Wednesday, 24 June 2026

AI Smart Automatic Exam Paper Evaluation System

AI Smart Automatic Exam Paper Evaluation System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Dashboard
```php <?php echo $title; ?>

AI Smart Automatic Exam Paper Evaluation System

ESP32 + AI Agent + n8n + Telegram Voice Alerts + Google Sheets + ThingSpeak

1. Project Overview

This project automatically evaluates student answer sheets using AI, stores marks in Google Sheets, updates ThingSpeak dashboards, and sends Telegram voice notifications.

2. System Architecture

Student Answer Sheet
        |
        V
     ESP32-CAM
        |
        V
      WiFi
        |
        V
     n8n Server
        |
  -----------------
  |       |       |
 OCR     AI    Database
  |       |
  ---------
      |
      V
Google Sheets
      |
      V
ThingSpeak
      |
      V
Telegram Voice Alert

3. Components List

Component Quantity
ESP32-CAM1
ESP32 Development Board1
OV2640 Camera1
WiFi Router1
USB TTL Converter1
Breadboard1
Jumper WiresAs Required
Power Supply5V 2A

4. Flowchart

START
  |
Initialize ESP32
  |
Connect WiFi
  |
Capture Image
  |
Upload to n8n
  |
OCR Processing
  |
AI Evaluation
  |
Generate Marks
  |
Store Results
  |
Send Telegram Alert
  |
END

5. ESP32 Source Code

#include <WiFi.h>
#include <HTTPClient.h>

const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASSWORD";

void setup()
{
  Serial.begin(115200);

  WiFi.begin(ssid,password);

  while(WiFi.status()!=WL_CONNECTED)
  {
     delay(500);
  }
}

void loop()
{
  HTTPClient http;

  http.begin(
  "https://yourserver.com/webhook/exam");

  http.addHeader(
  "Content-Type",
  "application/json");

  String data =
  "{\"student\":\"101\"}";

  http.POST(data);

  http.end();

  delay(30000);
}

6. n8n Workflow

Webhook
   |
OCR
   |
AI Evaluation
   |
Score Calculation
   |
---------------------
|         |         |
Google   ThingSpeak Telegram
Sheets              Voice

7. Google Sheets Structure

Student ID Subject Marks Similarity Feedback
101 Maths 85 90% Good Performance

8. ThingSpeak Dashboard Fields

Field Description
Field1Marks
Field2Similarity
Field3Evaluation Time
Field4AI Confidence
Field5Power Consumption

9. Telegram Voice Alert

AI Result
    |
Text Message
    |
Google TTS
    |
MP3 Audio
    |
Telegram Send Audio

Example Voice: "Student 101 scored 85 marks. Performance is excellent."

10. AI Power Consumption Prediction

Formula:

Power = Voltage x Current

Example:

5V x 0.24A

Power = 1.2 Watts

11. Database Table

CREATE TABLE students(
 id INT,
 name VARCHAR(50),
 subject VARCHAR(30),
 marks FLOAT,
 similarity FLOAT,
 feedback TEXT,
 timestamp DATETIME
);

12. Future Enhancements

  • Handwritten OCR
  • Face Recognition
  • AI Proctoring
  • WhatsApp Alerts
  • Mobile Application
  • TinyML Deployment
  • Offline Evaluation

13. Deployment Architecture

ESP32-CAM
    |
Internet
    |
Cloud Server
    |
n8n Automation
    |
OpenAI / Gemini
    |
Google Sheets
    |
ThingSpeak
    |
Telegram Alerts
```

SunCharge: Smart Solar EV Charging Station with Secure System

SunCharge: Smart Solar EV Charging Station with Secure System <?php echo $title; ?>

Project Overview

SunCharge is an intelligent EV charging station that uses solar energy as its primary power source, combined with battery storage and secure digital access control. The system provides sustainable, cost-effective, and secure charging for electric vehicles while reducing dependence on the power grid.

Objectives

  • Utilize renewable solar energy for EV charging.
  • Reduce grid dependency and carbon emissions.
  • Provide secure user authentication.
  • Monitor charging activity in real time.
  • Enable remote management through IoT technology.
  • Ensure safe and reliable charging operations.

System Architecture

Solar Panels
      │
      ▼
MPPT Charge Controller
      │
      ▼
Battery Storage System
      │
      ▼
Smart Power Management Unit
      │
      ├── EV Charging Port
      ├── IoT Monitoring Module
      ├── RFID/User Authentication
      └── Safety & Security System
    

Key Components

1. Solar Power Generation

  • High-efficiency photovoltaic panels convert sunlight into electricity.
  • MPPT controller maximizes solar energy extraction.

2. Battery Energy Storage

  • Stores excess solar energy.
  • Supports charging during low sunlight and nighttime.
  • LiFePO₄ batteries offer safety and long cycle life.

3. Smart EV Charger

  • Supports controlled charging rates.
  • Monitors voltage, current, power, and energy consumption.
  • Prioritizes solar energy before battery or grid power.

4. Secure Access Control

  • RFID card authentication.
  • Mobile application login.
  • Charging session tracking and billing.
  • Prevents unauthorized charger access.

5. IoT Monitoring Platform

  • Real-time monitoring through web or mobile dashboard.
  • Displays solar generation, battery status, and charging progress.
  • Sends maintenance and fault alerts.

Security Features

  • RFID-based authentication
  • Encrypted communication (SSL/TLS)
  • Secure cloud database
  • User access management
  • Charging session logs
  • Intrusion detection and fault monitoring
  • Secure firmware updates

Working Principle

  1. Solar panels generate electricity.
  2. MPPT controller optimizes energy harvesting.
  3. Energy is stored in the battery bank.
  4. User authenticates via RFID card or mobile app.
  5. Smart controller enables charging.
  6. Charging data is sent to the IoT platform.
  7. System continuously monitors safety parameters.
  8. Alerts are generated for faults or unauthorized access.

Innovative Features

  • Solar-first charging strategy
  • Mobile application integration
  • RFID-based user management
  • Real-time energy analytics
  • Battery health monitoring
  • Remote diagnostics
  • Load balancing for multiple EVs
  • Support for OCPP smart charging standards

Applications

  • University campuses
  • Smart cities
  • Residential communities
  • Shopping malls
  • Corporate offices
  • Highway charging stations
  • Fleet charging depots

Expected Benefits

Parameter Benefit
Energy Cost Reduced through solar generation
Sustainability Lower carbon emissions
Reliability Battery backup support
Security Controlled user access
Monitoring Real-time analytics
Scalability Supports future expansion

Conclusion

SunCharge integrates solar power, battery storage, IoT monitoring, RFID authentication, and cybersecurity into a single intelligent platform. It offers a sustainable, secure, and scalable solution for the future of EV charging infrastructure.

