Volume 7 Issue 1 2026

In recent years, increasing air pollution from vehicular emissions has become a major environmental problem to the society. To address this issue, an IOT-based vehicle emission monitoring system is proposed to continuously track and analyze the exhaust gas levels emitted by vehicles. The system utilizes gas sensors such as MQ-135, MQ- 7, and MQ-2 to detect harmful gases like carbon monoxide (CO), carbon dioxide (CO₂), and hydrocarbons. These sensors are connected with ESP8266 microcontroller and for real-time data transmission to a cloud server. The collected data can be viewed through a web or mobile dashboard, allowing authorities and users to monitor vehicle pollution status effectively. When emission levels go over the permissible limits, alerts or notifications are generated, enabling timely maintenance actions. This system promotes environmental awareness, supports government pollution control policies, and contributes to sustainable smart city development by ensuring cleaner and safer transportation

Our project presents the design and implementation of a Smart Helmet system aimed at significantly enhancing biker safety through real-time monitoring and automated emergency alerting. The integrated system utilizes an MQ-6 gas sensor for proactive alcohol detection, preventing ignition if the rider's breath alcohol concentration (BAC) exceeds a safe threshold. For crash events, an impact/vibration sensor rapidly detects potential accidents. Upon detection of a hazardous event (either high BAC or an accident), a GSM800L module is employed to ensure timely communication. This module automatically sends an SMS alert containing the critical event data and location information to a set of predefined emergency contacts or an emergency medical service. The system is designed to provide immediate and critical information, thereby drastically reducing response times for medical assistance and potentially mitigating severe injury or fatality. This work offers a practical and cost-effective solution for improving road safety for motorcyclists.

In recent years, the integration of Internet of Things (IoT) technology into agriculture has transformed traditional farming practices into smart and efficient systems. This project, titled “Smart Agriculture Fencing Using IoT,” aims to develop an intelligent fencing system that monitors and prevents animal intrusion in agricultural fields. The system employs an ESP8266 Wi-Fi module as the central controller, interfaced with ultrasonic sensors and a camera to detect and identify approaching animals. The captured images are processed using Convolutional Neural Network (CNN) algorithms for accurate classification of animals such as elephants, cows, and sheep.Upon detection, the system triggers an alert mechanism through a buzzer and displays real-time information on an LCD screen. Simultaneously, the system captures and transmits images to the farmer via email alerts using the SMTP protocol, ensuring timely action. A database is maintained for animal identification and monitoring, enabling intelligent decision-making. The proposed system is powered through a regulated power supply and offers remote monitoring through IoT connectivity.This smart fencing solution enhances farm security, reduces crop damage, and promotes efficient wildlife management, ultimately contributing to sustainable agricultural practices and improved farmer productivity.

Breast cancer is one of the most common fatal diseases affecting women whose incidence rate is increasing worldwide. Early detection is the only approach to increase the survival rate because the stage of cancer at the time of detection determines how successfully it may be treated. Recent technological improvements in early screening techniques have decreased the death rate. Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization- crow search algorithm with multilayer perceptron system is compared to multilayer perceptron-based algorithms of enhanced and adaptive genetic algorithm, teaching learning- based whale optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, and grasshopper optimization algorithm. From the results obtained, the proposed grasshopper optimization-crow search algorithm with the multilayer perceptron method outperforms the comparative models in terms of classification accuracy (97.1%), sensitivity (98%), and specificity (95.4%) for the mammographic image analysis society dataset.

Anemia, a condition characterized by reduced hemoglobin levels, continues to be a major public health issue, especially in rural and resource-limited areas. Conventional detection techniques rely on invasive blood sampling and laboratory testing, which are often inaccessible in such regions. This paper proposes a non-invasive, IoT-enabled anemia diagnosis system that utilizes photoplethysmography (PPG) signals obtained from the MAX30100 sensor integrated with an ESP32-CAM microcontroller. The system captures red and infrared light absorption patterns from the fingertip to estimate hemoglobin concentration. Extracted time- and frequency domain features are processed using machine learning algorithms to classify anemia severity levels accurately. The ESP32CAM provides wireless connectivity for real-time data transmission, visualization, and alert notifications via cloud and mobile platforms. Key hardware components include the MAX30100 pulse oximeter sensor, ESP32CAM module, OLED display, and regulated power supply. Experimental results demonstrate that the proposed system achieves reliable hemoglobin estimation and efficient IoT based remote monitoring. This work presents a low-cost, portable, and intelligent healthcare solution capable of continuous anemia screening and early detection in underserved populations.

