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Working IoT, AI/ML and embedded builds with abstracts, component lists, documentation, and developer support — ready for your final-year submission.
IoT-Based Pollution Detection and Monitoring System
Rapid urbanization and industrialization have significantly contributed to the increase in air pollution, posing severe health risks and environmental degradation. To combat this challenge, real-time monitoring of air quality has become essential. This project introduces an IoT-Based Pollution Detection and Monitoring System, designed to continuously monitor environmental conditions and provide real-time data visualization through a web-based interface. The system is powered by an ESP32 microcontroller, which collects air quality data using MQ-6 and MQ-7 gas sensors, capable of detecting harmful gases such as carbon monoxide (CO), methane (CH₄), and liquefied petroleum gas (LPG). Additionally, a temperature sensor is included to correlate environmental heat levels with pollution severity. These sensors are interfaced with the ESP32, which processes the data and transmits it over Wi-Fi to a Flask-based backend server. The backend stores the data and feeds it to a user-friendly front-end dashboard, accessible via any web browser. The dashboard displays real-time pollution levels, historical data trends, and alerts when gas concentrations exceed predefined thresholds. This enables authorities, environmental agencies, and individuals to make informed decisions and take timely action to reduce exposure to hazardous air conditions. This system provides a cost-effective, scalable, and portable solution for air quality monitoring in urban areas, industrial zones, schools, and public spaces. By combining sensor technology with IoT and web-based monitoring, the project offers a significant step toward achieving smarter, healthier, and environmentally conscious cities.
Collision Avoidance System for Hairpin Bends Using ESP32-CAM
Hairpin bends on hilly and mountainous roads are notorious for causing vehicle collisions due to limited visibility and lack of intelligent traffic management. To address this critical safety challenge, this project presents a Collision Avoidance System for Hairpin Bends, integrating computer vision, machine learning, and real-time alert mechanisms to intelligently manage traffic flow and reduce accident risk. The system is built using an ESP32-CAM module, strategically placed at a hairpin turn to monitor incoming vehicles in real time. The camera captures live video feeds and sends them to a NodeMCU-controlled server for further processing. A machine learning model is employed to classify the type of vehicle — particularly identifying large vehicles such as trucks and buses — and determines the priority of passage. The model is trained to decide that larger vehicles should be given right of way to reduce the risk of obstruction or collision in narrow bends. Once a large vehicle is detected, the system activates a traffic light module (Red, Yellow, Green) to control vehicle movement on either side of the bend. A 16x2 LCD display provides on-site information to drivers about the vehicle type detected and the current signal status, while a buzzer alerts drivers to stop or proceed. All components are mounted on a custom PCB board for robustness and field deployment, and the entire system is powered using wired infrastructure suitable for roadside installation. This intelligent, automated system ensures safe passage through blind turns by combining edge detection, vehicle classification, and coordinated signaling, thus improving safety on dangerous terrains. It demonstrates a practical, low-cost, and scalable solution to one of the most common causes of accidents on mountain roads and can be extended to tunnels, single-lane bridges, or other low-visibility traffic zones.
Fingerprint Recognition-Based Attendance System
The Fingerprint Recognition-Based Attendance System is an IoT-based smart solution designed to automate attendance management using biometric identification. This system employs a fingerprint sensor connected to a NodeMCU (ESP8266) microcontroller to authenticate individuals uniquely and reliably. When a user places their finger on the sensor, the system compares the scanned fingerprint with the stored templates. Upon a successful match, the attendance is marked and updated in a real-time database through the Wi-Fi-enabled NodeMCU. The data is then displayed and managed through an intuitive frontend interface, allowing administrators to view, track, and export attendance records efficiently. This approach eliminates the risk of proxy attendance and ensures a secure, non-transferable, and accurate record-keeping process. Its compact design, low power consumption, and web-based interface make it ideal for schools, colleges, and workplaces aiming for a modern and reliable attendance solution.
Wireless Charging System for Electric Vehicles with Enhanced Efficiency
The Wireless Charging System for Electric Vehicles (EVs) with Enhanced Efficiency is an innovative solution that aims to simplify and modernize EV charging by eliminating the need for physical connectors. This system leverages electromagnetic induction through a copper coil-based transmitter and receiver, providing a safe, efficient, and contactless power transfer method. The system consists of a primary coil connected to a 12V transformer and a buck-boost converter, which regulates and boosts voltage levels for optimal inductive power transmission. On the receiving end, a secondary coil is used along with a buck converter to stabilize the voltage and safely charge the EV battery. A NodeMCU (ESP8266) microcontroller is integrated to monitor voltage, current, and temperature parameters, and to transmit real-time charging data to a web-based dashboard or mobile app. By removing mechanical plug-in systems, this project reduces wear and tear, increases safety in wet or hazardous environments, and offers automated charging capabilities. The use of efficient power electronics ensures minimal energy loss, making it a cost-effective and scalable solution for future smart charging stations and residential EV setups.