Friday, 19 June 2026

A LoRa-Based Multi-Hazard Monitoring and Early Warning System for Smart Disaster Management

A LoRa-Based Multi-Hazard Monitoring and Early Warning System for Smart Disaster Management A LoRa-Based Multi-Hazard Monitoring and Early Warning System (EWS) is a low-power, long-range framework designed to detect, analyze, and alert communities about environmental disasters like floods, landslides, earthquakes, and wildfires simultaneously. By utilizing LoRa (Long Range) wireless communication technology, this architecture circumvents traditional cellular networks, ensuring operational resilience and emergency communication continuity even when municipal infrastructure fails completely during a major disaster.Comprehensive System ArchitectureA smart disaster management framework relies on a multi-tier structure to safely route data from the remote ground level to emergency coordinators:The Sensing Layer (Sensor Nodes): Autonomous, low-power nodes deployed in high-risk zones. Each node uses specific environmental instruments tailored to individual hazards:Flooding: Ultrasonic sensors (e.g., HC-SR04) and water flow meters track sudden volumetric and elevation changes in catchments.Landslides: Soil moisture sensors, barometers, and accelerometers (e.g., MPU6050) track slope shifting and pore water pressure.Wildfires: Coupled thermal and gas sensor arrays track rapid anomalies against baseline parameters (e.g., the "30-30-30" climate risk rule).The Transmission Layer (LoRa & LoRaWAN): The physical transceivers (like the SX1278 module) broadcast raw sensor data packets across standard sub-gigahertz Industrial, Scientific, and Medical (ISM) radio bands. This allows wide-area network telemetry coverage spanning up to 10–15 kilometers away from central receiver gateways.The Edge Layer (Gateway Base Stations): Central hubs that aggregate concurrent multi-node broadcasts. They perform localized edge computing, filter high-frequency background noise, preserve data locally during complete backhaul drops, and feed information upwards.The Cloud & Application Layer: Web backends (such as The Things Network or Firebase) execute predictive machine learning algorithms (e.g., decision trees) to evaluate multi-hazard threshold conditions in real time. They push visualizations to disaster management agency dashboards and sync regional conditions with public Android or iOS safety applications.Operational Workflow for Multi-Hazard Early Warnings[ Sensor Nodes ] --(LoRa RF Band)--> [ LoRa Gateway ] --(Cellular/Sat)--> [ Cloud Backend ] | | | (Local Threshold Exceeded) (Internet Failure) (Normal Operation) | | | v v v [ Local Sirens / Buzzers ] [ Backup GSM Modems ] [ App Notifications ] Why LoRa Outperforms Traditional SystemsPerformance FactorTraditional Cellular (GSM / 4G / 5G)LoRa / LoRaWAN TechnologyDisaster ResilienceFragile; cell towers frequently lose power or suffer from extreme traffic congestion during mass emergencies.High Independence; decentralized mesh configurations run locally without relying on commercial telecom providers.Power ConsumptionHigh power requirements; needs continuous grid access or massive, high-maintenance stationary battery reserves.Ultra-Low Power; nodes optimize sleep modes, running on simple solar panels and small rechargeable batteries for years.Operational CostsHigh recurring subscription data plans across hundreds of distributed telemetry points.Zero Data Fees; uses unlicensed public radio spectrums, offering free long-range site-wide infrastructure.Data ThroughputMassive bandwidth; built for heavy video streams and rich multimedia.Low Throughput; restricted to small data packets containing essential physical numerical values.Redundant Emergency Alert DisseminationTo fulfill the essential pillars of disaster early warning, a smart system utilizes a dual-path notification strategy to ensure maximum community reach:Online Paths: Pushes instantaneous real-time cloud notifications, SMS alerts via global Twilio APIs, and agency emails to trigger immediate evacuation protocols if internet infrastructure is intact.Offline Paths: If cloud internet pathways drop entirely, the local gateway or master receiver node utilizes directly attached, independent hardware—such as secondary backup GSM modems to broadcast regional cell text alerts or localized loud-frequency sirens—to physically warm nearby high-risk communities.Are you building a prototype or researching this system for a project? If you tell me which specific hazards you want to focus on (e.g., flash floods or structural shifting) or your preferred microcontrollers (e.g., ESP32 or Arduino), I can provide the exact hardware circuit maps and open-source LoRa code libraries you will need.

Thursday, 18 June 2026

ESP32 Based Vehicle Accident & Alcohol Detection System Using GSM-GPS | IoT Smart Vehicle Safety Project

An ESP32-Based Vehicle Accident & Alcohol Detection System Using GSM-GPS is an IoT-enabled vehicle safety project that detects accidents and prevents drunk driving while automatically sending emergency alerts with location information. If you want the project report content saved as a PHP file, you can place the text inside an HTML structure and save it as index.php. <?php echo $title; ?>

ESP32 Based Vehicle Accident & Alcohol Detection System Using GSM-GPS

IoT Smart Vehicle Safety Project

Project Overview

This project is designed to improve road safety by integrating accident detection, alcohol sensing, GPS tracking, and GSM communication using ESP32. The system can prevent drunk driving and automatically send emergency alerts with vehicle location during accidents.

Main Objectives

  • Detect alcohol consumption before driving.
  • Prevent vehicle ignition if alcohol is detected.
  • Detect accidents automatically.
  • Send GPS location through GSM SMS alerts.
  • Improve emergency response time.

Block Diagram

        +----------------+
        |   MQ-3 Sensor  |
        +-------+--------+
                |
                v
+--------------------------------+
|            ESP32               |
|                                |
|  - Alcohol Monitoring          |
|  - Accident Detection          |
|  - GPS Data Processing         |
|  - GSM Communication           |
+-----+-------------+------------+
      |             |
      v             v
+-----------+   +----------+
| GPS Module|   | GSM SIM  |
| NEO-6M    |   |800L/SIM900|
+-----------+   +----------+

      |
      v
 Location Coordinates

      |
      v
+------------+
| Relay Motor|
| Control    |
+------------+

      |
      v
+------------+
| Buzzer/LCD |
+------------+

Required Components

Component Quantity
ESP32 Development Board1
MQ-3 Alcohol Sensor1
GPS Module (NEO-6M)1
GSM Module (SIM800L/SIM900A)1
Vibration Sensor / MPU60501
Relay Module1
DC Motor1
Buzzer1
LCD 16x2 I2C1
Power Supply1

Working Principle

1. Alcohol Detection

  • MQ-3 sensor continuously monitors alcohol concentration.
  • If alcohol exceeds the threshold value:
    • ESP32 activates buzzer.
    • Relay turns OFF vehicle ignition.
    • LCD displays "Alcohol Detected".

2. Accident Detection

  • Vibration sensor or MPU6050 detects collision or rollover.
  • ESP32 confirms the accident condition.

3. GPS Tracking

  • GPS module obtains Latitude and Longitude.
  • Location is used in emergency SMS alerts.

4. GSM Alert System

Sample Alert Message:

ALERT!

Vehicle Accident Detected.

Location:
https://maps.google.com/?q=17.3850,78.4867

Need Immediate Assistance.