The In the rapidly evolving landscape of industrial automation and Industry 4.0, the deployment of cost-effective wireless sensors for environmental and machine condition monitoring has become essential. These sensors enable continuous data collection, which is critical for process optimization, fault detection, and predictive maintenance. However, the widespread use of low-cost sensors, like the DHT11 for temperature and humidity, is often hindered by their inherent inaccuracies and response variability. To bridge this gap, this paper presents a novel on-machine calibration framework where machine learning (ML) models are embedded directly into ESP32 microcontroller-based sensor nodes. These models continuously adjust and correct sensor readings by comparing them with benchmark reference sensors (BMP280) under real operating conditions of CNC machining. The implemented calibration demonstrates marked improvements in measurement accuracy and reliability, achieving a correlation coefficient larger than 0.9 with reference sensors and reducing error margins substantially. This framework provides an easily deployable, scalable, and autonomous solution poised to enhance sensor data fidelity in manufacturing environments, empowering small and medium enterprises (SMEs) to leverage Industry 4.0 technologies effective.

The Automated IV Fluid Tracking and Alert Notification System is an IoT-based solution designed to monitor intravenous (IV) infusions in real time. Manual supervision of IV therapy often leads to complications due to human error or delay. This project uses an ESP8266 NodeMCU microcontroller integrated with an Infrared (IR) sensor to detect and count each drop passing through the drip chamber. The system determines three key conditions — Normal Flow, No Flow, and Completed Infusion — based on drop count and flow rate. When a No Flow condition occurs, the buzzer produces three short beeps, and on completion, a long beep is generated. Simultaneously, the NodeMCU sends updates to a Flask-based web dashboard through WiFi, displaying real-time IV status with colorcoded indicators for quick nurse response. Testing achieved over 95% accuracy in detecting drops and reliable alert performance for both flow interruption and completion states. This low-cost, compact system enhances patient safety, reduces manual monitoring, and improves nurse efficiency, especially in budget-limited hospitals and home healthcare setups. Future improvements include mobile notifications, integration with hospital databases, and advanced sensors for predictive flow control.

Electricity theft remains a critical challenge for power distribution companies, undermining grid reliability and causing significant economic losses worldwide. This paper introduces the design and evaluation of a compact, real-time energy theft detection system, leveraging the capabilities of an ESP32 microcontroller and dual ACS712 current sensors. The proposed approach continuously analyzes current flows on both supply and consumer lines, automatically triggering alerts and disconnecting service when unauthorized usage is detected. Unlike conventional manual inspections or high-cost metering solutions, our system emphasizes affordability, local alerting, and rapid response, making it well-suited for deployment in small-scale or resource-constrained environments. Experimental evaluation demonstrates effective detection with a low rate of false positives, and the architecture provides a pathway for future integration with cloud-based reporting or advanced analytics. This work presents a practical step towards smarter, more secure grids by enabling accessible and automated anti-theft measures.

Water scarcity and inefficient irrigation remain major challenges in sustainable agriculture and home gardening. This paper presents Green Guard, an intelligent IoT-based irrigation and plant health monitoring system that integrates real-time embedded control with cloud-based decision support. The system employs an ESP32 microcontroller running Free RTOS to concurrently execute tasks for soil-moisture sensing, environmental monitoring, weather forecasting via Open Weather Map API, and MQTT-based communication with a remote dashboard. Time synchronization using NTP enables precise scheduled watering routines at configurable times (morning, midday, evening). The control logic evaluates both weather forecasts and local soil moisture levels before irrigation to minimize unnecessary water usage. Data and system events are published to a cloud MQTT broker for visualization and manual override. The prototype demonstrated reliable multitasking, accurate forecast-based skip functionality, and robust network communication. Green Guard effectively combines real-time deterministic control with IoT connectivity, offering a scalable and energy-efficient solution for modern precision irrigation.

This project presents the design and implementation of a Bluetooth-enabled smart phone charging controller using an ESP32 microcontroller and a relay-based power switching mechanism. The system intelligently monitors the smartphone’s battery status in real time and controls the charging process to automatically disconnect the charger once the user-defined target charge percentage is achieved. This approach effectively prevents overcharging, reduces unnecessary power consumption, and prolongs the overall battery lifespan. The ESP32 serves as the core processing unit, equipped with Bluetooth Serial (SPP) communication to interface with a custom mobile application developed using MIT App Inventor. The app continuously transmits two key parameters; they are the current battery level and the target charge limit to the ESP32. These values are stored in the ESP32’s non-volatile flash memory using the Preferences library, ensuring data retention even after power loss or device restart. A relay module integrated with the ESP32 regulates the 5V line of the USB charging cable, enabling or disabling power delivery to the smartphone as required. To enhance user interaction, LED indicators and a buzzer provide clear visual and auditory feedback for charging, disconnection, and fault conditions. The proposed system’s logic incorporates safeguards for scenarios such as Bluetooth disconnection or phone shutdown, where the controller applies configurable rules such as treating the battery as fully discharged or maintaining the last known value. The device operates autonomously, requiring minimal user intervention once the desired charging threshold is set. Overall, the proposed prototype demonstrates a low cost, energy efficient, and reliable IoT-based solution for intelligent charging control. It is particularly suitable for everyday smartphone users seeking to extend battery life, reduce energy waste, and improve charging safety through an embedded automation approach. The project also serves as an excellent educational model for understanding the integration of microcontrollers, Bluetooth communication, and real-time control systems within the Internet of Things (IoT) ecosystem.

