Friday, 6 November 2026

INSPIRE Awards – MANAK 2026 | 25+ Final Year Project Ideas for 2026 (CSE, ECE, EEE & IT Students)

1 . Patient Health Monitoring

- IoT Patient Health Monitoring System Using ESP32 | AI + n8n + Gmail + Telegram Alerts
- Patient Health Monitoring System Using ESP8266 & Arduino | AI + IoT + n8n Automation
- Smart Patient Health Monitoring Using ESP32 | AI Alerts + Telegram + Gmail + Google Sheets
- AI Based Patient Health Monitoring System Using IoT | ESP32 + Sensors + n8n Automation
- Final Year IoT Project: AI Patient Health Monitoring System | ESP32 + n8n + Telegram
- Advanced Patient Monitoring System Using ESP32 | AI + IoT + Gmail + Telegram Alert
- IoT Healthcare Project Using ESP32 | Real-Time Patient Monitoring + AI Emergency Alerts

2 . Disease Prediction

- AI Disease Prediction System Using Arduino & Sensors | IoT + n8n + Telegram + Gmail
- Multiple Disease Prediction Using AI & IoT | Arduino + Sensors | Final Year Project
- AI Based Disease Prediction System Using Arduino | Smart Healthcare IoT Project
- Machine Learning Disease Prediction Using Arduino & Sensors | AI + n8n Automation

 

🌱 Smart Agriculture

3 . Smart Agriculture

- Smart Agriculture Monitoring Using ESP32 & LoRa | AI + IoT + n8n + Telegram Alerts
- IoT Smart Agriculture Project Using ESP32 | Soil Moisture + Temperature + AI Prediction
- Smart Farming Using ESP32 LoRa | AI Crop Monitoring + n8n + Gmail + Telegram
- AI Based Smart Agriculture System Using ESP32 | IoT Sensors + Automated Alerts
- Final Year Smart Agriculture Project | ESP32 + LoRa + AI + n8n Automation
- Precision Agriculture Using IoT & AI | ESP32 + LoRa + Soil Monitoring System
- Smart Farm Monitoring System Using ESP32 & LoRa | AI Early Warning + Telegram Alerts

4 . Plant Disease

- AI Plant Disease Detection Using ESP32-CAM | IoT + Computer Vision + n8n Alerts
- Plant Disease Detection Using ESP32-CAM & AI | Smart Agriculture Final Year Project
- AI Plant Disease Detection Robot Using ESP32-CAM | IoT + AI + Telegram Alert
- ESP32-CAM AI Plant Disease Detection System | Smart Farming IoT Project

 

⚡ Smart Energy

5 . Smart Energy Meter

- AI Smart Energy Meter Using ESP32 | IoT Power Monitoring + n8n + Telegram Alerts
- Smart Energy Meter Using ESP32 | Voltage Current Power Monitoring + AI Analytics
- IoT Based Smart Energy Meter | AI Energy Prediction + Gmail + Telegram + Google Sheets
- AI Energy Monitoring System Using ESP32 | Smart Grid Final Year Project
- Smart Electricity Monitoring Using ESP32 | AI + IoT + n8n Automation
- IoT Energy Meter Project Using ESP32 | Real-Time Power Monitoring & AI Alerts

6 . Energy Theft Detection

- AI Energy Theft Detection Using ESP32 | Smart Energy Meter + IoT + n8n
- Smart Energy Meter with Electricity Theft Detection | AI + ESP32 + Telegram Alerts
- AI Based Electricity Theft Detection System Using IoT | ESP32 Final Year Project
- IoT Smart Meter with AI Energy Theft Detection | Gmail + Telegram + Google Sheets

 