ESP32 Pin Connections

Module ESP32 Pin
MQ3 AOGPIO34
Vibration SensorGPIO27
GPS TXGPIO16
GPS RXGPIO17
GSM TXGPIO26
GSM RXGPIO25
RelayGPIO18
BuzzerGPIO19
LCD SDAGPIO21
LCD SCLGPIO22

Algorithm

START

Initialize ESP32
Initialize GPS
Initialize GSM
Initialize MQ3

LOOP

Read Alcohol Sensor

IF Alcohol > Threshold
    Stop Vehicle
    Activate Alarm
ENDIF

Read Accident Sensor

IF Accident Detected
    Read GPS Location
    Send SMS via GSM
    Activate Buzzer
ENDIF

Repeat

Applications

  • Smart Vehicles
  • Commercial Transport
  • School Buses
  • Taxi Services
  • Fleet Management
  • Emergency Response Systems

Future Enhancements

  • Cloud Monitoring using IoT
  • Mobile Application Integration
  • Real-Time Vehicle Tracking
  • AI-Based Accident Analysis
  • Automatic Ambulance Notification

Expected Outcome

The system detects alcohol before vehicle operation, prevents ignition when alcohol is present, and automatically sends GPS-based accident alerts through GSM during emergencies, improving vehicle safety and reducing response time.

Save the above code as index.php and run it on a PHP-enabled server such as XAMPP, WAMP, or Apache with PHP installed.

AI-Based Intelligent Fire Fighting Robot with Vision Navigation

Below is a complete, structured end-to-end documentation for your: 🤖 AI-Based Intelligent Fire Fighting Robot (ESP32 + Vision + IoT + n8n + Telegram Alerts) This system is an Agentic IoT fire-fighting robot built using: ESP32 n8n Telegram Google Sheets ThingSpeak
Here is your complete project documentation converted into a single PHP webpage file format. You can save it as: 👉 fire_fighting_robot.php AI Fire Fighting Robot - IoT Project

🤖 AI-Based Intelligent Fire Fighting Robot

ESP32 + AI Vision + IoT + n8n Automation + Telegram Alerts

🔥 Project Overview

This project is an AI-powered autonomous fire-fighting robot using ESP32, sensors, cloud IoT platforms, and automation tools like n8n. It detects fire, takes action, and sends real-time alerts via Telegram and cloud dashboards.

🔩 Components List

  • ESP32 Dev Board
  • Flame Sensor
  • MQ-2 Gas Sensor
  • DHT11 Temperature Sensor
  • Ultrasonic Sensor
  • L298N Motor Driver
  • Water Pump + Relay
  • Chassis + Wheels

🧠 System Architecture

Sensors → ESP32 → WiFi → n8n Server
                           ↓
     Telegram Alerts | Google Sheets | ThingSpeak Dashboard
    

🔁 System Flow

Start
 ↓
Read Sensors
 ↓
Fire Detected?
 ↓ YES → Activate Pump + Buzzer
 ↓
Send Data to n8n
 ↓
AI Analysis + Alerts + Cloud Logging
    

💻 ESP32 Source Code

#include <WiFi.h>
#include <HTTPClient.h>
#include "DHT.h"

#define FLAME 34
#define MQ2 35
#define PUMP 32
#define BUZZER 13

void setup() {
    Serial.begin(115200);
    pinMode(FLAME, INPUT);
    pinMode(MQ2, INPUT);
    pinMode(PUMP, OUTPUT);
    pinMode(BUZZER, OUTPUT);
}

void loop() {
    int flame = analogRead(FLAME);
    int gas = analogRead(MQ2);

    if(flame < 2000 || gas > 400) {
        digitalWrite(PUMP, HIGH);
        digitalWrite(BUZZER, HIGH);
    } else {
        digitalWrite(PUMP, LOW);
        digitalWrite(BUZZER, LOW);
    }
    delay(1000);
}
    

⚙️ n8n Workflow Logic

Webhook Trigger → AI Risk Analysis → Telegram Alert → Google Sheets → ThingSpeak Update
    

📱 Telegram Alerts

Real-time fire alerts are sent using Telegram Bot API integrated with n8n automation.

📊 Google Sheets Logging

Sensor data (temperature, gas, flame, risk level) is stored in cloud spreadsheets for analysis.

📡 ThingSpeak Dashboard

Live IoT visualization of sensor values using cloud dashboards.

🧠 AI Logic

IF temperature > 50 OR gas high:
    Risk = HIGH
ELSE:
    Risk = LOW
    

🚀 Future Enhancements

  • AI Vision using ESP32-CAM
  • YOLO Fire Detection Model
  • Autonomous Navigation (SLAM)
  • Cloud AI Decision Making
🚀 How to Run Save file as: fire_fighting_robot.php Run using: XAMPP / WAMP / Laragon Place file in htdocs folder Open browser: http://localhost/fire_fighting_robot.php If you want next upgrades, I can also build: 🔥 Admin Dashboard (PHP + MySQL) 📊 Real-time IoT live graph dashboard 🤖 AI chat assistant inside webpage 📱 Mobile responsive UI (Bootstrap version) 🌐 Full final year project website Just tell me 👍

AI-Based Real-Time Air Pollution Monitoring and Prediction

Below is a complete, structured, end-to-end documentation for your project: 🌍 AI-Based Real-Time Air Pollution Monitoring & Prediction System (ESP32 + IoT + n8n Automation + Telegram Voice Alerts + ThingSpeak + Google Sheets + AI Prediction)
Below is your complete project documentation converted into a single PHP file format (ready for a website/webpage display). You can save this as: air_pollution_iot_project.php and run it on XAMPP / WAMP / Laravel / any PHP server. ✅ PHP FILE (FULL WEBPAGE FORMAT) AI IoT Air Pollution Monitoring System

🌍 AI-Based Real-Time Air Pollution Monitoring System

ESP32 + IoT + n8n Automation + Telegram Alerts + ThingSpeak + AI Prediction


1. 🚀 Project Overview

This system monitors air quality using ESP32 sensors and sends real-time data to cloud platforms. It uses AI to predict pollution levels and sends Telegram alerts with voice notifications.

2. 🧰 Components List

  • ESP32 Development Board
  • MQ-135 Air Quality Sensor
  • DHT11 Temperature & Humidity Sensor
  • Jumper Wires
  • WiFi Connection
  • ThingSpeak + Google Sheets + Telegram Bot

3. 🏗 System Architecture

MQ Sensors → ESP32 → WiFi → ThingSpeak
                     ↓
                 n8n Automation
                     ↓
 Google Sheets + Telegram Alerts + AI Prediction

4. ⚡ Circuit Diagram (Connections)

  • MQ135 → GPIO 34
  • DHT11 → GPIO 4
  • VCC → 5V
  • GND → GND

5. 💻 ESP32 Code

#include <WiFi.h>
#include "DHT.h"

#define DHTPIN 4
#define DHTTYPE DHT11
DHT dht(DHTPIN, DHTTYPE);

const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASSWORD";

const char* server = "api.thingspeak.com";
String apiKey = "YOUR_API_KEY";

int mq135Pin = 34;

void setup() {
  Serial.begin(115200);
  dht.begin();
  WiFi.begin(ssid, password);

  while (WiFi.status() != WL_CONNECTED) {
    delay(500);
  }
}

void loop() {
  float h = dht.readHumidity();
  float t = dht.readTemperature();
  int airValue = analogRead(mq135Pin);

  float AQI = map(airValue, 0, 4095, 0, 500);