The BLINKIT TIME project presents a compact, energy-efficient, and visually engaging wearable LED-based binary clock that displays time in Binary Coded Decimal (BCD) format using sequential LED blinking patterns. The system is built around the ESP32-C3-WROOM-02-N4 microcontroller, which integrates Bluetooth Low Energy (BLE) for seamless wireless synchronization with a custom web application. This app parses accurate real-time data from an online source and transmits it to the device, eliminating the need for a traditional Real-Time Clock (RTC) module. Upon reception, the ESP32-C3 processes the time data and converts it into BCD form to control the LED matrix, where each LED corresponds to a specific binary digit representing hours and minutes. A tactile button enables switching between hour and minute modes, offering interactive functionality within a minimalist interface. The project emphasizes low-power design, compact PCB layout, and modular firmware, demonstrating how modern IoT technologies can redefine classic timekeeping systems. The proposed wearable not only serves as a unique digital clock but also highlights the convergence of IoT, embedded electronics, and human-centric design, paving the way for future smart wearable innovations featuring Wi-Fi synchronization, mobile app control, OLED displays, and power optimization techniques.

The IoT-Based Coal Mine Safetyand Health Monitoring System is designed to enhance the safety and well-being of coal miners by providing real-time monitoring of environmental and health parameters inside mining areas. The system integrates various sensors such as the DHT11 for temperature and humidity, MQ135 for detecting hazardous gases like methane and carbon monoxide, and a pulse sensor for tracking the miner’s heart rate. These sensors are interfaced with the ESP8266 Wi-Fi module, which processes the data and transmits it to the Thing Speak cloud platform for remote visualization and analysis. In case of abnormal conditions, a buzzer and LCD provide immediate alerts to ensure timely response and preventive action. By combining IoT technology with smart sensing and cloud connectivity, the system enables continuous safety supervision, reduces human errors, and minimizes the risks associated with coal mining operations, offering a reliable, efficient, and cost-effective solution for miner safety management.

The increasing frequency of accidents and fatalities near cliff edges highlights the urgent need for low cost, intelligent safety monitoring systems. Inspired by recent advances in IoT based environmental monitoring, edge computing, and human detection technologies, this research presents an Arduino powered Human Presence Monitoring and Alert System for Cliff Edges. The proposed system employs the Arduino microcontroller to trigger and process data from lowcost sensing platforms such as Passive Infrared (PIR) and ultrasonic distance sensors, which are used to detect human motion and measure proximity to hazardous zones. When a human is detected near the edge and the distance falls below a defined safety threshold, the Arduino triggers an immediate alert through a buzzer and LED indicator. Simultaneously, the system utilizes the ESP8266 Wi-Fi module to send SMS notifications or security alerts to remote personnel or control rooms via IoT integration. This ensures real-time communication and rapid response during potential danger events. The use of lightweight algorithms and decentralized edge processing minimizes latency and reduces reliance on external servers, making the system ideal for deployment in remote or coastal areas with limited infrastructure. Experimental validation confirms that the system reliably detects human presence near cliff boundaries, classifies risk levels, and provides timely alerts both locally and remotely. The proposed design demonstrates a scalable, cost-effective, and efficient solution for enhancing public safety, environmental monitoring, and accident prevention in high-risk terrains.

Defense surveillance demands continuous monitoring and rapid response, often placing soldiers at high risk due to hostile environments, fatigue, and unpredictable threats. To mitigate these challenges, an autonomous radar-guided patrolling vehicle has been designed to support secure and intelligent monitoring in military zones. The system functions independently without human intervention and enables real-time situational awareness through IoT- based communication. Radar/ultrasonic sensors are utilized to scan the surroundings, detect obstacles, and identify potential intrusions, while an ESP8266/ESP32 microcontroller analyzes sensor data and executes navigation decisions. Motor control is achieved using an L298 driver to ensure stable and precise vehicle movement. This autonomous patrolling platform enhances surveillance efficiency, minimizes human exposure to danger, and provides a reliable solution for continuous defense-grade monitoring.

