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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.
ac smart energy meter
Conventional energy meters lack the capability for real-time monitoring and remote analytics, limiting users' ability to manage electricity consumption effectively. This project introduces an IoT-based AC Smart Energy Meter that measures and tracks electrical energy usage in real time and provides remote accessibility through a web interface. The system is powered by an ESP32 microcontroller, which interfaces with an AC voltage sensor and an AC current sensor to calculate power consumption accurately. A variable resistor allows simulation of varying load conditions, while a bulb and bulb holder act as the output load. The system processes voltage and current readings to compute energy usage, and all data is sent to a Flask-based web dashboard, where users can view live energy metrics, usage trends, and historical logs. This solution promotes efficient energy management, enhances user awareness, and supports smart home or industrial applications with real-time decision-making capabilities.
underground water leakage detection
Undetected underground water leakages can lead to significant water wastage, soil erosion, and structural damage, especially in agricultural, industrial, and municipal water distribution systems. This project presents an IoT-based Underground Water Leakage Detection System that provides real-time monitoring and alerting to prevent such losses. The system is developed using an ESP32 microcontroller, which interfaces with a water flow sensor to measure and track the volume of water passing through a pipeline. A soil moisture sensor is embedded underground near the tube to detect unexpected moisture levels indicative of leaks. A water pump is included to simulate active water flow in the setup, allowing for controlled testing and system validation. All data collected is transmitted to a Flask-based IoT dashboard, which visualizes flow rates, soil conditions, and generates alerts if discrepancies suggest leakage. This intelligent solution offers a scalable and low-cost approach to improve the efficiency and sustainability of underground water systems.
iv and trips controller
Intravenous (IV) fluid administration is a critical process in medical care that requires careful control to prevent under-infusion, over-infusion, or fluid imbalances. Manual monitoring can be error-prone and labor-intensive, especially in high-demand healthcare environments. This project proposes an IoT-enabled IV and Drip Controller System designed for automated flow regulation and real-time patient monitoring. Built around the ESP32 microcontroller, the system integrates a solenoid valve to precisely control IV flow rate. Patient vitals such as body temperature and oxygen saturation (SpO₂) are continuously monitored using respective sensors. Based on these real-time parameters, the system dynamically adjusts the drip rate and ensures safety thresholds are maintained. All data is transmitted to a Flask-based web interface for remote supervision by healthcare professionals. A 3D-printed model is used to simulate the IV stand and fluid control assembly for demonstration purposes. The system enhances patient care, reduces nurse workload, and provides a reliable, automated approach to IV fluid management in both clinical and home care settings.
animal detect and car slow down system
Animal crossings on roads, especially in rural and forest-adjacent areas, contribute significantly to road accidents, posing a risk to both human life and wildlife. This project presents an IoT and Machine Learning-based Animal Detection and Car Slow Down System that detects animals in vehicle pathways and automatically initiates deceleration to prevent collisions. The system is powered by a NodeMCU and integrates an ESP32-CAM module for real-time video surveillance. Captured frames are processed via a Flask-based server using machine learning algorithms trained to identify animals. Upon detection, the system communicates with a motor driver to reduce the speed of a DC motor-powered vehicle (simulated using wheels), thereby imitating automatic braking. An ultrasonic sensor enhances environmental awareness by measuring proximity to obstacles, enabling layered safety logic. This intelligent vehicle safety prototype demonstrates an effective solution for wildlife protection and road safety using affordable hardware and edge computing.
IoT-Based Multi-Hazard Detection System Using Sensor Fusion and Real-Time Weather Data (earthquake, landslide, flood, cyclone in sea)
Natural disasters such as earthquakes, floods, cyclones, and mudslides pose significant threats to human life and infrastructure. Early detection and real-time monitoring of these events are essential to reduce damage and ensure timely alerts. This project presents an IoT-based Multi-Hazard Detection and Monitoring System that integrates both hardware sensors and online weather data to detect and track potential natural hazards. The system employs an ESP32 microcontroller connected to multiple sensors: a vibration sensor for earthquake detection, a water level sensor for flood monitoring, and a soil moisture sensor to detect landslide-prone conditions. Additionally, real-time weather data—including wind speed, atmospheric pressure, and storm updates—is fetched using a public API (such as OpenWeatherMap) to monitor cyclone risks, especially in coastal regions. Sensor data is transmitted to a central Python Flask server, which stores it in an SQLite database and provides a dashboard for live monitoring. The system can analyze sensor readings alongside online weather reports to trigger alerts when critical thresholds are exceeded. This integrated approach ensures proactive disaster risk management through early warning mechanisms, helping authorities or individuals take timely preventive action. The project aims to deliver a cost-effective, scalable, and real-time hazard monitoring solution, especially useful for rural or remote areas with limited access to traditional early warning systems.