  WiFiClient client;
  if (client.connect(server, 80)) {
    String url = "/update?api_key=" + apiKey +
                 "&field1=" + String(AQI) +
                 "&field2=" + String(t) +
                 "&field3=" + String(h);

    client.print("GET " + url + " HTTP/1.1\r\nHost: api.thingspeak.com\r\nConnection: close\r\n\r\n");
  }

  delay(15000);
}

6. 🔁 n8n Workflow Logic

Webhook → Google Sheets → IF AQI Check → Telegram Alert

7. 🤖 Telegram Bot Setup

  • Open Telegram → BotFather
  • Create bot using /newbot
  • Copy API Token

8. 📊 Google Sheets Setup

  • Create sheet with columns: Time, AQI, Temperature, Humidity
  • Enable Google Sheets API
  • Connect in n8n

9. ☁️ ThingSpeak Setup

  • Create channel
  • Fields: AQI, Temp, Humidity
  • Use API Key in ESP32 code

10. 🧠 AI Prediction Logic

from sklearn.linear_model import LinearRegression
import pandas as pd

data = pd.read_csv("aqi.csv")

X = data[["temperature", "humidity", "aqi"]]
y = data["future_aqi"]

model = LinearRegression()
model.fit(X, y)

11. 🔊 Voice Alerts

  • Use Google TTS API or ElevenLabs API
  • Convert alert text → voice
  • Send via Telegram Bot

12. 🚨 AQI Alert Levels

  • 0–50 → Good
  • 51–100 → Moderate
  • 101–150 → Unhealthy for sensitive people
  • 151–200 → Unhealthy
  • 200+ → Hazardous

13. 🚀 Future Enhancements

  • Mobile App Dashboard
  • LSTM AI Forecasting
  • GPS Pollution Mapping
  • Edge AI on ESP32-S3

✔ Project Completed: AI Agentic IoT Air Pollution System

🚀 What you get from this PHP file ✔ Full website documentation ✔ Clean UI for project presentation ✔ Ready for college submission ✔ Works on XAMPP / hosting server ✔ Includes code + architecture + AI + IoT flow If you want next upgrade 👇 I can also convert this into: 🌐 Professional portfolio website (React + PHP backend) 📱 Mobile-friendly dashboard (Bootstrap UI) 🤖 Live real-time dashboard with charts (Chart.js) 🔥 Admin panel + login system 📡 Live ESP32 data visualization web app Just tell 👍

AI-Based Sign Language to Speech Conversion System

Below is a complete step-by-step documentation for your project: 🤖 AI-Based Sign Language to Speech Conversion System 🌐 ESP32 + IoT + AI Agent + n8n Automation + Telegram + Google Sheets + ThingSpeak
Below is your complete project documentation converted into a PHP file format. You can directly save it as: sign_language_iot_project.php ESP32 Sensor Reading -> WiFi/MQTT Transmission -> n8n Webhook Trigger -> AI Gesture Classification -> Output: - Telegram Voice Alert - Google Sheets Logging - ThingSpeak Dashboard - Speech Output ----------------------------------------------------------- COMPONENTS LIST ----------------------------------------------------------- Hardware: - ESP32 Development Board - Flex Sensors (5 fingers) OR MPU6050 - Jumper Wires - Breadboard - 5V Power Supply - Optional OLED Display Software: - Arduino IDE - n8n Automation Tool - Telegram Bot API - Google Sheets API - ThingSpeak IoT Platform - AI Python Backend (optional) ----------------------------------------------------------- ESP32 SOURCE CODE (REFERENCE) ----------------------------------------------------------- */ ?>
#include <WiFi.h>
#include <HTTPClient.h>

const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASSWORD";

String serverUrl = "http://YOUR_N8N_WEBHOOK_URL";

int flexPins[5] = {34, 35, 32, 33, 36};

void setup() {
  Serial.begin(115200);

  WiFi.begin(ssid, password);
  while (WiFi.status() != WL_CONNECTED) {
    delay(1000);
    Serial.println("Connecting...");
  }
  Serial.println("Connected to WiFi");
}

void loop() {
  int sensorData[5];

  for (int i = 0; i < 5; i++) {
    sensorData[i] = analogRead(flexPins[i]);
  }

  String jsonData = "{";
  jsonData += "\"f1\":" + String(sensorData[0]) + ",";
  jsonData += "\"f2\":" + String(sensorData[1]) + ",";
  jsonData += "\"f3\":" + String(sensorData[2]) + ",";
  jsonData += "\"f4\":" + String(sensorData[3]) + ",";
  jsonData += "\"f5\":" + String(sensorData[4]);
  jsonData += "}";

  if (WiFi.status() == WL_CONNECTED) {
    HTTPClient http;
    http.begin(serverUrl);
    http.addHeader("Content-Type", "application/json");

    int response = http.POST(jsonData);
    Serial.println(response);

    http.end();
  }

  delay(3000);
}
AI Function Node -> Telegram Node -> Google Sheets Example AI Logic: if (f1 > 2000 && f2 < 1500) => HELLO if (f3 > 2000) => HELP ----------------------------------------------------------- TELEGRAM BOT SETUP ----------------------------------------------------------- Steps: 1. Open Telegram 2. Search BotFather 3. Create bot using /newbot 4. Get API Token 5. Connect token in n8n Telegram Node ----------------------------------------------------------- GOOGLE SHEETS STRUCTURE ----------------------------------------------------------- Columns: Timestamp | Gesture | F1 | F2 | F3 | F4 | F5 ----------------------------------------------------------- THINGSPEAK SETUP ----------------------------------------------------------- Channel Fields: Field1 = Gesture Field2 = Sensor Average API Example: https://api.thingspeak.com/update?api_key=YOUR_KEY&field1=HELLO ----------------------------------------------------------- AI POWER PREDICTION LOGIC ----------------------------------------------------------- Formula: Power = Base + (Sensor Load × Transmission Factor) Example: function predictPower($gesturesPerMin) { $base = 0.5; $tx = 0.2 * $gesturesPerMin; return $base + $tx; } ----------------------------------------------------------- VOICE NOTIFICATION FLOW ----------------------------------------------------------- Gesture -> Text -> TTS Engine -> Audio -> Telegram Voice Message ----------------------------------------------------------- FUTURE ENHANCEMENTS ----------------------------------------------------------- - CNN-based sign recognition - Camera-based gesture detection - MQTT instead of HTTP - Edge AI on ESP32-S3 - Mobile app dashboard ----------------------------------------------------------- END OF PROJECT FILE ----------------------------------------------------------- */ ?> If you want, I can also convert this into: 🌐 Fully working PHP website dashboard (live IoT panel) 📊 Real-time web dashboard with charts (ThingSpeak + JS) 📱 Mobile-friendly UI version ⚡ Complete GitHub project structure Just tell me 👍

AI-Based Smart ATM Security System with Face Recognition

Below is a complete, step-by-step professional documentation for your: 🚀 AI-Based Smart ATM Security System (ESP32 + Face Recognition + IoT + n8n Automation + Telegram Alerts + Google Sheets + ThingSpeak + AI Agentic Workflow)
https://svsembedded.wordpress.com/2026/06/18/ai-based-smart-atm-security-system-with-face-recognition-2/ Below is a complete PHP-based documentation webpage version of your project. You can directly save it as: 👉 atm_security_system.php It will render as a full webpage describing your AI + IoT ATM Security System. ✅ 📄 PHP FILE: atm_security_system.php AI-Based Smart ATM Security System

🚀 AI-Based Smart ATM Security System

ESP32 + Face Recognition + IoT + n8n Automation + Telegram Alerts

📌 Project Overview

This system uses ESP32-CAM with AI-based face recognition to secure ATM access. It integrates IoT cloud platforms, Telegram alerts, Google Sheets logging, and n8n automation workflows.