This paper presents the design and implementation of an IoT-based Anti-Theft Mat System utilizing a microcontroller, piezoelectric sensor, GSM 800L module, LCD and buzzer to provide real-time home security and automated intrusion detection. The proposed system detects pressure variations when an individual steps on the mat and sends the corresponding signal to the microcontroller for processing. Based on this input, the system triggers an alert through the GSM module, notifying the owner via call or message. The owner can verify the situation using CCTV surveillance and decide to accept or reject the alert. The buzzer is activated and when the owner rejects; if recognized as known person, the servo motor rotates by 180° to unlock the door automatically. The system offers a low-cost, reliable, and efficient security solution suitable for homes and offices. Experimental results confirm effective performance with high accuracy and fast response time, demonstrating the system’s potential for smart home automation and IoT-based safety applications.

The Smart Gas Leakage Detection System based on IoT is designed to detect and prevent hazardous gas leaks in residential and industrial areas to ensure safety and prevent accidents. This system continuously monitors the concentration of combustible gases such as LPG or methane using an MQ- series gas sensor. When the gas concentration exceeds the predefined safety threshold, the sensor sends a signal to a microcontroller (Arduino Uno or NodeMCU). The controller then triggers an alarm through a buzzer and automatically turns off the gas supply using a servo motor connected to the regulator. Simultaneously, the system transmits real-time alerts to the user’s smartphone via the Internet of Things (IoT) using Wi-Fi connectivity, such as through the Blynk or ThingSpeak platform. This enables remote monitoring and quick response, even when the user is away from home. The system is powered by an SMPS adapter for stable operation. The proposed IoT-based design is low- cost, reliable, and highly efficient compared to traditional gas detection systems. It ensures not only early detection but also preventive action, minimizing fire risks and property damage. Thus, this project provides a smart, automated, and safe solution for modern gas leakage management.

This paper presents a portable and intelligent digital stethoscope system designed for the real-time detection of heart valve disorders using the ESP32 microcontroller and MEMS microphone (INMP441). The system captures phonocardiogram (PCG) signals, preprocesses them through digital filtering, and extracts Mel-Frequency Cepstral Coefficients (MFCC) features. A Support Vector Machine (SVM) model trained in Python classifies heart sounds as normal or abnormal, achieving an average accuracy between 75% and 85%. This approach reduces dependency on clinical instruments and enables accessible cardiac screening in remote areas.

Smart agriculture leverages Artificial Intelligence (AI), the Internet of Things (IoT), and wireless sensor networks to transform conventional farming into a more efficient, sustainable, and data-driven system. Traditional agricultural practices often struggle with issues such as inefficient water and fertilizer usage, crop losses, pest infestations, and labour shortages. Furthermore, conventional soil and plant analysis methods are time-consuming,destructive, and lack real-time monitoring, resulting in delayed responses and resource wastage. To address these challenges, this study proposes a smart agriculture system that integrates sensors, Arduino microcontrollers, and cloud-based IoT platforms to continuously monitor and manage agricultural fields. Key components include ultrasonic sensors for water level monitoring to regulate irrigation, soil moisture sensors to assess land fertility and water requirements, and temperature sensors to measure atmospheric conditions crucial for crop growth. These sensors transmit real-time data wirelessly to an Arduino-based central controller, which processes the information and uploads it to an IoT cloud platform for further analysis. AI algorithms applied on the cloud perform predictive analytics, anomaly detection, and early disease identification, enabling proactive decision-making and resource optimization.

The “Smart Sericulture System using IoT and Image Processing” is an intelligent monitoring and automation solution designed to enhance silk production by integrating modern technology into traditional sericulture practices. The project focuses on creating a smart environment that continuously monitors key parameters such as temperature, humidity, and light intensity inside the silkworm rearing house. These parameters are critical for healthy cocoon formation and improved silk yield. By utilizing IoTenabled sensors, real-time data is collected and transmitted to a cloud platform via Wi-Fi using an ESP32 microcontroller, enabling farmers to remotely monitor environmental conditions through a mobile or web-based interface. The system also incorporates image processing techniques using a camera module to automatically detect the growth stage and health condition of silkworms, reducing manual inspection errors and improving efficiency. Based on the sensor readings and image analysis, the system can automatically control actuators such as fans, heaters, or humidifiers to maintain optimal rearing conditions. This integration of IoT and image processing ensures a smart, scalable, and energy-efficient sericulture management system that supports precision farming. The proposed system not only helps in improving productivity and quality but also provides a cost-effective, user-friendly solution for farmers to achieve sustainable silk farming through technology-driven automation and monitoring.

