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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.
Non invasive Measurement of cholesterol and haemoglobin using ppg
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 SpOâ 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 userâs 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.
Waste to Energy: Arduino-Based Thermoelectric Energy Harvesting Using TEG Modules
With the growing global emphasis on sustainable energy solutions, the conversion of waste heat into usable electrical energy has emerged as a promising strategy. This project presents a waste-to-energy harvesting system utilizing Thermoelectric Generator (TEG) modules to convert heat energy into electricity. The system is built around an Arduino Uno microcontroller, which monitors and manages energy generation in real-time. Heat from waste sources is applied to the TEG modules, producing a DC voltage output, which is regulated using a buck-boost converter to maintain consistent voltage levels. An LCD (16x2) display provides live data on the voltage output, supported by a voltmeter for validation. LED indicators are used to demonstrate successful energy output and system status. The system illustrates a practical and scalable approach for micro-energy harvesting from ambient or industrial waste heat, aligning with green energy goals. Its simple design, low cost, and adaptability make it suitable for educational, industrial, and rural applications where waste heat is abundant and underutilized.
Voice Controlled Home Automation
The evolution of smart homes has driven the need for more intuitive and hands-free control systems to enhance user convenience, accessibility, and energy efficiency. This project proposes a Voice Controlled Home Automation System using IoT and Flask, enabling users to operate home appliances through voice commands. At the core of the system is an ESP32 microcontroller integrated with a microphone (mic) module for capturing voice inputs. These inputs are processed through a Flask-based web application, which interprets commands and triggers the corresponding actions. A relay module is used to control devices such as a light bulb and a motor with wheel, simulating home appliances like fans, lights, or curtains. Upon recognizing specific voice commands, the system actuates the appropriate relay switches to turn appliances ON or OFF. The entire setup supports remote control and real-time feedback through an IoT dashboard, offering both automation and user-friendly interaction. This system demonstrates a low-cost, efficient, and scalable approach to smart home management, particularly useful for elderly or differently-abled individuals.
smart street Light
Conventional street lighting systems consume substantial energy and often remain powered regardless of environmental lighting conditions or pedestrian activity, leading to energy wastage. To address this issue, this project proposes an IoT-based Smart Street Light System designed to optimize energy usage by dynamically controlling street lights based on environmental and motion conditions. The system employs a NodeMCU microcontroller to collect data from an array of sensors, including an LDR (Light Dependent Resistor) sensor to detect ambient light levels and IR (Infrared) sensors to detect human or vehicular movement near the light pole. Based on sensor inputs, the system intelligently turns the LED light on or off via a relay module. A stick-mounted setup simulates real-world deployment of the street light pole. When ambient light is sufficient (e.g., during the daytime), the street light remains off, while in low-light conditions, it turns on only if movement is detected, thus conserving energy. This smart lighting solution represents a cost-effective and sustainable advancement in urban infrastructure, promoting intelligent energy management and public safety.
IoT-Based Remote Patient Monitoring System using surrounding weather
The integration of IoT technologies into healthcare has enabled significant advancements in remote patient monitoring, especially for patients requiring continuous observation outside clinical settings. This project presents an IoT-based remote patient monitoring system that not only tracks vital health parameters but also correlates them with surrounding environmental conditions to provide more accurate and context-aware insights. The system is built around a NodeMCU microcontroller, which collects physiological data using temperature and SpOâ sensors. These readings are wirelessly transmitted to a Flask-based cloud platform, where real-time monitoring and analysis are performed. The novelty of the system lies in its capability to incorporate ambient weather conditions into patient monitoring. Environmental factors such as temperature fluctuations can significantly affect patient health, particularly in individuals with respiratory or cardiovascular conditions. By integrating this additional layer of data, the system provides more informed alerts and health predictions. This approach enhances the reliability of remote health surveillance, making it suitable for deployment in home care, rural healthcare, and post-discharge patient management.
Energy Efficient Wireless Communication for IOT Enabled Greenhouses
With the rise of smart agriculture, greenhouses are increasingly adopting IoT technologies to monitor and control environmental conditions for optimal plant growth. However, continuous wireless communication between sensors and cloud platforms can lead to excessive energy consumption, especially in off-grid or solar-powered systems. This project presents an Energy-Efficient Wireless Communication System for IoT-Enabled Greenhouses, incorporating selective data transmission and a Flask-based web server for real-time monitoring and control. The system is developed using a NodeMCU (ESP8266) microcontroller, interfaced with a soil moisture sensor, temperature sensor, and LDR sensor to monitor key environmental parameters such as soil hydration, ambient temperature, and light intensity. Instead of transmitting data continuously, the NodeMCU uses a threshold-based approachâcommunicating with the server only when there is a significant change in sensor values or when readings cross critical limits. This approach reduces power consumption while maintaining accurate environmental insights. Collected data is sent via Wi-Fi to a Flask-based server, which receives, stores, and visualizes the sensor values on a web dashboard. The dashboard allows remote users to track real-time and historical data, and to configure alert thresholds or control strategies. Flask provides a lightweight yet powerful backend framework that supports future expansion, integration with databases, and data-driven decision-making tools. By combining intelligent communication protocols, real-time sensing, and a responsive Flask web interface, the system delivers a low-power, cost-effective, and scalable solution for smart greenhouse environments. This approach not only enhances energy efficiency but also supports sustainable agricultural practices through data-driven automation and remote accessibility.