🧰 Components List

  • ESP32-CAM module
  • FTDI Programmer
  • PIR Motion Sensor
  • Buzzer
  • Relay Module
  • Power Supply

🔌 Circuit Connections

PIR Sensor → GPIO 13
Buzzer     → GPIO 12
Relay      → GPIO 14
FTDI TX    → U0R
FTDI RX    → U0T
VCC        → 5V
GND        → GND
    

🔄 System Flow

Motion Detected → Capture Face → AI Verification
        ↓
Authorized → Grant Access
Unauthorized → Trigger Alarm + Send Alerts
        ↓
Log to Google Sheets + ThingSpeak
        ↓
n8n Automation → Telegram Notification
    

💻 ESP32 Sample Code

#include "esp_camera.h"
#include 

const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASS";

void setup() {
  Serial.begin(115200);
  WiFi.begin(ssid, password);
}

void loop() {
  Serial.println("Monitoring ATM...");
  delay(2000);
}
    

⚙️ n8n Automation Flow

Webhook Trigger → Telegram Alert → Google Sheets → ThingSpeak Update
    

📲 Telegram Bot Setup

Create Bot via BotFather:
/newbot

API Format:
https://api.telegram.org/bot/sendMessage
    

☁️ ThingSpeak Setup

https://api.thingspeak.com/update?api_key=API_KEY&field1=1
    

🧠 AI Power Prediction

if motion_events > 50:
    power = "HIGH"
elif motion_events > 20:
    power = "MEDIUM"
else:
    power = "LOW"
    

🚀 Future Enhancements

  • DeepFace AI recognition
  • Anti-spoofing detection
  • Mobile app dashboard
  • Blockchain security logs

© 2026 Smart ATM Security System | IoT + AI Project

🚀 How to Run Save file as: atm_security_system.php Place in: htdocs (XAMPP) / www (WAMP) Start Apache server Open in browser: http://localhost/atm_security_system.php 💡 If you want next upgrades, I can also build: ✔ Admin login panel (PHP + MySQL) ✔ Real-time ESP32 data dashboard ✔ Face recognition API backend (Python + Flask + PHP bridge) ✔ Live Telegram control panel ✔ AI fraud detection web app Just tell me 👍

AI-Based Smart Battery Management System for EV Applications

Below is a complete, structured engineering documentation for your project: 🚀 AI-Based Smart Battery Management System for EV Applications (ESP32 + IoT + n8n + AI Agent + Telegram Voice Alerts + Google Sheets + ThingSpeak)
Below is your complete project documentation converted into a single PHP webpage file format. You can directly save it as: index.php and run it on XAMPP / WAMP / any PHP server. ✅ 📄 FULL PHP WEBPAGE (IoT + AI BMS Documentation) AI-Based Smart Battery Management System (EV)

🚀 AI-Based Smart Battery Management System for EV Applications

📌 Project Overview

This project is an AI-powered Smart Battery Management System using ESP32, IoT cloud platforms, n8n automation, Telegram alerts, Google Sheets logging, and ThingSpeak dashboard for EV applications.

⚡ System Architecture

ESP32 Sensors → WiFi → ThingSpeak / n8n Webhook → AI Processing →
Decision Engine → Telegram Alerts → Google Sheets Logging

🔋 Components List

  • ESP32 Microcontroller
  • Voltage Sensor Module
  • Current Sensor (INA219 / ACS712)
  • Temperature Sensor
  • Relay Module
  • OLED Display (optional)
  • Battery Pack

🔌 Circuit Diagram (Text)

Battery → Voltage Sensor → ESP32 GPIO34
Current Sensor → I2C (SDA/SCL)
Temperature Sensor → GPIO 4
Relay → GPIO 26
Buzzer → GPIO 27

💻 ESP32 Code

#include <WiFi.h>
#include <HTTPClient.h>

const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASSWORD";

String server = "http://api.thingspeak.com/update?api_key=YOUR_KEY";

void setup() {
  Serial.begin(115200);
  WiFi.begin(ssid, password);
}

void loop() {
  float voltage = analogRead(34);
  float current = analogRead(35);
  float temp = analogRead(32);

  if(WiFi.status() == WL_CONNECTED){
    HTTPClient http;
    String url = server +
    "&field1=" + String(voltage) +
    "&field2=" + String(current) +
    "&field3=" + String(temp);

    http.begin(url);
    http.GET();
    http.end();
  }

  delay(5000);
}

⚙️ n8n Automation Flow

Webhook → IF Condition (Battery Check) →
Telegram Alert → Google Sheets Log

📲 Telegram Bot Setup

  1. Create bot using BotFather
  2. Get API Token
  3. Use token in n8n Telegram node
  4. Get Chat ID from getUpdates API

📊 Google Sheets Integration

Store real-time battery data such as voltage, current, temperature, SOC, and alerts.

☁️ ThingSpeak Dashboard

Used for real-time IoT visualization of EV battery parameters using channels and API keys.

🤖 AI Logic

Power = Voltage × Current

If Temp > 45°C → Warning
If Voltage < 11V → Critical Alert
SOC = (Current / Capacity) × 100

🔊 Voice Alert System

Telegram + AI Text-to-Speech system sends voice alerts when battery is critical.

🚀 Future Enhancements

  • Machine Learning Battery Prediction
  • Mobile App Dashboard
  • GPS Tracking System
  • Edge AI on ESP32

📦 Deployment Steps

  1. Upload ESP32 code
  2. Configure WiFi
  3. Setup n8n workflow
  4. Connect Telegram Bot
  5. Enable ThingSpeak channel
  6. Connect Google Sheets

⚡ Smart EV Battery Management System | AI + IoT + Automation

🚀 What you get with this PHP file ✔ Full project documentation website ✔ Clean UI dashboard style ✔ Ready to host on XAMPP/WAMP ✔ Beginner-friendly IoT explanation page ✔ Works as project submission website 🔥 If you want next upgrade, I can also create: 🌐 Full multi-page PHP website (login + dashboard + charts) 📊 Live real-time ESP32 data dashboard in PHP + MySQL 📈 Chart.js battery analytics dashboard 🤖 AI prediction integrated PHP backend 📱 Mobile responsive IoT web app UI Just tell 👍

AI-Based Smart Bus Tracking and Passenger Monitoring System

AI-Based Smart Bus Tracking & Passenger Monitoring System Using ESP32 + AI Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak
AI Smart Bus Tracking System

🚍 AI-Based Smart Bus Tracking System

ESP32 + IoT + AI + n8n + Telegram + Google Sheets + ThingSpeak

Last Updated:

📌 Project Overview

This system uses ESP32 to track bus location, monitor passengers, predict power usage using AI logic and send alerts via Telegram.