The Continuous animal health monitoring is crucial for ensuring livestock welfare and improving productivity in the agricultural sector. Conventional animal health tracking methods rely on manual observation, which is time-consuming, error-prone, and incapable of detecting early-stage illnesses. This paper presents an IoT-based Animal Health Monitoring System that enables realtime, non-invasive measurement of vital parameters such as body temperature, ambient temperature, humidity, and heart rate. The system employs ESP8266 NodeMCU as a processing and communication unit, interfaced with DS18B20 (body temperature sensor), DHT11 (environmental temperature and humidity sensor), and MAX30105 (heart rate sensor). Acquired data is transmitted to the Blynk Cloud via Wi-Fi, where it can be visualized remotely on a smartphone dashboard. The system alerts the user when vital signs exceed normal ranges, allowing timely intervention. Testing on small animals demonstrated accurate results comparable to clinical devices. The design is compact, low-cost, and ideal for livestock management, veterinary care, and smart farm applications. Future enhancements include integration with weather forecast APIs for predictive control, mobile application development for remote monitoring, solar power integration for energy autonomy, and machine learning algorithms for adaptive behavior based on historical weather patterns.

Our project presents the Vehicle Collision Avoidance and Alert System utilizing an Android application to enhance safety and prevent accidents. The system is built frontend and backend using a 360-degree ultrasonic sensor to detect obstacles within a threshold distance of 40 cm. This detection is communicated to the Android app via a Bluetooth device. Upon detecting an obstacle, the app triggers a voice alert, notifying the driver to take immediate action. In such cases, the system uses GCM communication to automatically send an SMS to an authorized person, providing the vehicle’s location and alerting them about the incident. This real-time detection and communication mechanism enhances vehicle safety by preventing collisions and ensuring prompt response during emergencies. The system is designed to be efficient and reliable, offering a comprehensive safety solution for drivers.

The project “Multifunctional Military Robot” is designed to enhance safety and surveillance in defense applications using an ESP8266 microcontroller as the main control unit. The system integrates multiple sensors such as PIR, Gas, Flame, and Ultrasonic to perform real-time environmental monitoring and autonomous movement. The robot can detect human presence, obstacles, fire, and hazardous gases in its surroundings and respond intelligently by controlling the motors through a motor driver circuit. It also communicates critical alerts wirelessly via Wi-Fi to remote users, ensuring quick response in emergency conditions. The 7805-voltage regulator ensures stable power supply to all modules for reliable operation. This multifunctional robot demonstrates high efficiency in performing military surveillance, safety monitoring, and hazardous environment detection, providing a robust and cost-effective solution for modern defense automation.

The rapid increase in the number of vehicles has led to severe traffic congestion and parking management challenges in urban areas. Conventional parking systems rely on manual supervision, which often results in time delays, inefficiency, and human error. To overcome these issues, an automated Smart Parking Slot Booking System is proposed in this project. The system enables users to reserve parking slots through a web-based interface by entering their name, vehicle number, and expected entry time. Upon successful booking, a unique QR code is generated and sent to the user for authentication at the parking gate. The QR code remains valid for a limited time, ensuring secure and controlled access. The integration of IoT components such as ESP32-CAM, IR sensors, and esp12e allows for realtime monitoring of slot availability and software technologies such as frontend and backend websites for booking and monitoring the slots. This system enhances parking efficiency, minimizes human intervention, and contributes toward the development of a sustainable smart city infrastructure.

In growing urban areas, effective traffic signal management is critical, especially to expedite emergency vehicles like ambulances. This journal presents a smart traffic light controller that prioritizes emergency vehicles using dual-mode detection involving RF communication and sound sensors, managed by an ESP32 microcontroller. Upon detecting an emergency vehicle, the system automatically overrides normal signals to create a green corridor, enhancing emergency response times. The design integrates IoT capabilities for remote monitoring and real-time data analytics. Prototype results demonstrate improved reliability, real-time responsiveness, and scalability for urban deployment.

The automatic analysis of respiratory sounds has been a field of great disquisition interest during the last decades. Automated type of respiratory sounds has the implicit to descry abnormalities in The onset phase of respiratory dysfunction and thus enhance the effectiveness of decision timber. still, the actuality of a publically available large database, in which new algorithms can be executed, estimated, and compared, is still asking and is vital for further developments in the field. The first scientific challenge was organized with the main thing of developing algorithms suitable to characterize respiratory sound recordings from clinical and non- clinical surroundings. The database was created by two disquisition armies in Portugal and in Greece, and it includes 920 recordings acquired from 126 subjects. Altogether, 6,898 breathing cycles. were recorded. The cycles were annotated by respiratory experts as including crackles, fizzes, a combination of them, or no accidental respiratory sounds. The recordings were collected using eclectic outfit and their duration ranged from 10s to 90s. The casket locales from which the recordings were acquired was also handed. Noise situations in some respiration cycles were high, which dissembled real life condition sand made the type process more challenging.