Integration of IoT with Cloud Computing for Scalable Water Quality Monitoring
The quality of water plays a vital role in ensuring environmental safety, public health, and sustainable development. Traditional methods of water quality testing are often time-consuming, manual, and limited in scope. This project proposes an IoT-based Water Quality Monitoring System integrated with Cloud Computing, offering a scalable, real-time solution for monitoring water sources across diverse environments. The system is built around a NodeMCU (ESP8266) microcontroller, interfaced with water quality sensors capable of measuring key parameters such as pH, turbidity, temperature, and conductivity. These sensors collect data at regular intervals, which is then displayed locally on a 16x2 LCD screen for immediate on-site monitoring. Simultaneously, the data is transmitted via Wi-Fi to a cloud platform, enabling remote access, historical data tracking, and advanced analytics. By leveraging cloud computing, the system offers scalable storage, multi-location monitoring, and real-time dashboards accessible through mobile or web applications. Alerts and trends can be generated for authorities or users when water quality falls below safe thresholds, enabling rapid response and preventive action. This integration of IoT sensing, local display, and cloud-based analytics delivers a low-cost, scalable, and efficient solution for water quality monitoring in urban water supplies, rural reservoirs, fish farms, and industrial effluents. The system promotes better decision-making, environmental sustainability, and health safety through continuous, automated, and intelligent water quality assessment.
Object detection using AI
Object detection is a fundamental task in computer vision with wide-ranging applications in surveillance, automation, and smart environments. This project presents an AI-driven object detection system using an ESP32-CAM module integrated within an IoT and Flask-based architecture. The system captures live video feeds through the ESP32-CAM and transmits image frames to a Flask server for processing. Machine learning models, trained to recognize specific objects, are employed to perform real-time detection and classification. Upon detection, the results are displayed through a web dashboard, enabling remote monitoring and decision-making. The compact design of the ESP32-CAM makes the system highly portable and suitable for embedded smart surveillance, home automation, and access control applications. The use of lightweight ML models ensures efficient on-device inference or low-latency cloud processing, depending on the deployment configuration. This project demonstrates how combining AI, IoT, and Flask technologies can yield a low-cost, intelligent, and scalable solution for real-time object detection in dynamic environments.
Low-Cost IoT Enabled Weather Station
Accurate and localized weather monitoring is crucial for agriculture, disaster preparedness, and smart environmental management. However, commercially available weather stations are often expensive and inaccessible for small-scale applications. This project presents a Low-Cost IoT-Enabled Weather Station that provides real-time monitoring of key atmospheric parameters such as temperature, air pressure, and rainfall using affordable sensors and microcontroller-based communication. The system is built using cost-effective sensors including a temperature sensor for ambient temperature measurement, an air pressure sensor for barometric readings, and a rainwater sensor to detect precipitation levels. These sensors are interfaced with a microcontroller (such as ESP32 or NodeMCU), which collects data and transmits it to an IoT platform via Wi-Fi. The transmitted data is visualized on a web-based dashboard, allowing users to monitor weather conditions remotely in real time. This solution enables users such as farmers, researchers, or local authorities to make data-driven decisions related to irrigation, crop planning, or weather alerts. By combining IoT communication with simple sensing technology, this weather station offers a scalable and energy-efficient solution that can be deployed in rural areas, school campuses, or community gardens, contributing to smart environment monitoring and climate awareness at the grassroots level.
IOT Based Garbage Monitoring System
In modern urban environments, inefficient waste management continues to be a major environmental and logistical challenge. Overflowing garbage bins lead to unsanitary conditions, attract pests, and degrade public hygiene. To address this issue, this project proposes an IoT-Based Garbage Monitoring System that enables real-time monitoring of waste levels in garbage bins using ultrasonic sensing and Wi-Fi-based communication. The system is built using a NodeMCU (ESP8266) microcontroller connected to an ultrasonic sensor, which is placed inside the garbage bin to measure the fill level by calculating the distance between the sensor and the garbage surface. When the bin reaches a pre-defined threshold (e.g., 80â90% full), the system activates an LED indicator to signal that the bin needs to be emptied. Simultaneously, the NodeMCU sends the fill level data over Wi-Fi to a centralized IoT server or web dashboard, allowing municipal authorities or private waste collection agencies to monitor multiple bins remotely in real-time. The system is designed using minimal hardware, including the ultrasonic sensor, LED, NodeMCU, wiring, and a compact PCB board to ensure low cost, ease of installation, and scalability. This setup can be deployed in public areas such as streets, parks, schools, and marketplaces to automate waste collection scheduling and reduce unnecessary fuel consumption from routine garbage truck rounds. By combining IoT and sensor technology, this smart garbage monitoring system provides a sustainable and efficient solution to urban waste management, helping cities transition toward cleaner and more organized environments.
Smart Agricultural Monitoring System Using Color Sensor, Temperature Sensor, Soil Moisture Sensor, and Node MCU
The Smart Agricultural Monitoring System is an intelligent IoT-based solution designed to enhance crop monitoring and management using a combination of sensor data, machine learning, and a Flask-based web interface. The system integrates an ESP32 microcontroller with a color sensor, temperature sensor, and soil moisture sensor to collect vital environmental and crop condition data in real-time. The color sensor analyzes plant leaf color to detect signs of disease or nutrient deficiency. The temperature sensor records ambient environmental conditions, and the soil moisture sensor assesses water content in the soil to help optimize irrigation schedules. These parameters are sent via ESP32 to a Flask-powered backend server, where a machine learning model analyzes historical and real-time data to predict potential crop health issues and suggest proactive measures. Through the Flask-based frontend dashboard, farmers or users can visualize live data, receive intelligent recommendations, and be alerted in case of abnormal patterns. The ML model continuously improves with more data, ensuring increasingly accurate predictions and smarter farming decisions. This system promotes precision agriculture by automating monitoring, optimizing resource usage, and improving yields. It is cost-effective, scalable, and user-friendly, making it ideal for smart farms, research fields, and greenhouse applications.