Thursday, 5 November 2026

Smart Ration Distribution System Using RFID, OTP & GSM | Raspberry Pi Pico

1 RationX – An Intelligent and Secure Public Distribution System Using RFID, OTP and Automated Dispensing Futuristic identity.
2 GrainGuard 360 – A Smart Dual-Factor Authentication Framework for Transparent Ration Distribution Security innovation.
3 SmartPDS Next – Next-Generation IoT-Enabled Public Distribution and Automation Ecosystem Next-gen technology.
4 RationShield – An Intelligent Multi-Level Authentication System for Secure Food Distribution Authentication.
5 FairGrain – A Smart and Transparent Food Allocation and Distribution Framework Social impact.
6 RationVault – An Embedded Security Architecture for Intelligent Ration Distribution Data security.
7 GrainPass – A Digital Beneficiary Verification and Automated Food Distribution Platform Digital identity.
8 RationSphere – An Integrated IoT-Based Smart Ration Management Ecosystem Integrated system.
9 FoodSecure 360 – An Intelligent Food Distribution and Beneficiary Authentication Framework Food security.
10 RationSentinel – A Smart Monitoring and Authentication System for Automated Public Distribution Monitoring.
11 Design and Implementation of an IoT-Enabled Smart Public Distribution System Using RFID-Based Identification and OTP Authentication.
12 Development of a Secure Automated Ration Distribution Framework Integrating RFID, Dynamic OTP and GSM Communication.
13 An Intelligent Dual-Factor Authentication Architecture for Transparent and Automated Public Food Distribution.
14 Design and Development of a Microcontroller-Based Smart Fair Price Shop Automation System with Real-Time SMS Notifications.
15 An Embedded IoT Framework for Secure Beneficiary Identification and Automated Ration Dispensing.
16 Implementation of a Low-Cost Smart Public Distribution System Using RFID Authentication and Mobile-Based Verification.
17 A Secure and Intelligent Ration Management Architecture Using Raspberry Pi Pico and Wireless Communication.
18 Development of an Automated Food Allocation and Distribution System with Multi-Level Beneficiary Authentication.
19 An IoT-Based Public Distribution Monitoring Framework with Secure Access Control and Real-Time Transaction Notifications.
20 Design and Implementation of an RFID and GSM-Integrated Automated Ration Distribution System for Transparent Food Allocation.
21 TrustRation An Intelligent Beneficiary-Centric Secure Ration Distribution Mechanism.
22 RationMatrix A Multi-Layer Embedded Authentication and Distribution Architecture.
23 GrainSecure An Intelligent Access-Controlled Food Dispensing Framework.
24 RationLink A Connected Digital Identity and Smart Food Distribution Platform.
25 e-Grain 360 An Integrated Digital Food Distribution and Monitoring Ecosystem.
26 GrainLogic An Intelligent Embedded Framework for Automated Ration Allocation.
27 RationOne A Unified Authentication and Automated Distribution Solution.
28 FoodChain Secure A Smart Beneficiary Verification and Ration Transaction Monitoring Framework.
29 GrainTrust A Secure and Accountable Public Food Distribution Architecture.
30 RationNova A Next-Generation Intelligent Food Distribution and Authentication Platform.
31 Smart Ration Distribution System Using RFID, OTP and GSM Raspberry Pi Pico IoT Project
32 IoT-Based Smart Ration Management System Using RFID Authentication and OTP Verification.
33 Automated Public Distribution System Using Raspberry Pi Pico, RFID and GSM SMS Alert.
34 RFID-Based Smart Ration Distribution System with Mobile OTP Authentication Final Year Project
35 Smart PDS Automation Project Using RFID, OTP Generation and GSM Communication.
36 Raspberry Pi Pico Based Smart Ration Shop Automation RFID and GSM Integration.
37 Advanced IoT Ration Distribution System with Dual Authentication and Automatic Dispensing
38 Digital Public Distribution System Using RFID Card, OTP Verification and SMS Notification.
39 Smart Fair Price Shop Automation Using Embedded Systems, RFID and GSM Technology.
40 Final Year Engineering Project: Secure Ration Distribution Using RFID, OTP and Raspberry Pi Pico.
41 Intelligent Public Distribution 4.0: An Embedded IoT Framework for Secure and Automated Food Allocation.
42 GrainTech – A Smart Embedded Architecture for Digital Beneficiary Authentication and Automated Ration Dispensing.
43 RationVision – An Intelligent Monitoring and Secure Access-Control Framework for Public Distribution.
44 SmartGrain Nexus – An Integrated RFID, OTP and GSM-Based Food Distribution Architecture.
45 Digital Grain Network – A Connected and Secure Public Ration Distribution Ecosystem.
46 RationEdge – An Edge-Enabled Embedded Framework for Intelligent Ration Distribution.
47 SecurePDS – A Multi-Factor Authentication and Real-Time Monitoring Architecture for Smart Fair Price Shops.
48 GrainFlow – An Automated and Intelligent Food Allocation and Dispensing System.
49 RationSync – A Connected Beneficiary Authentication and Smart Distribution Management Platform.
50 NextGen PDS – A Secure, Automated and IoT-Enabled Public Food Distribution Framework.

Wednesday, 4 November 2026

INSPIRE-MANAK - 2026 | Major Final Year Project Ideas across CSE, ECE and EEE

💧 8. Smart Water & Environmental Monitoring

51. AquaGuard AI: Intelligent IoT-Based Water Quality Monitoring and Contamination Detection
52. WaterSense AI: Real-Time Multi-Parameter Water Quality Intelligence and Automated Alert System
53. AquaSentinel: AI-Enabled pH, Turbidity, Temperature and Water-Level Monitoring
54. HydroShield AI: Intelligent Water Quality Assessment Using IoT and Predictive Analytics
55. AquaMind: AI-Driven Water Quality Monitoring and Early Contamination Warning System

 

🎓 9. Smart Education & Campus

56. AttendAI: Intelligent RFID-Based Student Attendance and Automated Parent Notification System
57. CampusGuard AI: Intelligent Student Safety and Attendance Monitoring Platform
58. SmartAttend Nexus: AI-Enabled Attendance Analytics and Automated Parent Alert Framework
59. EduSense AI: Intelligent Student Attendance, Engagement and Academic Risk Monitoring
60. CampusSentinel: IoT-Based Smart Campus Monitoring and Automated Emergency Communication

 

🍽️ 10. Smart Restaurant & Food Technology

61. FoodGuard AI: Intelligent IoT-Based Food Spoilage Detection and Automated Alert System
62. FreshSense AI: Predictive Food Quality Monitoring Using IoT and Intelligent Analytics
63. SmartKitchen Nexus: AI-Enabled Food Safety Monitoring and Automated Chef Alert System
64. FoodShield AI: Real-Time Food Storage Condition Monitoring and Spoilage Prediction
65. RestaurantFlow AI: Intelligent E-Menu, Order Management and Automated Kitchen Notification System

 

🏠 11. Smart Home & Security

66. HomeShield AI: Intelligent Multi-Sensor Home Security and Automated Threat Notification
67. SecureNest AI: IoT-Based Smart Home Intrusion Detection and Intelligent Alert Management
68. SafeHome Nexus: AI-Enabled Real-Time Security Monitoring and Automated Emergency Response
69. GuardianHome AI: Intelligent Residential Security Monitoring Using IoT and AI
70. SmartSentinel: AI-Driven Multi-Sensor Security and Incident Detection Platform

 

🤖 12. AI + Computer Vision

71. PlantVision AI: Intelligent Plant Disease Detection Using ESP32-CAM and Computer Vision
72. ChildGuard AI: Intelligent Child Safety Monitoring with Emotion and Activity Recognition
73. DriverVision AI: Computer Vision-Based Driver Drowsiness Detection and Intelligent Alert System
74. VisionGuard: AI-Powered Real-Time Human Activity and Safety Monitoring
75. VisionSentinel AI: Edge-AI Computer Vision Framework for Intelligent Safety Monitoring