Effective inventory management plays a vital role in ensuring accurate stock control, reducing human error, and maintaining operational efficiency in industrial and institutional environments. InvenTrack – Smart Inventory Management System is an IoTenabled solution that combines RFID technology and real-time data processing to automate inventory tracking and access management. The system utilizes an ESP32 microcontroller interfaced with RFID modules for user authentication, IR sensors and limit switches for box status detection, and a camera module for visual verification of item access. Captured images are automatically uploaded to a secure cloud storage and logged in Google Sheets along with corresponding RFID user data, ensuring complete traceability of inventory operations. A web app dashboard displays live inventory status, user activity, and camera logs, allowing administrators to monitor usage remotely. By integrating Wi-Fi communication and cloud-based analytics, InvenTrack enhances transparency, accuracy, and security in inventory handling. The system’s modular and cost-effective design makes it highly adaptable for laboratories, warehouses, and educational institutions aiming to transition from manual to smart inventory control.

The “IoT Integrated Energy Monitoring and Control Unit using ESP32” is a smart system designed to measure, monitor, and control electrical energy consumption in real time. The project aims to overcome the limitations of traditional meters, which only provide cumulative readings without any form of remote monitoring or control. By integrating the Internet of Things (IoT) technology, this system allows users to track power usage through the Blynk mobile application, offering instant visibility and control over connected electrical appliances. The ESP32 microcontroller acts as the central processing unit, interfacing with the PZEM-004T energy sensor to measure voltage, current, power, and energy parameters with high accuracy.The measured data is transmitted to the cloud using the built-in Wi-Fi capability of the ESP32, enabling users to view live readings and monitor load conditions remotely. Additionally, the system includes a relay module that allows appliances to be switched ON or OFF through the mobile app, thereby offering complete remote control functionality. This not only helps in energy conservation but also provides a cost-effective solution for smart energy management at homes, offices, and industries. The system is efficient, scalable, and easy to deploy, making it suitable for integration with renewable energy systems and future smart grid applications. Overall, the project demonstrates an intelligent, IoT-based approach toward efficient power usage and control.

Road accidents are one of the leading causes of fatalities worldwide, emphasizing the need for rapid detection and emergency response mechanisms. This research presents an IoT-based real-time vehicle accident detection and RSA (Road Safety Assistance) emergency alert system utilizing the Raspberry Pi Pico W as the core processing unit. The proposed system integrates multiple sensors, including an accelerometer, gyroscope, and vibration sensor, to continuously monitor vehicular motion and detect collision impact through thresholdbased analysis. Upon detecting an accident, the system automatically transmits critical information such as location coordinates, time, and accident intensity to nearby emergency services and pre-registered contacts via Wi-Fi connectivity. A GPS module ensures accurate geolocation, while a cloud-based IoT platform enables real-time monitoring and data storage for post-accident analysis. The system minimizes human dependency by automating detection and alerting processes, significantly reducing response time and improving survival rates. Experimental results demonstrate that the prototype achieves high accuracy in impact detection with minimal false alerts. This work highlights the potential of low-cost, IoT-enabled embedded systems in enhancing road safety and enabling intelligent emergency response infrastructures.

Gas leakage is one of the major causes of fire accidents in homes and industries. To prevent such incidents, this project proposes an IoT-Based Gas Detector with Auto Valve Off, Power Cut, and Exhaust On. The system uses an MQ-6 gas sensor with an Arduino UNO microcontroller to continuously monitor gas levels. When leakage is detected beyond the safety limit, the system automatically turns off the main power supply, closes the gas valve using a servo motor, and switches on the exhaust fan to remove the leaked gas. A buzzer is activated to alert nearby users, while the ESP8266 Wi-Fi module sends real-time notifications through the IoT platform. An OLED display shows live gas concentration levels for on-site monitoring. The proposed system is low-cost, efficient, and reliable, suitable for domestic and small-scale industrial use. By combining IoT technology with automatic control, the system ensures early detection and quick preventive action to reduce the risk of gas-related accidents.

The IoT-Based Smart Roof System presents an innovative automated solution designed to respond dynamically to changing weather conditions using sensor technology and microcontroller-based automation. Manual operation of roofs during varying weather patterns is time-consuming, prone to human error, and often results in property damage due to delayed response. This project employs an ESP32-C3 microcontroller integrated with DHT11 temperature and humidity sensor, LDR light intensity sensor, and rain detection module to monitor environmental parameters in real time. When precipitation is detected, the system automatically closes the roof via servo motor actuation to prevent water ingress. During high light intensity with no rainfall, the roof opens automatically to enable natural ventilation and reduce energy consumption. A 16×2 LCD display provides real-time status information including temperature, humidity readings, and roof positioning state.