🧠 Key Features

  • Live GPS Tracking
  • Passenger Counting System
  • AI Power Prediction
  • Cloud Dashboard (ThingSpeak)
  • Google Sheets Logging
  • Telegram Voice Alerts via n8n

🔧 Components Used

  • ESP32 Board
  • NEO-6M GPS Module
  • IR Sensors (2)
  • ACS712 Current Sensor
  • DHT11 Sensor
  • Buzzer

📡 System Architecture

Sensors → ESP32 → Cloud (ThingSpeak / Google Sheets)
                 ↓
              n8n Automation
                 ↓
        Telegram Voice Alerts + Dashboard
        

📊 AI Prediction Formula

Power Prediction:

P = (0.5 × Temperature) + (0.8 × Passenger Count) + (0.3 × Current)
        

🚨 Alert System Logic

if (passengerCount > 40) {
    sendTelegramAlert("Overcrowding detected!");
}
        

📥 ESP32 Data Flow

GPS → Latitude, Longitude
IR Sensors → Passenger Count
DHT11 → Temperature
ACS712 → Power Usage

All data → ThingSpeak + Google Sheets
        

🤖 n8n Automation Flow

Webhook Trigger
   ↓
Check Passenger Limit
   ↓
Generate Alert Message
   ↓
Send Telegram Voice Notification
   ↓
Store Data in Google Sheets
        

📲 Telegram Integration

BotFather → Create Bot
Get Token
Send API Request:

https://api.telegram.org/bot/sendMessage
        

☁️ ThingSpeak Setup

Channel Fields:
Field1 → Latitude
Field2 → Longitude
Field3 → Passenger Count
Field4 → Temperature
Field5 → Power Prediction
        

📈 Future Enhancements

  • AI Camera Passenger Detection
  • Mobile App Integration
  • Face Recognition for Driver
  • Smart Ticketing System
  • Edge AI (TinyML on ESP32)

🏁 Conclusion

This project is a complete AI-powered IoT transportation system combining ESP32, cloud computing, automation, and real-time analytics for smart city applications.

✅ OPTIONAL (API FILE FOR ESP32 → PHP SERVER) If you want ESP32 to send data to PHP instead of ThingSpeak: create file: api.php ✅ ESP32 CALL EXAMPLE String url = "http://yourserver.com/api.php?lat=" + String(latitude,6) + "&lon=" + String(longitude,6) + "&pass=" + String(passengerCount) + "&temp=" + String(temp); If you want next upgrade 🚀 I can also convert this into: 🔥 Full React Dashboard UI 📊 Live Map tracking (Leaflet / Google Maps) 🤖 AI ML model integration (Python backend) 📱 Mobile App (Flutter) ☁️ AWS / Firebase version 📡 Real-time WebSocket dashboard Just tell 👍

AI-Based Smart Flood Detection and Early Warning System

AI-Based Smart Healthcare Assistant Chatbot with IoT Sensors https://svsembedded.wordpress.com/2026/06/18/ai-based-smart-flood-detection-and-early-warning-system/ ESP32 + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard
"; echo ""; echo "AI Smart Healthcare Assistant Project"; echo " "; echo ""; echo ""; echo "

AI-Based Smart Healthcare Assistant Chatbot with IoT Sensors

"; /* ===================================================== */ echo "
"; echo "

1. Project Overview

"; echo "

This project is an AI-powered Smart Healthcare Monitoring System using:

  • ESP32 Microcontroller
  • MAX30102 Heart Rate & SpO2 Sensor
  • DHT22 Temperature & Humidity Sensor
  • ThingSpeak Cloud Dashboard
  • n8n Automation
  • Telegram Bot Alerts
  • Google Sheets Logging
  • Voice Notification System
"; echo "
"; /* ===================================================== */ echo "
"; echo "

2. Components List

"; echo "
Component Quantity Purpose
ESP32 Dev Board 1 Main Controller
MAX30102 Sensor 1 Heart Rate & SpO2 Monitoring
DHT22 Sensor 1 Temperature & Humidity
OLED Display 1 Display Sensor Data
Buzzer 1 Emergency Alert
LEDs 2 Status Indicators
Breadboard 1 Circuit Prototyping
Jumper Wires Several Connections
"; echo "
"; /* ===================================================== */ echo "
"; echo "

3. Circuit Connections

"; echo "
Sensor ESP32 Pin
DHT22 DATA GPIO4
MAX30102 SDA GPIO21
MAX30102 SCL GPIO22
Buzzer GPIO18
OLED SDA GPIO21
OLED SCL GPIO22
"; echo "
"; /* ===================================================== */ echo "
"; echo "

4. System Flowchart

"; echo "
START

Initialize ESP32

Connect WiFi

Read Sensor Data

Analyze Health Conditions

Upload to ThingSpeak

Send Data to n8n

Store in Google Sheets

Check Alert Conditions

Send Telegram Voice Alert

Repeat Loop
"; echo "
"; /* ===================================================== */ echo "
"; echo "

5. ESP32 Arduino Source Code

"; echo "
#include <WiFi.h>
#include <HTTPClient.h>
#include "ThingSpeak.h"
#include "DHT.h"

const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASSWORD";

WiFiClient client;

unsigned long channelID = YOUR_CHANNEL_ID;
const char* writeAPIKey = "YOUR_API_KEY";

#define DHTPIN 4
#define DHTTYPE DHT22

DHT dht(DHTPIN, DHTTYPE);

float temperature;
float humidity;
int heartRate = 78;
int spo2 = 97;

void setup(){
Serial.begin(115200);
WiFi.begin(ssid, password);

while(WiFi.status() != WL_CONNECTED){
delay(1000);
Serial.println("Connecting...");
}

ThingSpeak.begin(client);
dht.begin();
}

void loop(){
temperature = dht.readTemperature();
humidity = dht.readHumidity();

ThingSpeak.setField(1, temperature);
ThingSpeak.setField(2, humidity);
ThingSpeak.setField(3, heartRate);
ThingSpeak.setField(4, spo2);

ThingSpeak.writeFields(channelID, writeAPIKey);

delay(15000);
}
"; echo "
"; /* ===================================================== */ echo "
"; echo "

6. ThingSpeak Setup

"; echo "
  1. Create ThingSpeak Account
  2. Create New Channel
  3. Add 4 Fields
  4. Copy Channel ID
  5. Copy Write API Key
  6. Paste into ESP32 Code
"; echo "
"; /* ===================================================== */ echo "
"; echo "

7. Telegram Bot Setup

"; echo "
  1. Open Telegram
  2. Search BotFather
  3. Type /newbot
  4. Enter Bot Name
  5. Enter Username
  6. Copy Bot Token
"; echo "
https://api.telegram.org/botYOUR_TOKEN/getUpdates
"; echo "
"; /* ===================================================== */ echo "
"; echo "

8. n8n Workflow

"; echo "

Workflow Process:

Webhook Trigger

Receive Sensor Data

Save to Google Sheets

Analyze Data

IF abnormal condition

Send Telegram Alert

Generate Voice Notification
"; echo "
"; /* ===================================================== */ echo "
"; echo "

9. Google Sheets Integration

"; echo "
Column Description
Timestamp Date and Time
Temperature Body Temperature
Humidity Humidity Value
Heart Rate BPM Value
SpO2 Oxygen Level
"; echo "
"; /* ===================================================== */ echo "
"; echo "

10. AI Power Prediction Logic

"; echo "
Power = Voltage × Current

AI adjusts upload frequency based on battery percentage.