 

🚨 13. Emergency & Disaster Management

76. RescueNet AI: Intelligent IoT-Based Emergency Detection and Automated Multi-Channel Alert System
77. AlertGuard AI: Real-Time Multi-Sensor Emergency Detection and Intelligent Response Framework
78. DisasterSense AI: Predictive Environmental Hazard Detection and Automated Emergency Communication
79. EmergencyNexus: AI-Enabled IoT Emergency Monitoring and Intelligent Alert Escalation
80. RescueSentinel AI: Multi-Sensor Disaster Detection with Automated Emergency Notification

 

🧠 14. More Advanced / Patent-Style Titles

If the objective is a serious engineering final-year project , these titles sound more like research/patent concepts:

81. An AI-Driven Adaptive IoT Framework for Predictive Multi-Domain Safety Monitoring and Intelligent Alert Escalation
82. A Context-Aware Intelligent IoT Architecture for Real-Time Anomaly Detection and Automated Emergency Response
83. An Edge-AI and IoT-Based Predictive Monitoring Framework with Multi-Channel Automated Alert Orchestration
84. An Intelligent Multi-Sensor Fusion Framework for Predictive Fault Detection and Autonomous Alert Management
85. A Cloud-Connected AI-IoT Architecture for Real-Time Condition Monitoring, Risk Prediction and Automated Response
86. An AI-Assisted Event-Driven IoT Framework for Real-Time Monitoring, Predictive Analytics and Intelligent Notification
87. A Multi-Modal AI-IoT Framework for Continuous Environmental Monitoring and Predictive Hazard Detection
88. An Intelligent Edge-to-Cloud IoT Architecture for Anomaly Detection, Predictive Maintenance and Automated Incident Escalation
89. An Adaptive AI-Based Multi-Sensor Decision Support Framework for Real-Time Safety and Condition Monitoring
90. A Unified AI-IoT Automation Framework for Predictive Monitoring, Intelligent Decision-Making and Multi-Channel Alert Management

 

Tuesday, 3 November 2026

INSPIRE Awards – MANAK 2026 High-Impact Final-Year Project Titles For CSE, ECE and EEE

🏆 50 High-Impact Final-Year Project Titles

🏥 1. AI Healthcare & Biomedical Systems

1. HealthGuard AI: An Intelligent IoT-Based Patient Health Monitoring and Emergency Alert System
2. MediSense AI: Predictive Patient Health Monitoring Using IoT, AI and Automated Emergency Communication
3. CarePulse AI: Real-Time Multi-Parameter Patient Monitoring with Intelligent Risk Detection
4. VitalGuard: AI-Driven IoT Healthcare Monitoring and Automated Patient Alert Management
5. SmartCare Nexus: An AI-Enabled Remote Patient Monitoring and Emergency Response System
6. HealthShield AI: Intelligent Physiological Monitoring with Predictive Abnormality Detection
7. MediWatch: An IoT-AI Framework for Continuous Patient Monitoring and Automated Caregiver Alerts
8. NeuroCare AI: Intelligent Monitoring and Early-Risk Identification for Vulnerable Patients

🌾 2. AI Smart Agriculture

9. AgriVision AI: Intelligent Crop Health Monitoring and Early Plant Disease Detection Using IoT
10. FarmGuard AI: Predictive Smart Agriculture Monitoring with Multi-Sensor IoT and Intelligent Alerts
11. AgroSense Nexus: AI-Driven Soil, Weather and Crop Condition Monitoring for Precision Agriculture
12. CropShield AI: Intelligent Plant Disease Detection and Smart Agricultural Decision Support
13. AgriPulse: IoT-Based Precision Farming with AI-Powered Irrigation and Crop Stress Prediction
14. FarmSentinel AI: Long-Range LoRa-Based Agricultural Monitoring and Predictive Crop Protection
15. AgroGuardian: AI-Enabled Multi-Sensor Smart Farming and Automated Early-Warning System
16. GreenMind AI: Intelligent Crop Monitoring and Resource Optimization Using IoT and AI

⚡ 3. Smart Energy & Electrical Systems

17. GridGuard AI: Intelligent IoT-Based Electrical Load Monitoring and Anomaly Detection
18. PowerSense AI: Predictive Smart Energy Monitoring with Intelligent Consumption Analytics
19. EnergyShield: AI-Powered Smart Metering and Abnormal Energy Consumption Detection
20. GridMind AI: Intelligent Electrical Load Prediction and Real-Time Power Monitoring System
21. WattGuard AI: IoT-Based Energy Monitoring with Predictive Analytics and Automated Fault Alerts
22. PowerPulse: AI-Driven Real-Time Electrical Parameter Monitoring and Decision Support
23. EnergySentinel AI: Intelligent Energy Theft and Abnormal Consumption Detection Using IoT
24. SmartWatt Nexus: AI-Enabled IoT Energy Monitoring and Automated Energy Intelligence Platform

🔌 4. Transformer & Power-Grid Protection

25. TransformerGuard AI: Intelligent Three-Phase Transformer Health Monitoring and Predictive Fault Detection
26. GridShield-3P: AI-Enabled Real-Time Transformer Monitoring and Automated Protection Framework
27. TransfoSense AI: Predictive Transformer Health Assessment Using IoT Multi-Sensor Analytics
28. TransformerSentinel: Intelligent IoT-Based Transformer Fault Detection and Maintenance Alert System
29. PowerGuard-3P: AI-Driven Three-Phase Transformer Monitoring, Anomaly Detection and Protection
30. TransfoMind: Intelligent Transformer Condition Monitoring with Predictive Maintenance Analytics
31. SmartTransformer AI: IoT-Based Real-Time Thermal, Electrical and Load Condition Assessment
32. GridSentinel AI: Intelligent Distribution Transformer Monitoring and Early-Fault Warning System

