healthcare Framework for Paralysis Patient Safety.
5.ESP32-Based Remote Health Monitoring System with SMS and Email Notifications.
6.Wireless Sensor Network Architecture for Smart Paralysis Patient Monitoring.
7.Cloud-Integrated IoT Healthcare System for Critical Patient Monitoring.
8.Smart Embedded Medical Alert System using ESP32 and IoT Sensors.
9.Development of a Real-Time Health Surveillance System for Disabled Individuals.
10.IoT-Assisted Continuous Patient Monitoring with Multi-Channel Emergency Alerts.
11.Smart IoT-Based Paralysis Patient Health Monitoring System using ESP32.
12.Advanced Patient Health Monitoring and Alert System using Arduino ESP32.
13.Automated Healthcare Monitoring System with GSM, SMS & Email Alerts.
14.Low-Cost IoT Healthcare Monitoring Framework for Paralysis Patients.
15.ESP32 Smart Medical Monitoring and Emergency Communication System.
16.Sensor-Based Smart Patient Care System with Real-Time Alert Features.
17.Embedded IoT Health Monitoring Platform for Immobile Patients.
18.Wireless Healthcare Monitoring Device using ESP32 and GSM Module.
19.Remote Vital Parameter Monitoring System using IoT Technology.
20.Smart Emergency Healthcare Assistance System using Embedded IoT.
21.AIoT-Based Smart Paralysis Patient Prediction and Monitoring System.
22.Edge AI Healthcare Monitoring using ESP32 and IoT Sensors.
23.AI-Powered Smart Patient Observation and Alert Platform.
24.Predictive Healthcare Monitoring System using Artificial Intelligence and IoT.
25.Machine Learning Enabled Smart Healthcare Monitoring Framework.
26.Intelligent AI-Based Emergency Detection System for Paralysis Patients.
27.Deep Learning Assisted Real-Time Patient Monitoring using ESP32.
28.AI-Driven Smart Healthcare Analytics and Alert System.
29.Next-Generation AIoT Healthcare Monitoring Architecture.
30.Smart Predictive Medical Monitoring System with Emergency Intelligence.
31.NeuroGuardian: AIoT-Based Intelligent Paralysis Care Ecosystem.
32.VitalSphere: Smart Autonomous Health Surveillance Platform.
33.PulseNexus: Intelligent Emergency Healthcare Interface.
34.BioSentinel: IoT-Powered Critical Health Monitoring System.
35.CareMatrix IoT: Smart Paralysis Assistance Network.
36.LifeGuardian-X: Intelligent Patient Safety and Alert Engine.
37.MediVision Edge: AI-Driven Remote Patient Observation System.
38.HealthSync Edge Monitoring Platform.
39.AegisCare: Predictive Smart Healthcare Monitoring Framework.
40.OmniCare Intelligent Medical Alert Ecosystem.
41.Smart IoT-Based Paralysis Patient Monitoring System using ESP32 | GSM SMS & Email Alerts.
42.ESP32 IoT Healthcare Monitoring Project for Paralysis Patients.
43.Arduino ESP32 Smart Health Monitoring System with Emergency Alerts.
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