Battery Level Action
> 70% Upload every 15 seconds
40% - 70% Upload every 1 minute
< 40% Deep Sleep Mode
"; echo "
"; /* ===================================================== */ echo "
"; echo "

11. Voice Notification Automation

"; echo "

Voice alerts are generated using:

  • Google Text-to-Speech API
  • Telegram Audio Messages
  • n8n Automation
Emergency Warning.
Patient oxygen level is critically low.
Please seek medical assistance.
"; echo "
"; /* ===================================================== */ echo "
"; echo "

12. Future Enhancements

"; echo "
  • Machine Learning Health Prediction
  • Firebase Integration
  • Mobile App Dashboard
  • GPS Tracking
  • ECG Monitoring
  • Cloud AI Analytics
"; echo "
"; /* ===================================================== */ echo "
"; echo "

13. Deployment Applications

"; echo "
Application Use Case
Hospitals Patient Monitoring
Elderly Care Remote Health Tracking
Fitness Monitoring Health Analytics
Rural Healthcare Remote Diagnostics
"; echo "
"; /* ===================================================== */ echo "
"; echo "

14. Conclusion

"; echo "

This AI-Based Smart Healthcare Assistant combines:

  • IoT
  • AI
  • Cloud Computing
  • Automation
  • Healthcare Analytics

The system provides real-time monitoring, AI predictions, Telegram alerts, cloud dashboards, and automated healthcare assistance.

"; echo "
"; /* ===================================================== */ echo "
"; echo "

15. Official Resources

"; echo " "; echo "
"; echo ""; echo ""; ?>

AI-Based Smart Healthcare Assistant Chatbot with IoT Sensors

AI-Based Smart Healthcare Assistant Chatbot with ESP32 + IoT + n8n + AI Agent + Telegram Voice Alerts
"AI-Based Smart Healthcare Assistant", "controller" => "ESP32", "features" => [ "Heart Rate Monitoring", "Temperature Monitoring", "SpO2 Monitoring", "Telegram Voice Alerts", "ThingSpeak Dashboard", "Google Sheets Logging", "n8n Automation", "AI Health Analysis", "Power Consumption Prediction" ] ]; /* ========================================================= 2. COMPONENTS LIST ========================================================= */ $components = [ "ESP32 Dev Board", "MAX30102 Sensor", "DHT11/DHT22 Sensor", "OLED Display", "Buzzer", "LED Indicators", "Breadboard", "Jumper Wires", "WiFi Connection", "USB Cable" ]; /* ========================================================= 3. CIRCUIT CONNECTIONS ========================================================= */ $circuit = [ "MAX30102" => [ "VIN" => "3.3V", "GND" => "GND", "SDA" => "GPIO21", "SCL" => "GPIO22" ], "DHT11" => [ "VCC" => "3.3V", "GND" => "GND", "DATA" => "GPIO4" ], "OLED" => [ "VCC" => "3.3V", "GND" => "GND", "SDA" => "GPIO21", "SCL" => "GPIO22" ], "BUZZER" => [ "+" => "GPIO15", "-" => "GND" ] ]; /* ========================================================= 4. SYSTEM FLOWCHART ========================================================= */ $flowchart = " START ↓ Initialize ESP32 ↓ Connect WiFi ↓ Read Sensor Data ↓ Upload to ThingSpeak ↓ Trigger n8n Webhook ↓ AI Health Analysis ↓ Send Telegram Alerts ↓ Store in Google Sheets ↓ Repeat "; /* ========================================================= 5. ESP32 SOURCE CODE ========================================================= */ $esp32_code = ' #include #include #include "DHT.h" #define DHTPIN 4 #define DHTTYPE DHT11 DHT dht(DHTPIN, DHTTYPE); const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String apiKey = "YOUR_THINGSPEAK_API"; void setup() { Serial.begin(115200); WiFi.begin(ssid, password); while (WiFi.status() != WL_CONNECTED) { delay(1000); Serial.println("Connecting..."); } dht.begin(); } void loop() { float temp = dht.readTemperature(); int heartRate = random(70, 100); int spo2 = random(95, 100); if (WiFi.status() == WL_CONNECTED) { HTTPClient http; String url = "http://api.thingspeak.com/update?api_key=" + apiKey + "&field1=" + String(temp) + "&field2=" + String(heartRate) + "&field3=" + String(spo2); http.begin(url); int code = http.GET(); Serial.println(code); http.end(); } delay(15000); } '; /* ========================================================= 6. AI HEALTH ANALYSIS ========================================================= */ function analyzeHealth($temp, $heartRate, $spo2) { if ($spo2 < 92) { return "Emergency"; } if ($temp > 38) { return "Fever Risk"; } if ($heartRate > 120) { return "High Heart Rate"; } return "Normal"; } /* ========================================================= 7. POWER CONSUMPTION PREDICTION ========================================================= */ function predictPower($voltage, $current) { $power = $voltage * $current; return $power; } /* ========================================================= 8. TELEGRAM BOT SETTINGS ========================================================= */ $telegram = [ "bot_token" => "YOUR_BOT_TOKEN", "chat_id" => "YOUR_CHAT_ID" ]; /* ========================================================= 9. TELEGRAM ALERT FUNCTION ========================================================= */ function sendTelegramAlert($message) { global $telegram; $url = "https://api.telegram.org/bot" . $telegram["bot_token"] . "/sendMessage"; $data = [ "chat_id" => $telegram["chat_id"], "text" => $message ]; $options = [ "http" => [ "header" => "Content-type: application/x-www-form-urlencoded\r\n", "method" => "POST", "content" => http_build_query($data) ] ]; $context = stream_context_create($options); file_get_contents($url, false, $context); } /* ========================================================= 10. GOOGLE SHEETS INTEGRATION ========================================================= */ $google_sheet_webhook = "https://script.google.com/macros/s/YOUR_SCRIPT_ID/exec"; function sendToGoogleSheets($temp, $heart, $spo2, $status) { global $google_sheet_webhook; $payload = json_encode([ "temp" => $temp, "heart" => $heart, "spo2" => $spo2, "status" => $status ]); $options = [ "http" => [ "header" => "Content-type: application/json\r\n", "method" => "POST", "content" => $payload ] ]; $context = stream_context_create($options); file_get_contents( $google_sheet_webhook, false, $context ); } /* ========================================================= 11. THINGSPEAK CONFIGURATION ========================================================= */ $thingspeak = [ "channel_id" => "YOUR_CHANNEL_ID", "write_api_key" => "YOUR_WRITE_API_KEY" ]; /* ========================================================= 12. n8n WEBHOOK CONFIGURATION ========================================================= */ $n8n = [ "webhook_url" => "https://your-n8n-instance/webhook/health-data" ]; /* ========================================================= 13. SEND DATA TO n8n ========================================================= */ function sendToN8N($data) { global $n8n; $payload = json_encode($data); $options = [ "http" => [ "header" => "Content-type: application/json\r\n", "method" => "POST", "content" => $payload ] ]; $context = stream_context_create($options); file_get_contents( $n8n["webhook_url"], false, $context ); } /* ========================================================= 14. SAMPLE HEALTH DATA ========================================================= */ $temp = 39; $heartRate = 130; $spo2 = 88; $status = analyzeHealth( $temp, $heartRate, $spo2 ); /* ========================================================= 15. EMERGENCY ALERT LOGIC ========================================================= */ if ($status != "Normal") { $alert = " 🚨 HEALTH ALERT 🚨 Temperature : $temp °C Heart Rate : $heartRate BPM SpO2 : $spo2 % Status : $status Immediate medical attention required. "; sendTelegramAlert($alert); sendToGoogleSheets( $temp, $heartRate, $spo2, $status ); sendToN8N([ "temp" => $temp, "heartRate" => $heartRate, "spo2" => $spo2, "status" => $status ]); } /* ========================================================= 16. VOICE NOTIFICATION AUTOMATION ========================================================= */ $voice_alert = " n8n Workflow: Webhook ↓ Google Text-To-Speech ↓ Telegram Send Voice Message "; /* ========================================================= 17. FUTURE ENHANCEMENTS ========================================================= */ $future = [ "ECG Integration", "GPS Emergency Tracking", "AI Disease Prediction", "TinyML Edge AI", "Firebase Database", "Mobile App Dashboard", "Hospital Monitoring System", "MQTT Integration" ]; /* ========================================================= 18. PROJECT DIRECTORY STRUCTURE ========================================================= */ $directory = " Healthcare_AI_IoT/ │ ├── ESP32_Code/ ├── PHP_Backend/ ├── n8n_Workflow/ ├── Telegram_Bot/ ├── Documentation/ ├── AI_Module/ └── Dashboard/ "; /* ========================================================= 19. OUTPUT DISPLAY ========================================================= */ echo "