🔧 5. Industrial IoT & Predictive Maintenance

33. IndustryGuard AI: Intelligent Multi-Sensor Industrial Fault Monitoring and Predictive Maintenance
34. MachineMind AI: IoT-Based Machine Health Monitoring and Early Failure Prediction
35. FactorySentinel: AI-Driven Industrial Equipment Monitoring and Automated Fault Escalation
36. PredictaMach AI: Intelligent Machine Condition Monitoring and Predictive Maintenance Framework
37. IndustrialShield AI: Real-Time Equipment Anomaly Detection Using IoT and Machine Learning
38. SmartFactory Nexus: AI-Enabled Industrial Asset Monitoring and Intelligent Maintenance Alerts

⛏️ 6. Mining & Worker Safety

39. MineGuard AI: Intelligent IoT-Based Coal Mine Safety and Worker Health Monitoring
40. SmartMine Sentinel: AI-Enabled Multi-Sensor Hazard Detection and Emergency Alert System
41. MineShield AI: Predictive Environmental Hazard Detection for Underground Mining
42. MinerSafe AI: Intelligent Worker Health and Mine Environment Monitoring Using IoT
43. DeepMine Guardian: AI-Powered Underground Safety Monitoring and Automated Emergency Communication
44. MineSense Nexus: Multi-Sensor IoT and AI Framework for Real-Time Mining Safety Intelligence

🚗 7. Intelligent Transportation & Vehicle Safety

45. DriveGuard AI: Intelligent Driver Drowsiness Detection and Emergency Alert System
46. RoadShield AI: IoT-Based Accident Detection, Location Tracking and Intelligent Emergency Response
47. SafeDrive Nexus: AI-Powered Driver Monitoring and Real-Time Road Safety Alert Platform
48. AutoGuardian AI: Intelligent Vehicle Accident Detection and Automated Emergency Communication
49. DriveSentinel: Multi-Sensor AI System for Drowsiness, Accident and Driver Safety Monitoring
50. RoadGuard AI: Intelligent Vehicle Safety Monitoring with GPS-Based Emergency Response

 

INSPIRE-MANAK - 2026 | Smart Alcohol Detection & Automatic Engine Locking System Using Arduino UNO, MQ-3 Alcohol Sensor

INSPIRE-MANAK 2026 Project

Smart Alcohol Detection and Automatic Engine Locking System Using Arduino UNO and MQ-3 Alcohol Sensor

Student Innovation Project INSPIRE-MANAK 2026

Alcohol Sensor Engine Lock with Arduino – SR Robotics
Alcohol Detection and Engine Immobilization System for Drunk Driving Prevention” | The Student Projec 1
Circuit design Alcohol detector - Tinkercad

1. Project overview

Project title: Smart Alcohol Detection and Automatic Engine Locking System for Drunken Drivers Using Arduino UNO.

Project idea: Develop an intelligent alcohol detection system that checks whether the driver has consumed alcohol before allowing a vehicle-start simulation. If alcohol is detected above a preset threshold, the system prevents the demonstration motor from starting and activates an alert.

This project aims to reduce drunk-driving incidents by introducing an alcohol detection mechanism into a vehicle-start authorization system.

The proposed prototype uses:

  • Arduino UNO as the main controller.

  • MQ-3 alcohol sensor to detect alcohol vapours.

  • Relay module to control a low-voltage demonstration motor.

  • LCD display to show system status.

  • Buzzer to provide an audio warning.

  • LEDs to indicate safe and unsafe conditions.

Important safety consideration: The student prototype described below controls only a small, isolated, low-voltage demonstration motor. It must not be connected to a real vehicle's ignition, starter, fuel pump, ECU, or engine wiring. Cutting propulsion while a vehicle is moving can cause a serious accident. Any real vehicle application would require automotive engineering, independent safety systems, and professional validation.

2. Problem statement

Drunk driving is one of the major causes of road accidents. Alcohol affects a driver's:

  • Reaction time.

  • Concentration.

  • Decision-making ability.

  • Vision and coordination.

  • Ability to control a vehicle safely.

Traditional vehicle starting systems generally do not check whether the driver has consumed alcohol.

Our project proposes a preventive system that checks for alcohol vapours before authorizing a vehicle-start demonstration.

Proposed solution

  1. The driver approaches the alcohol sensor.

  2. The MQ-3 sensor detects alcohol vapours.

  3. Arduino UNO reads the sensor's analogue output.

  4. The controller compares the reading against a calibrated threshold.

  5. If the reading is below the threshold, the demonstration motor is permitted to start.

  6. If the reading exceeds the threshold, the motor remains disabled.

  7. The buzzer and warning LED indicate alcohol detection.

3. Main objectives

  1. Detect alcohol vapours using an MQ-3 sensor.

  2. Develop an automatic vehicle-start prevention prototype.

  3. Control a demonstration motor using Arduino UNO.

  4. Display system status through an LCD.

  5. Generate an alert when alcohol is detected.

  6. Develop an affordable and educational road-safety solution.

  7. Demonstrate how sensor-based automation can improve driver safety.

4. Components required

ARDUINO® Uno Rev3

        – Robot Pi Shop
MQ-3 Alcohol Gas Sensor

        – Robot Pi Shop
LCD1602 I2C Module, White color with blue background, 16x2 characters LCD, 3.3V/5V

Component

Quantity

Purpose

Arduino UNO

1

Main controller

MQ-3 alcohol sensor module

1

Alcohol vapour detection

16×2 LCD with I2C

1

Display messages

5V relay module

1

Demonstration motor control

Active buzzer

1

Warning sound

Red LED

1

Alcohol warning

Green LED

1

Safe/start permitted indication

220Ω resistors

2

LED current limiting

Push button

1

Start request

Breadboard

1

Circuit assembly

Jumper wires

As required

Connections

Low-voltage DC motor

1

Vehicle-start demonstration

Suitable motor power supply

1

Motor power

5. Working principle

The project operates using the sensing capability of the MQ-3 alcohol sensor.