oxygen tank controll
Precise regulation of oxygen supply is critical in medical treatments, especially for patients with respiratory conditions. Manual control of oxygen tanks often leads to inefficiencies or delayed responses to a patient's changing needs. This project introduces an IoT and Machine Learning-based Oxygen Tank Control System that automatically adjusts oxygen flow based on real-time physiological data. Built using an ESP32 microcontroller, the system integrates an SpO₂ sensor to continuously monitor a patient's blood oxygen saturation levels. When a deviation from the normal range is detected, a servo motor adjusts the oxygen flow from a connected tank or container accordingly. All data is transmitted to a Flask-based web interface, allowing caregivers to monitor vitals and tank status remotely. Additionally, a machine learning model aids in predicting oxygen needs based on patterns in SpO₂ readings, enabling proactive oxygen regulation. This system enhances patient safety, reduces dependency on manual supervision, and supports intelligent health care management in both clinical and remote settings.
piezo electric shoes
Traditional methods for measuring cholesterol and hemoglobin levels are invasive, requiring blood samples and laboratory processing, which may be inconvenient for continuous or remote health monitoring. This project proposes a non-invasive health monitoring system that estimates cholesterol and hemoglobin levels using Photoplethysmography (PPG) signals, combined with machine learning (ML) algorithms. The system is built using a NodeMCU microcontroller and integrates an SpO₂ sensor and heart rate sensor to capture real-time PPG waveforms. These signals are analyzed using a Flask-based server, where ML models trained on clinical datasets predict the user’s cholesterol and hemoglobin levels with reasonable accuracy. The results are displayed on a secure web dashboard for remote health monitoring. This contactless solution supports continuous assessment without needles or lab intervention, making it ideal for home use, remote clinics, and chronic care settings. It provides an accessible, low-cost alternative for routine health diagnostics while promoting user comfort and reducing healthcare burdens.
smart agri with wild life and leaf disease and solinoid
Modern agriculture demands smart, scalable, and autonomous solutions to tackle challenges such as crop disease, wildlife intrusion, and inefficient irrigation. This project presents a Smart Agriculture System integrating IoT and Machine Learning (ML) to monitor environmental conditions, detect leaf diseases, and deter wildlife in real time. At the core, an ESP32 microcontroller collects data from a temperature sensor, soil moisture sensor, water level sensor, and other components. A solenoid valve, controlled through a relay, along with a water pump, automates irrigation based on soil conditions. For plant health, an ESP32-CAM, interfaced via a camera converter, captures leaf images which are processed using ML algorithms through a Flask-based web server to identify signs of disease. Wildlife detection is achieved through real-time video analysis, triggering a buzzer, LED light, or servo motor-based deterrent system to scare animals away. A toy-based setup with a container simulates the real-time farm environment, while an external adaptor powers the system for stability. The dashboard offers real-time monitoring, historical logs, and remote actuation features. This solution promotes precision agriculture, resource optimization, and crop protection through intelligent automation.
bandage wound monitoring system
Wound management is a critical aspect of post-surgical and chronic care, where delays in detecting infections or abnormal healing can lead to severe complications. This project proposes a Bandage-Integrated Wound Monitoring System that utilizes IoT and Machine Learning (ML) to provide continuous, non-invasive monitoring of wound conditions. The system is built using a NodeMCU microcontroller, integrated with a temperature probe, soil moisture sensor (used here to detect wound exudate levels), and a SpO₂ sensor to assess localized oxygenation—an indicator of tissue health. An ESP32-CAM module captures periodic images of the wound site, which are processed through machine learning models hosted on a Flask-based server to detect signs of infection, inflammation, or abnormal tissue behavior. All physiological data and image analytics are displayed on a secure web dashboard for remote monitoring by healthcare professionals. This system enables early intervention, personalized wound care, and reduces the burden of in-person monitoring, making it ideal for both hospital and home-based recovery settings.