AI Smart Healthcare Assistant

"; echo "

Project Features

"; echo "
";
print_r($project);
echo "
"; echo "

Components List

"; echo "
";
print_r($components);
echo "
"; echo "

Health Status

"; echo "

Status : $status

"; echo "

Power Prediction

"; $power = predictPower(5, 0.8); echo "

Power Consumption : $power W

"; echo "

Future Enhancements

"; echo "
";
print_r($future);
echo "
"; /* ========================================================= END OF PROJECT ========================================================= */ ?>

AI-Based Smart Shopping Trolley with Automatic Billing and Recommendations

AI-Based Smart Shopping Trolley with Automatic Billing & Recommendations ESP32 + RFID + Load Cell + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak
AI Smart Shopping Trolley Project

AI-Based Smart Shopping Trolley

ESP32 + RFID + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak

1. Project Overview

This project creates an intelligent shopping trolley that automatically scans products, calculates billing, sends cloud notifications, stores data in Google Sheets, and generates AI-based recommendations.

2. Main Features

  • Automatic Billing System
  • RFID Product Detection
  • ESP32 IoT Connectivity
  • AI Recommendation Engine
  • Google Sheets Cloud Logging
  • Telegram Notification Alerts
  • Telegram Voice Notifications
  • ThingSpeak Dashboard Monitoring
  • Power Consumption Prediction

3. Components List

Component Quantity
ESP32 Dev Board1
RFID RC522 Module1
RFID TagsMultiple
16x2 LCD Display1
HX711 Load Cell1
Load Cell Sensor1
Buzzer1
LEDs2
Battery/Power Bank1

4. System Architecture

RFID Products
      |
      V
+----------------+
| ESP32 Controller|
+----------------+
      |
 WiFi / HTTP
      |
      V
+----------------+
| n8n Automation |
+----------------+
   |      |     |
   V      V     V
Telegram Sheets ThingSpeak

5. Circuit Connections

RFID RC522 to ESP32

RFID ESP32
SDAGPIO5
SCKGPIO18
MOSIGPIO23
MISOGPIO19
RSTGPIO22
3.3V3.3V
GNDGND

6. Project Flowchart

START
   |
Initialize ESP32
   |
Connect WiFi
   |
Scan RFID Product
   |
Validate Product
   |
Add to Cart
   |
Update LCD Bill
   |
Send Data to n8n
   |
Store in Google Sheets
   |
Send Telegram Alert
   |
Update ThingSpeak
   |
END

7. ESP32 Source Code

#include <WiFi.h>
#include <HTTPClient.h>
#include <SPI.h>
#include <MFRC522.h>

#define SS_PIN 5
#define RST_PIN 22

MFRC522 rfid(SS_PIN, RST_PIN);

const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASSWORD";

String webhook =
"https://your-n8n-webhook-url";

void setup() {

  Serial.begin(115200);

  SPI.begin();
  rfid.PCD_Init();

  WiFi.begin(ssid, password);

  while(WiFi.status() != WL_CONNECTED){
      delay(1000);
      Serial.println("Connecting...");
  }

  Serial.println("WiFi Connected");
}

void loop() {

  if (!rfid.PICC_IsNewCardPresent())
      return;

  if (!rfid.PICC_ReadCardSerial())
      return;

  String uid = "";

  for (byte i = 0; i < rfid.uid.size; i++) {
      uid += String(rfid.uid.uidByte[i], HEX);
  }

  HTTPClient http;

  http.begin(webhook);
  http.addHeader("Content-Type",
  "application/json");

  String json =
  "{\"uid\":\""+uid+"\"}";

  int code = http.POST(json);

  Serial.println(code);

  http.end();

  delay(2000);
}

8. n8n Workflow

Webhook
   |
Function Node
   |
Google Sheets
   |
AI Recommendation
   |
Telegram Alerts
   |
ThingSpeak Dashboard

9. Telegram Bot Setup

  1. Open Telegram
  2. Search BotFather
  3. Create new bot using /newbot
  4. Copy BOT TOKEN
  5. Add token inside n8n Telegram node

10. Google Sheets Integration

Time Product Price Total
10:30 AM Milk 50 150

11. ThingSpeak Cloud Setup

  1. Create ThingSpeak Account
  2. Create New Channel
  3. Add Fields:
    • Total Bill
    • Product Count
    • Power Usage
  4. Copy API Key
  5. Send ESP32 data using HTTP API
https://api.thingspeak.com/update?
api_key=XXXX&field1=100

12. AI Recommendation Logic

IF Product = Bread
THEN Recommend = Butter, Jam

IF Product = Tea
THEN Recommend = Biscuits

13. Power Consumption Formula

Power = Voltage × Current

P = V × I

Example:

Voltage = 5V
Current = 0.2A

Power = 5 × 0.2
Power = 1 Watt

14. Future Enhancements

  • UPI Payment Integration
  • Mobile App Development
  • Computer Vision Product Detection
  • Face Recognition Login
  • Voice Assistant Support
  • AI Demand Prediction

15. Applications

  • Supermarkets
  • Shopping Malls
  • Retail Stores
  • Automated Billing Systems
  • Smart Warehouses

16. Advantages

  • Reduces Billing Time
  • Improves Customer Experience
  • Provides Real-Time Monitoring
  • Supports AI-Based Recommendations
  • Improves Retail Automation

17. Final Conclusion

The AI-Based Smart Shopping Trolley combines IoT, AI automation, cloud analytics, and smart retail technologies into a single intelligent system.

This project is ideal for:

  • Final Year Engineering Projects
  • IoT Research Projects
  • AI + Automation Demonstrations
  • Smart Retail Applications

AI Smart Shopping Trolley Project Documentation

ESP32 + RFID + AI + n8n + Telegram + Google Sheets + ThingSpeak