MQ-3 alcohol sensor

The MQ-3 is a semiconductor gas sensor designed to respond to alcohol vapours.

It contains a sensing element whose electrical resistance changes when exposed to alcohol-containing vapours.

The module provides:

  • VCC – power supply.

  • GND – ground.

  • AO – analogue output.

  • DO – digital output, depending on the module.

For this project, we use the AO pin because Arduino can read different analogue levels.

Arduino UNO

Arduino UNO acts as the brain of the system.

Its responsibilities include:

  • Reading the alcohol sensor.

  • Comparing readings with a threshold.

  • Processing the driver's start request.

  • Controlling the LCD.

  • Activating the buzzer.

  • Controlling the demonstration motor relay.

Relay module

The relay provides electrical switching for the low-voltage demonstration motor.

In this project, the motor is enabled only when the controller authorizes a start request.

LCD display

The LCD displays messages such as:

  • SYSTEM INITIALIZING

  • SENSOR WARMING

  • READY TO TEST

  • ALCOHOL DETECTED

  • START PERMITTED

  • MOTOR DISABLED

6. Complete system block diagram

The logical block diagram is:

text
           DRIVER
              |
              v
       MQ-3 ALCOHOL SENSOR
              |
              v
          ARDUINO UNO
              |
       +------+------+
       |      |      |
       v      v      v
      LCD   BUZZER  LEDS
              |
              v
       START AUTHORIZATION
              |
       +------+------+
       |             |
       v             v
   ALCOHOL       NO ALCOHOL
   DETECTED      ABOVE THRESHOLD
       |             |
       v             v
   MOTOR OFF     MOTOR ENABLED
   RED LED ON    GREEN LED ON

7. Circuit connections

Arduino UNO pin connection table

Component

Component pin

Arduino UNO

MQ-3 sensor

VCC

5V

MQ-3 sensor

GND

GND

MQ-3 sensor

AO

A0

LCD I2C

VCC

5V

LCD I2C

GND

GND

LCD I2C

SDA

A4

LCD I2C

SCL

A5

Relay module

VCC

5V

Relay module

GND

GND

Relay module

IN

D8

Buzzer

Positive

D9

Buzzer

Negative

GND

Red LED

Anode through 220Ω

D6

Green LED

Anode through 220Ω

D7

Push button

One terminal

D2

Push button

Other terminal

GND

Demonstration motor relay connections

Use a separate, appropriately rated low-voltage supply for the demonstration motor.

text
       ISOLATED LOW-VOLTAGE MOTOR CIRCUIT

       Motor supply (+)
              |
              |
        Relay COM
              |
        Relay NO
              |
          DC MOTOR
              |
       Motor supply (-)

The relay module's control side connects to Arduino. Its switching contacts control only the isolated demonstration motor circuit.

Do not connect this relay to a real vehicle.

8. Schematic diagram

The following is the simplified electrical architecture to use when assembling the prototype:

text
                   +------------------+
                   |    ARDUINO UNO   |
                   |                  |
 MQ3 AO ---------->| A0               |
 LCD SDA --------->| A4               |
 LCD SCL --------->| A5               |
 Relay IN -------->| D8               |
 Buzzer (+) ------>| D9               |
 Red LED ----------| D6               |
 Green LED --------| D7               |
 Start Button ---->| D2               |
                   |                  |
                   +------------------+
                      |      |
                     5V     GND
                      |      |
              +-------+------+-------+
              |              |       |
            MQ-3           LCD     Relay

Assembly precautions:

  • Check all power connections before switching on.

  • Use a current-limited USB supply for the Arduino.

  • Never power the DC motor directly from an Arduino GPIO pin.

  • Use a motor supply suitable for the motor and relay ratings.

  • Keep motor wiring separate from sensor wiring to reduce electrical interference.

9. System flowchart

This flowchart explains the complete working sequence.

mermaid
flowchart TD
    A([START]) --> B[Power ON]
    B --> C[Initialize Arduino]
    C --> D[Initialize MQ3, LCD and Relay]
    D --> E[Sensor Warm-up]
    E --> F[Read Alcohol Sensor]
    F --> G{Sensor Reading Valid?}
    G -->|No| H[Motor Disabled]
    H --> I[Display Sensor Error]
    I --> F
    G -->|Yes| J{Alcohol Above Threshold?}
    J -->|Yes| K[Display Alcohol Detected]
    K --> L[Red LED and Buzzer ON]
    L --> M[Motor Disabled]
    M --> F
    J -->|No| N[Display Ready]
    N --> O{Start Button Pressed?}
    O -->|No| F
    O -->|Yes| P[Green LED ON]
    P --> Q[Enable Demonstration Motor]
    Q --> F

10. Decision-making flowchart

The main decision is based on the sensor's analogue reading.

mermaid
flowchart TD
    A[Read MQ3 Sensor] --> B[Obtain Analogue Value]
    B --> C[Compare With Calibrated Threshold]
    C --> D{Reading Above Threshold?}
    D -->|YES| E[Alcohol Warning]
    E --> F[Disable Motor]
    F --> G[Activate Buzzer]
    D -->|NO| H[Show Safe Sensor Reading]
    H --> I{Start Button Pressed?}
    I -->|YES| J[Permit Demo Motor]
    I -->|NO| K[Wait]
    J --> A
    G --> A
    K --> A

11. Step-by-step construction procedure

Step 1: Prepare the Arduino UNO

  • Take an Arduino UNO board.