Natural and man-made disasters such as earthquakes, fires, and building collapses pose serious risks to human life and make traditional rescue operations dangerous and inefficient. This project presents ARES (Advanced Rescuer and Evacuation System) — an intelligent, multi-sensor rover designed to assist in search and rescue missions under hazardous conditions. The system integrates multiple sensors including MLX90614 infrared sensors for body heat detection, MQ-2 gas sensors for hazardous gas and smoke identification, and HCSR04 ultrasonic sensors for obstacle detection and navigation. Controlled by an Arduino UNO R3 microcontroller and equipped with Bluetooth (HC-05) and Wi-Fi modules, ARES enables real-time monitoring and wireless control. Operating autonomously, the rover identifies victims, detects environmental hazards, and transmits live sensor data to a remote operator. Testing results demonstrate over 92% accuracy in detection and navigation. Compact, cost-effective, and reliable, ARES offers a smart, IoT-enabled robotic solution that enhances the efficiency, safety, and speed of modern disaster management operations.

This project proposes a smart system for Non-Destructive Evaluation (NDE) of industrial components using AI and IoT-based wireless sensor networks.Various sensors such as vibration, temperature, humidity, and pressure sensors are used to collect data from industrial equipment without causing any physical damage. The sensor data are transmitted to a cloud platform (Thing Speak) through a NodeMCU microcontroller for storage and visualization. Using Python-based analysis and a Decision Tree algorithm, the collected data are processed to identify patterns and detect abnormalities that indicate possible component failure. This system provides a cost-effective and intelligent solution for predicting faults, minimizing unexpected breakdowns, and improving the overall maintenance efficiency of industrial systems. The integration of AI and IoT thus ensures accurate monitoring, remote accessibility, and early fault prediction for reliable industrial operation.

This system deals with measuring Air Quality using MQ-7 sensor along with Carbon Monoxide CO using MQ7 sensor. Measuring Air Quality is an important element for bringing lot of awareness in the people to take care of the future generations a healthier life from air pollution. Based on this, Government of India has already taken certain measures to ban ‘Single Stroke’ and ‘Two Stroke’ Engine based motorcycles which are emitting high pollutions comparatively. We are trying to implement the same system using web network, we can bring awareness to every individual about the harm we are doing to our environment. Already, New Delhi is remarked as the most pollution city in the world recording Air Quality above 300PPM. We have corrected the other papers where they have wrongly calibrated the sensor and wrongly projecting the PPM values. We have also used easiest platform like webpage and set the dashboard to public such that everyone can come to know the Air Quality at the location where the system is installed. The design of the system is focused towards an IoT-based real time air pollution monitoring system uses interconnected sensors to collect real-time data on various air quality parameters like particulate matter, gases, and environmental conditions. This data is transmitted wirelessly to a central system for analysis and decision-making, enabling timely detection of pollution levels and promoting environmental quality management. The ESP8266 module is used to act as an IOT module. The data monitoring process can be viewed through web browser using IP address of the module. The analysis of the proposed system is design is in hardware model.

The Smart Ambulance System Using IoT and Doctor Allocation Using Python is an innovative healthcare system aimed at improving emergency medical response through real-time monitoring and intelligent decision-making. Traditional ambulances lack continuous health data tracking and real-time specialist coordination, leading to treatment delays during critical conditions. This project integrates an ESP8266 NodeMCU microcontroller with biomedical sensors such as the MAX30102 to measure vital parameters including heart rate, SpO₂ (oxygen saturation), and DHT11 sensor for monitoring ambulance cabinet temperature. The acquired data is transmitted to the ThingSpeak cloud platform via Wi-Fi and visualized using a HTML-based web interface. The Python algorithm analyzes the patient’s health parameters and automatically allocates the appropriate specialist—such as a cardiologist, pulmonologist, or general physician—based on predefined threshold values. The system ensures that hospitals receive patient information in advance, enabling faster medical preparedness and improved survival rates. This IoT-based approach combines embedded sensing, cloud analytics, and intelligent automation to create a connected healthcare environment for efficient and life-saving ambulance management.