Facial Recognition-Based Attendance Management System
The Facial Recognition-Based Attendance Management System is an advanced solution that leverages ESP32-CAM, machine learning, and a Flask-based web server to automate and streamline attendance tracking. The system captures live images of individuals using the ESP32-CAM module and performs real-time face recognition using pre-trained machine learning models. Once a face is identified, the system logs the attendance data with a timestamp into a centralized database, accessible through a Flask-powered web interface. Unlike traditional manual or biometric methods, this system provides a contactless, accurate, and tamper-proof method of recording attendance. The use of machine learning ensures that the system can recognize faces under various lighting and environmental conditions. Flask handles the backend services, including image processing requests, database management, and frontend integration. This project is ideal for educational institutions, workplaces, and secured areas, offering not only operational efficiency but also increased safety through non-contact verification. With features like real-time logging, data visualization, and remote access, the system presents a scalable and cost-effective attendance management solution.
Automated Vision-based Surveillance System to Detect Drowning Incidents in Swimming Pools
The Automated Vision-Based Surveillance System to Detect Drowning Incidents in Swimming Pools is a real-time monitoring solution that utilizes ESP32-CAM, machine learning, and a Flask-based server to enhance safety in aquatic environments. The system is designed to detect early signs of drowning and immediately trigger alerts to prevent fatalities. The ESP32-CAM module continuously captures live video feeds from the swimming pool. These feeds are transmitted to a Flask-powered backend, where machine learning algorithms analyze human motion patterns to identify unusual or erratic behavior that may indicate drowning—such as prolonged immobility or struggling movements. Once such activity is detected, the system sends instant alerts via SMS, email, or buzzer to pool authorities or guardians, allowing for rapid intervention. The system offers a low-cost, scalable, and non-intrusive alternative to manual monitoring. It works effectively under various lighting conditions and does not require the individual to wear any device. This project is especially valuable for residential pools, schools, public swimming facilities, and training centers, providing a crucial layer of automated life-saving surveillance.
RFID-Based Attendance Management System
The RFID-Based Attendance Management System is a smart and efficient solution designed to automate the process of attendance tracking using Radio Frequency Identification (RFID) technology. This system utilizes a NodeMCU (ESP8266) microcontroller integrated with an RFID card reader to scan individual RFID cards assigned to users such as students or employees. When an RFID card is tapped near the reader, the unique ID of the card is read and sent via Wi-Fi to a remote database through the NodeMCU. The system records the date, time, and cardholder identity in real-time, ensuring quick and accurate attendance logging. This data can be accessed through a secure web-based frontend for monitoring, reporting, and analytics. Compared to traditional manual systems, this method is faster, more secure, and tamper-resistant, preventing proxy attendance. The system is ideal for educational institutions, offices, and restricted facilities aiming for a scalable and contactless attendance solution.
Automated Medical Dispenser for Train Passengers
Access to essential medications during train journeys is often limited, especially during emergencies or in remote travel segments. To address this issue, this project introduces an “Automated Medical Dispenser for Train Passengers”, a compact and intelligent vending machine-based system designed to dispense over-the-counter medicines automatically during travel. The core of the system is powered by an Arduino Uno, which controls various components, including a BLDC motor, spring-loaded dispensing mechanism, and an ultrasonic sensor for inventory and object detection. When a passenger selects a medicine via a user interface (integrated with a Flask-based web application or onboard interface), the Arduino activates the corresponding relay to drive the motor and rotate the spring rod, releasing one unit of the selected medication into the output tray. The ultrasonic sensor checks for item availability and ensures proper dispensing, preventing errors or double delivery. A Flask server running on a local or cloud-connected computer handles inventory updates, user interaction, medicine information display, and can also be linked to a payment gateway or passenger identity verification system in advanced versions. This automated system ensures touch-free, quick, and reliable access to basic medical supplies, especially useful during long-distance travel. The integration of Arduino-based control logic with web-based inventory and status monitoring makes it a scalable and effective solution for smart railway environments.