  • Connect it to a computer using a USB cable.

  • Install Arduino IDE.

  • Verify that the Arduino board is detected.

  • Select Arduino UNO under Tools → Board.

Step 2: Connect the MQ-3 alcohol sensor

  • Connect VCC to Arduino 5V.

  • Connect GND to Arduino GND.

  • Connect AO to Arduino A0.

  • Allow the sensor to warm up.

  • Observe the readings using the Serial Monitor.

The MQ-3 requires a warm-up period. Its readings may drift, especially when the sensor is new or environmental conditions change. Follow the manufacturer's conditioning instructions.

Step 3: Connect the LCD

  • Connect LCD VCC to 5V.

  • Connect LCD GND to GND.

  • Connect SDA to A4.

  • Connect SCL to A5.

Install the LiquidCrystal_I2C library through Arduino IDE Library Manager.

Step 4: Connect the warning indicators

  • Connect the red LED to D6 through a 220Ω resistor.

  • Connect the green LED to D7 through a 220Ω resistor.

  • Connect the buzzer positive terminal to D9.

  • Connect the buzzer negative terminal to GND.

If your buzzer draws more current than a GPIO pin can safely supply, use a suitable transistor driver.

Step 5: Connect the start button

  • Connect one button terminal to D2.

  • Connect the other terminal to GND.

  • The program uses Arduino's internal pull-up resistor.

Step 6: Connect the demonstration relay

  • Connect relay VCC to 5V.

  • Connect relay GND to GND.

  • Connect relay IN to D8.

  • Use the relay's normally open contact in the isolated low-voltage motor circuit.

Step 7: Upload the program

  • Open Arduino IDE.

  • Create a new sketch.

  • Copy the program below.

  • Install the required LCD library.

  • Select the correct COM port.

  • Upload the program.

  • Open Serial Monitor at 9600 baud.

12. Arduino software program

Complete educational prototype code

The following program implements sensor reading, threshold comparison, LCD messages, audio and visual alerts, and low-voltage demonstration motor authorization.

It deliberately does not implement real vehicle ignition or engine-control wiring.

cpp

#include <Wire.h>
#include <LiquidCrystal_I2C.h>

// LCD address may be 0x27 or 0x3F
LiquidCrystal_I2C lcd(0x27, 16, 2);

// Pin configuration
const int alcoholSensor = A0;
const int relayPin = 8;
const int buzzerPin = 9;
const int redLED = 6;
const int greenLED = 7;
const int startButton = 2;

// Calibrate this value using clean-air readings
// and controlled sensor testing.
const int alcoholThreshold = 400;

// Many relay modules are active LOW.
// Change these if your relay module differs.
const int RELAY_ON = LOW;
const int RELAY_OFF = HIGH;

bool motorEnabled = false;

void disableMotor() {
  digitalWrite(relayPin, RELAY_OFF);
  motorEnabled = false;
}

void setup() {

  Serial.begin(9600);

  pinMode(relayPin, OUTPUT);
  pinMode(buzzerPin, OUTPUT);
  pinMode(redLED, OUTPUT);
  pinMode(greenLED, OUTPUT);
  pinMode(startButton, INPUT_PULLUP);

  disableMotor();

  digitalWrite(redLED, LOW);
  digitalWrite(greenLED, LOW);
  digitalWrite(buzzerPin, LOW);

  lcd.init();
  lcd.backlight();

  lcd.setCursor(0, 0);
  lcd.print("SMART ALCOHOL");
  lcd.setCursor(0, 1);
  lcd.print("DETECTION SYSTEM");

  delay(2000);

  lcd.clear();
  lcd.print("SENSOR WARMING");
  lcd.setCursor(0, 1);
  lcd.print("PLEASE WAIT");

  // Initial demonstration warm-up delay.
  // Follow the sensor manufacturer's full
  // conditioning and warm-up requirements.
  delay(30000);

  lcd.clear();
  lcd.print("SYSTEM READY");
  delay(1500);
}

void loop() {

  int alcoholValue = analogRead(alcoholSensor);

  Serial.print("MQ3 Reading: ");
  Serial.println(alcoholValue);

  lcd.clear();

  lcd.setCursor(0, 0);
  lcd.print("Alcohol:");
  lcd.print(alcoholValue);

  // Alcohol threshold exceeded
  if (alcoholValue >= alcoholThreshold) {

    disableMotor();

    digitalWrite(redLED, HIGH);
    digitalWrite(greenLED, LOW);
    digitalWrite(buzzerPin, HIGH);

    lcd.setCursor(0, 1);
    lcd.print("ALCOHOL DETECTED");

    Serial.println("WARNING: MOTOR DISABLED");

  }

  // Reading below threshold
  else {

    digitalWrite(redLED, LOW);
    digitalWrite(buzzerPin, LOW);

    lcd.setCursor(0, 1);
    lcd.print("CHECK START");

    // Permit only a button-requested
    // low-voltage demonstration motor start.
    if (digitalRead(startButton) == LOW) {

      digitalWrite(greenLED, HIGH);
      digitalWrite(relayPin, RELAY_ON);

      motorEnabled = true;

      lcd.clear();
      lcd.print("START PERMITTED");
      lcd.setCursor(0, 1);
      lcd.print("DEMO MOTOR ON");

      Serial.println("DEMO MOTOR ENABLED");

      delay(1000);
    }
    else {

      digitalWrite(greenLED, LOW);

      if (!motorEnabled) {
        disableMotor();
      }
    }
  }

  delay(500);
}

Explanation of the program

Program section

Function

Library inclusion

Enables LCD communication

Pin configuration

Defines Arduino connections

setup()

Initializes the components

analogRead()

Reads the MQ-3 sensor

Threshold comparison

Identifies elevated sensor readings

disableMotor()

Disables the demonstration motor

Red LED

Indicates an alcohol warning

Green LED

Indicates start authorization

Buzzer

Produces an alert

LCD

Displays the operating condition

Start button

Requests demonstration motor activation

Note about the sample code: The value 400 is only an example threshold, not a scientifically validated alcohol concentration or legal blood-alcohol limit. The program also uses a simple demonstration threshold, not a certified breath-alcohol measurement. MQ-3 readings can be affected by other vapours, temperature, humidity, sensor age and calibration.

