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Node MCU-Based Smart Energy Meter with GSM Communication
The NodeMCU-Based Smart Energy Meter with GSM Communication is an IoT-enabled system designed to modernize traditional energy monitoring by enabling real-time energy usage tracking and remote communication. This system utilizes a NodeMCU (ESP8266) microcontroller to read power consumption data and transmit it via GSM (SIM900A module) to a central server or directly to the consumer's mobile device. The energy meter measures parameters such as voltage, current, and power consumption using sensors like a current transformer (CT) or energy monitoring module. The NodeMCU processes this data and periodically sends readings to authorized users through SMS alerts or uploads the data to an online database or cloud server. This enables users to monitor their energy usage remotely, receive billing information, and get alerts for overconsumption or abnormal usage. This smart metering system eliminates the need for manual meter reading, reduces billing errors, and enhances transparency. It is particularly beneficial for residential users, industries, and utility providers, offering a low-cost, scalable, and energy-efficient solution for modern power monitoring and management.
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.
Solar-Powered Electric Vehicle Charging Station with IoT Integration
The Solar-Powered Electric Vehicle Charging Station with IoT Integration is a sustainable and smart energy solution aimed at promoting clean energy use for electric vehicle (EV) charging. This system harnesses solar energy through a solar panel, converts it using a solar charge controller (converter), and stores the energy in a battery pack composed of three rechargeable units. A buck-boost converter is used to regulate the voltage output to meet the charging requirements of EV batteries efficiently. The system is controlled and monitored by a NodeMCU (ESP8266) microcontroller, which provides IoT connectivity. Real-time data such as charging voltage, current, battery status, and solar power generation is collected and transmitted to a cloud platform or frontend dashboard. This enables users to remotely track charging status, system efficiency, and energy usage trends through a web or mobile interface. This project addresses key challenges in EV infrastructure by providing an eco-friendly, off-grid, and intelligent charging solution. It is ideal for deployment in remote areas, parking lots, and residential buildings. The integration of IoT ensures efficient energy management, preventive maintenance, and user transparency, supporting the broader goal of green mobility and smart city development.
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.
Atm face regonistion transcation
Ensuring secure and contactless ATM transactions has become a major concern in todayâs digital banking landscape. This project proposes a multi-factor authentication system titled âATM Face Recognition Transaction Systemâ, 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 approachâRFID card + facial recognitionâprovides 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.
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 (CHâ), 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.
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.
Comprehensive Biomedical Monitoring System with Triple Parameter Surveillance
The advancement of IoT-based biomedical systems has paved the way for real-time remote patient monitoring, offering crucial support in medical emergencies and rural healthcare. This project proposes a Comprehensive Biomedical Monitoring System with Triple Parameter Surveillance, which continuously monitors the patient's blood oxygen level (SpOâ), body temperature, and real-time location, and integrates an automated alert mechanism through GSM communication. The system is built using an ESP32 microcontroller, which interfaces with a MAX30102 SpOâ sensor, a temperature sensor, and a GPS module to collect vital physiological and location data. These parameters are wirelessly transmitted to a Flask-based web server, where the data is displayed on a live dashboard and analyzed using a machine learning model to detect abnormal health conditions such as hypoxia, fever, or sudden changes in location. If any health parameter crosses a predefined threshold, the system immediately activates the GSM module (SIM800L/SIM900) to send an emergency SMS alert containing the patientâs vital data and GPS location to medical responders or family members. This ensures quick response even in areas with poor internet connectivity. By integrating biomedical sensors, intelligent decision-making via machine learning, and dual communication through Wi-Fi and GSM, the system offers a reliable, portable, and scalable healthcare monitoring solution. It is particularly suited for remote patient care, mobile health units, elderly monitoring, and post-operative recovery tracking, where timely alerts and accurate health data are essential for saving lives.
Atm transcation with otp sending with card detection
In the modern era of digital banking, securing ATM transactions against unauthorized access has become increasingly critical. This project presents an innovative solution titled âATM Transaction with OTP Verification and Card Detectionâ, which combines RFID-based user authentication with One-Time Password (OTP) verification to enhance ATM security. The system uses an RFID card reader and RFID card for initial user identification. Once the user presents a valid card, the system reads the card data via the NodeMCU (ESP8266) microcontroller, which communicates wirelessly with a Python Flask-based web server. Upon successful card detection, the server generates a secure OTP (One-Time Password) and sends it to the registered mobile number or email ID associated with the RFID card. The user is then prompted to enter the OTP at the interface (physical keypad or web-based form). If the OTP matches the server record and is entered within the valid time window, the transaction is approved and access to the ATM functionality is granted. If the OTP is invalid or expired, access is denied. This dual-layer authentication system ensures that even if the RFID card is lost or stolen, unauthorized users cannot perform transactions without access to the legitimate OTP. The entire communication between the NodeMCU and the server is handled over Wi-Fi, ensuring real-time verification and secure data transfer. This project demonstrates a cost-effective, secure, and scalable solution for ATM transaction authentication, blending IoT and web technologies to enhance safety in financial operations.