Intelligent Home Automation System for Energy Efficiency and Safety
As the demand for smarter, safer, and more energy-efficient living spaces increases, integrating intelligent automation systems into homes has become a necessity. This project presents an Intelligent Home Automation System for Energy Efficiency and Safety, designed to control and monitor household appliances through a centralized embedded platform. The system is built using the NodeMCU (ESP8266) microcontroller, which offers Wi-Fi connectivity for real-time control and monitoring via a web or mobile interface. The automation setup includes relay-controlled outputs connected to devices such as bulbs and DC motors, enabling smart switching of lights, fans, or curtains. Touch-sensitive buttons are used to manually control appliances, enhancing user convenience while maintaining modern aesthetics. An IR sensor is deployed to detect human presence in specific rooms or areas, allowing the system to automatically turn off lights or fans in unoccupied spaces, thus optimizing energy usage. The hardware components are mounted on a custom-designed PCB board, ensuring a clean and organized layout for safe and reliable operation. The system not only reduces electricity consumption through smart scheduling and presence-based control but also improves household safety by avoiding manual intervention with high-voltage switches. All sensor data and appliance states are transmitted to the cloud or local server using the NodeMCU, allowing users to monitor and control their home remotely. This home automation framework demonstrates a practical, scalable, and cost-effective approach to making modern homes more intelligent, efficient, and secure, particularly suited for urban households, elderly care, and smart city implementations.
Smart Shopping Cart System with Automatic Billing and Inventory Management
Traditional retail shopping often results in long queues at billing counters and inefficient inventory tracking, leading to customer dissatisfaction and operational delays. To address these challenges, this project presents a “Smart Shopping Cart System with Automatic Billing and Inventory Management”, which automates the checkout process and updates inventory in real-time using RFID technology and IoT integration. The core of the system is built using a NodeMCU (ESP8266) microcontroller connected to an RFID card reader. Each product in the store is tagged with an RFID card containing a unique product ID. As the customer places items into the shopping basket (mounted on wheels for mobility), the RFID reader scans the tags, identifies the products, and automatically adds their details (such as price and name) to the billing system. The NodeMCU communicates wirelessly with a Flask-based web server, which maintains a database of products and generates a real-time bill. It also updates the inventory database by decrementing stock levels as products are added to the cart. Once shopping is complete, the customer can view and confirm the final bill on their mobile or at a smart kiosk. This system eliminates manual scanning, reduces billing time, prevents theft, and ensures accurate stock tracking. The integration of RFID and IoT in a mobile basket setup provides a seamless and efficient shopping experience, contributing to the development of smart retail environments.
Comprehensive Vehicle Security and Compliance System with Driver Sobriety Monitoring
The increasing need for secure and intelligent transportation systems has led to the development of a comprehensive vehicle monitoring solution that ensures both driver identity verification and safety compliance. This project, titled "Comprehensive Vehicle Security and Compliance System with Driver Sobriety Monitoring," presents a smart embedded system that integrates biometric authentication, alcohol detection, motor control, and video surveillance to enhance vehicle security and operational safety. The system is developed using an ESP32 microcontroller, which serves as the central controller for all components. Upon entering the vehicle, the driver is required to verify their identity using a fingerprint sensor. If the fingerprint is authenticated, the system proceeds to check for the driver’s sobriety using an alcohol sensor. Only if no alcohol is detected will the system activate a relay to power the vehicle’s motor, allowing movement. If alcohol is present, the vehicle remains immobilized, and a warning message is displayed on a 16x2 LCD screen. To further reinforce security and monitoring, the ESP32-CAM module captures the driver’s image during the authentication process. This image, along with relevant data such as fingerprint ID and alcohol status, is sent to a Flask-based web server for remote logging and monitoring. By combining biometric access, intoxication detection, and real-time video capture, this system offers a highly effective and integrated solution for preventing unauthorized access, reducing drunk driving incidents, and enabling remote supervision of vehicles. Its design is cost-effective, scalable, and well-suited for implementation in personal vehicles, public transportation, and fleet management systems in smart cities.
Atm face regonistion transcation
Ensuring secure and contactless ATM transations has become a major concern in today’s digital banking landscape. This project proposes a multi-factor authentication system titled “ATM Face Recognition Transaction System”, which integrates facial recognition using ESP32-CAM with RFID-based user identification and a Flask-based machine learning backend for real-time verification and secure transaction authorization. The system initiates when a user presents an RFID card to an RFID reader, which reads the unique card ID and verifies it as the first layer of authentication. Upon successful card detection, the ESP32-CAM module captures a live image of the user's face and transmits it to a Python Flask server, where a trained machine learning face recognition model compares it against a pre-registered dataset. If the face matches the card owner, the transaction is authorized, and the user is granted access to the ATM functions. If the face does not match or is unrecognized, the transaction is denied, ensuring that stolen or cloned RFID cards cannot be used fraudulently. The system operates over Wi-Fi, enabling seamless integration between the ESP32-CAM, Flask web server, and database of registered users. This dual-factor authentication approach—RFID card + facial recognition—provides an enhanced layer of security, reduces physical contact, and paves the way for future touchless ATM interfaces. This project demonstrates a cost-effective, scalable, and secure ATM authentication system suitable for smart banking applications, combining IoT, embedded systems, and AI-powered face recognition.