Before testing, inspect the relay's active-high/active-low behaviour and adjust the software constants to match the actual module.

13. Sensor calibration and threshold setting

This is one of the most important stages of the project.

The MQ-3 does not directly provide a blood-alcohol concentration. It provides an analogue voltage that changes with the surrounding vapour concentration.

Calibration procedure

  1. Power the sensor and allow it to stabilize according to its datasheet.

  2. Keep the sensor in a clean-air environment.

  3. Observe the analogue readings in Serial Monitor.

  4. Record the baseline readings.

  5. Perform controlled calibration using an appropriate certified alcohol reference gas and suitable laboratory procedures.

  6. Record the corresponding sensor readings.

  7. Select an experimental threshold for the demonstration.

  8. Test the prototype repeatedly to check whether the readings are stable.

Example of a results-recording table:

Test condition

Analogue reading

Expected response

Clean-air baseline

To be measured

Start may be permitted

Reference gas below experimental threshold

To be measured

Start may be permitted

Reference gas above experimental threshold

To be measured

Motor disabled

Sensor disconnected

To be measured

Requires separate fault detection

Do not interpret these analogue readings as a person's actual blood-alcohol concentration or fitness to drive.

14. Complete working explanation

The complete working process is as follows:

Stage 1: Power supply

When the system receives power, Arduino UNO starts executing the program.

Stage 2: Sensor initialization

The MQ-3 sensor begins warming up. The LCD displays the initialization message.

Stage 3: Alcohol sensing

The sensor responds to alcohol vapours in its surroundings and produces an analogue signal.

Stage 4: Signal processing

Arduino reads the signal using its analogue input A0.

Stage 5: Decision-making

Arduino compares the reading with the experimentally calibrated threshold.

Stage 6: Alcohol warning condition

If the reading is above the threshold:

  • Red LED turns ON.

  • Buzzer turns ON.

  • LCD displays ALCOHOL DETECTED.

  • Demonstration motor remains disabled.

Stage 7: Normal condition

If the reading is below the threshold:

  • Red LED turns OFF.

  • Buzzer turns OFF.

  • LCD displays CHECK START.

  • Green LED indicates authorization when the start button is pressed.

  • Demonstration motor is enabled.

Stage 8: Continuous monitoring

Arduino continuously reads the sensor and updates the demonstration system's status.

15. Project operation chart

Parameter

Alcohol warning condition

Normal sensor condition

MQ-3 reading

Above threshold

Below threshold

LCD

Alcohol detected

Check start

Red LED

ON

OFF

Green LED

OFF

ON when start requested

Buzzer

ON

OFF

Relay

Disabled

Enabled on start request

Demo motor

OFF

ON when authorized

16. Testing and validation

After assembly, perform the following tests.

Test

Procedure

Expected result

Test 1

Switch ON Arduino

LCD initializes

Test 2

Allow sensor warm-up

Sensor readings stabilize

Test 3

Check clean-air readings

Baseline is displayed

Test 4

Apply certified reference gas below threshold

Start authorization possible

Test 5

Apply certified reference gas above threshold

Motor disabled

Test 6

Press start button in permitted condition

Demo motor starts

Test 7

Increase sensor reading above threshold

Demo motor is disabled

Test 8

Remove sensor connection

Fault behaviour must be investigated and improved

These are proposed test cases, not claimed experimental results. Record your actual readings and observations during testing.

17. Advantages of the project

  1. Helps demonstrate preventive drunk-driving technology.

  2. Uses affordable electronic components.

  3. Simple Arduino-based implementation.

  4. Provides audio and visual warnings.

  5. Demonstrates automatic start authorization.

  6. Can be expanded with IoT technology.

  7. Suitable for educational electronics and road-safety demonstrations.

  8. Provides an opportunity to study sensors, embedded systems and automation.

18. Limitations

Every engineering project has limitations.

  • MQ-3 is not a certified evidential breathalyser.

  • It can respond to interfering vapours.

  • Temperature and humidity may affect measurements.

  • Sensor warm-up and calibration are important.

  • A single sensor cannot guarantee that the driver is sober.

  • Sensor readings cannot establish legal intoxication.

  • Power failure, wiring faults and relay failures require additional safety measures.

  • The educational prototype is not suitable for installation in a real vehicle.

19. Future scope

The project can be developed further with additional technologies.

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1. IoT-based monitoring

Use ESP32 to send system status to a monitoring dashboard.

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2. GPS integration

Add location reporting for a stationary demonstration or safety-monitoring prototype.

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3. Advanced display

Show sensor status, calibration state, system errors and test results.

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4. Cloud monitoring

Record demonstration test events and sensor performance.

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5. Advanced driver monitoring

Research additional independent driver-safety technologies, with appropriate privacy and safety protections.

20. Complete project documentation

The following sections can be included directly in your INSPIRE-MANAK project report.

A. Abstract

Smart Alcohol Detection and Automatic Engine Locking System Using Arduino UNO is an innovative road-safety project designed to demonstrate how alcohol detection technology can be used to prevent unauthorized vehicle starting.

The proposed system uses an MQ-3 alcohol sensor to detect alcohol vapours. Arduino UNO processes the sensor output and compares it with a predefined experimental threshold. When the sensor reading exceeds the threshold, the controller activates a warning buzzer, switches ON a red LED, displays an alcohol detection message on the LCD and disables the low-voltage demonstration motor.

When the sensor reading is below the threshold, the system permits a start request through a push button and activates the demonstration motor.

The project demonstrates the application of embedded systems, gas sensors and automatic control technology to road safety. It is a low-cost educational prototype that can be further developed through improved sensing, calibration and independent safety validation.

B. Introduction

Road accidents caused by drunk driving are a major public safety concern. Alcohol consumption can affect the human nervous system and reduce the ability to control vehicles safely.

This project introduces a smart alcohol detection system that demonstrates how a vehicle-start authorization mechanism could work.

The system uses an MQ-3 sensor connected to an Arduino UNO. The sensor detects alcohol vapours, and the microcontroller processes the output to determine whether a start request should be permitted in the demonstration model.

The primary goal is to demonstrate preventive road-safety technology using affordable electronic components.

C. Need for the project

  • To create awareness about drunk driving.

  • To demonstrate alcohol detection using electronic sensors.

  • To explore automatic vehicle-start authorization.

  • To develop an affordable student innovation prototype.

  • To encourage the use of technology for public safety.

D. Innovation and uniqueness

The innovative aspect of this project is the combination of:

  1. Alcohol vapour sensing.

  2. Automatic electronic decision-making.

  3. Audio warning.

  4. Visual indication.

  5. Start authorization.

  6. Demonstration motor control.

The system combines these functions into one compact prototype using Arduino UNO.

E. Social impact

The project has the potential to contribute to road-safety awareness by demonstrating how preventive technology can discourage drunk driving.

Its educational value includes:

  • Promoting responsible driving.

  • Creating awareness among young people.

  • Encouraging engineering innovation.

  • Demonstrating practical applications of electronics.

  • Inspiring students to develop safety-focused technologies.

F. Conclusion

The Smart Alcohol Detection and Automatic Engine Locking System using Arduino UNO is a student-oriented innovation project that demonstrates alcohol vapour sensing and automatic start authorization.

The MQ-3 sensor detects alcohol vapours, while Arduino UNO processes the sensor output. Based on the experimental threshold, the system either permits or denies a demonstration motor start request.

The LCD, buzzer and LEDs provide clear indications of the system's operating condition.

This project demonstrates how embedded systems and sensor technology can be applied to road-safety education. With further research, improved sensing, calibration and professional safety engineering, the concept could contribute to the development of more advanced driver-safety technologies.

G. References and learning resources

For the final report, consult:

  1. Arduino UNO official documentation – Arduino UNO Rev3 .

  2. MQ-3 alcohol sensor datasheet – obtain the datasheet corresponding to your sensor manufacturer and module.

  3. Arduino programming reference – Arduino Language Reference .

  4. LiquidCrystal_I2C library documentation – LiquidCrystal_I2C library .

  5. Basic embedded systems and electronic circuit design textbooks.

21. Suggested project report structure

For a professional school-level project submission, arrange the report in this order:

Chapter

Topic

1

Introduction

2

Problem statement

3

Objectives

4

Proposed solution

5

Components and specifications

6

Working principle

7

Block diagram

8

Circuit and schematic diagram

9

Flowcharts

10

Hardware implementation

11

Software implementation

12

Arduino source code

13

Testing and observations

14

Advantages and limitations

15

Future scope

16

Social impact

17

Conclusion

18

References

22. INSPIRE-MANAK presentation plan

Alcohol Sensor Engine Lock with Arduino – SR Robotics
Alcohol Detection and Engine Immobilization System for Drunk Driving Prevention” | The Student Projec 1
9th class students made alcohol detection device in Kanpur | कानपुर में 9वीं के छात्रों ने बनाई एल्कोहल पहचानने वाली-डिवाइस: शराब पीकर तेज गाड़ी चलाने पर बंद हो जाएगा इंजन, ऑटोमैटिक 3 नंबरों पर पहुंचेगा मैसेज - Kanpur News | Dainik Bhaskar

For your project exhibition, I recommend preparing these five items:

  1. Working model: Arduino UNO, MQ-3 sensor, LCD, buzzer, LEDs and a small demonstration motor.

  2. Project display board: Problem, objectives, innovation, working principle and social benefits.

  3. Circuit diagram: Clearly labelled connections.

  4. Flowchart: Sensor detection and start authorization.

  5. Demonstration video: Show both normal and alcohol-warning conditions using controlled reference testing.

23. Questions judges may ask

Question

Suggested answer

Why did you select MQ-3?

It is designed to respond to alcohol vapours and is suitable for an educational detection prototype.

Why Arduino UNO?

It is affordable, programmable and has analogue inputs for sensor measurements.

What happens when alcohol is detected?

The warning is activated and the demonstration motor is disabled.

What is the function of the relay?

It electrically switches the low-voltage demonstration motor.

Why use an LCD?

To display sensor readings and operating conditions.

Can this project prevent all drunk-driving incidents?

No. It demonstrates one possible preventive technology and has sensor limitations.

Can it be installed in a real car?

Not in its present form. Professional automotive safety engineering and validation would be necessary.

What is the main innovation?

Combining alcohol sensing with automatic start authorization and warning indications.

24. Recommended final project title

SMART ALCOHOL DETECTION AND AUTOMATIC ENGINE START LOCKING SYSTEM

Using Arduino UNO and MQ-3 Alcohol Sensor

INSPIRE-MANAK 2026

A student innovation project for road safety using embedded systems and sensor technology.

My recommendation: For INSPIRE-MANAK, emphasize not just the Arduino circuit but the actual social problem, your innovative solution, how it works, its limitations and how you would make it safer and more reliable. A well-explained working model and your own experimental observations will make the project report much stronger.