Air pollution has become a major environmental concern, especially in industrial areas such as stone crushing units where large quantities of dust and harmful gases are released into the atmosphere. Continuous monitoring of air quality in these regions is essential to ensure a safe and sustainable working environment. This project presents the design and development of a standalone air pollution monitoring system using an Arduino Nano controller.The proposed system integrates sensors such as MQ135 for detecting harmful gases and DHT11 for measuring temperature and humidity. The collected data is processed by the Nano microcontroller and displayed in real time on an LCD screen. A wireless communication module (HC-05 Bluetooth) is incorporated to transmit air quality data to a remote device, enabling easy monitoring and record keeping. The system operates on a regulated 5V power supply using a 7805 voltage regulator, ensuring stable and efficient performance even in outdoor industrial environments.This compact and low-cost system provides accurate and reliable air quality measurements, making it suitable for deployment near stone crushing operations. It can function independently using a battery or solar power, offering flexibility and portability. The developed prototype demonstrates the potential of embedded systems and sensor technologies in promoting environmental safety and aiding in pollution control management.

Migraine is a neurological disorder characterized by recurrent headaches often accompanied by physiological symptoms such as stress, elevated body temperature, and abnormal heart rate variations. In this paper, we present the design and development of a smart headband capable of detecting early migraine symptoms and providing automated cooling therapy using embedded sensing and control technologies. The proposed system integrates an Arduino Uno microcontroller with multiple biosensors, including a Galvanic Skin Response (GSR) sensor for stress detection, a heart rate sensor for monitoring pulse variations, a DHT11 sensor for temperature and humidity measurement, and an MPU6050 sensor for detecting head movement and motion anomalies associated with migraine episodes. The collected physiological and environmental data are continuously processed by the Arduino, which triggers a relay-controlled Peltier module to deliver localized cooling therapy when abnormal readings indicative of a migraine are detected. This closed-loop system enables real-time monitoring and automated therapeutic response, minimizing user intervention. Experimental results demonstrate that the smart headband effectively identifies stress-induced migraine patterns and provides immediate cooling relief, offering a non-invasive, portable, and cost-effective solution for migraine management. This work contributes to the growing field of wearable healthcare devices and highlights the potential of IoT-enabled biofeedback systems in personalized medicine.

Cardiovascular disorders continue to be one of the major contributors to global mortality, which emphasizes the need for continuous cardiac supervision and early detection of unusual heart activity. This work presents an Io T-enabled system capable of monitoring heart rate and detecting heart attack symptoms using Edge Artificial Intelligence (Tiny ML) for on-device ECG analysis. The design incorporates an AD8232 ECG sensor connected to an ESP32 micro-controller to acquire, filter, and analyze cardiac signals locally, ensuring quicker response time without heavy reliance on remote cloud servers. A compact Tiny ML model is embedded in the micro-controller to classify abnormal heart conditions like tachycardia, bradycardia, and arrhythmia. Unlike conventional methods that rely on SMS notifications, the proposed model automatically initiates a direct emergency call to ambulance services during critical episodes, reducing human intervention delay. Additionally, summarized data is periodically uploaded to the Things-peak cloud for longterm medical evaluation. This portable, affordable, and energy efficient solution offers real-time operation even without internet, making it ideal for rural and remote healthcare applications.

The rapid growth of urbanization and the increasing number of vehicles on roads have made traffic management a critical issue in modern cities. Conventional traffic light systems are generally programmed with fixed time intervals, which do not adapt to real-time traffic density. As a result, these systems often lead to longer waiting periods, unnecessary fuel consumption, and increased congestion, especially during peak hours. To overcome these drawbacks, an intelligent traffic control system can be designed using sensors integrated with a microcontroller. In this system, sensors such as Microphone are placed at each lane of an intersection to measure the density of vehicles. The data collected from these sensors is continuously transmitted to the microcontroller, which processes the input and dynamically adjusts the signal timing. Roads experiencing heavy traffic are automatically assigned longer green light durations, while less congested routes receive shorter intervals. This real-time decision-making ensures smooth traffic flow, reduces delays, and minimizes the chances of traffic jams. Furthermore, the system contributes to environmental sustainability by reducing idle time for vehicles, thereby lowering fuel usage and harmful emissions. The design is also scalable and cost-effective, as microcontrollers are affordable and easy to integrate with multiple sensor types. By using programmable logic, the system can be customized for various traffic conditions such as peak hours, emergency vehicle passage, or night-time operations.

The Intra Navi Bot is a semi-autonomous navigation robot designed to provide intelligent movement using onboard sensing and external machine learning processing. It combines ESP32-CAM for visual sensing, TCRT5000 sensors for path detection, and ultrasonic modules for obstacle avoidance. The system leverages Bluetooth communication for real-time decision-making and control. This project aims to create a low-cost, reliable navigation system for indoor applications such as surveillance, education, and automation. The design methodology, implementation, and analysis demonstrate high efficiency and accuracy in dynamic obstacle environments.

In an era where digital intrusions and unauthorized access incidents are rapidly increasing, traditional password-based vault systems are no longer sufficient to ensure security. This paper presents the design and development of an IoT-enabled smart vault system that utilizes dual-factor authentication based on biometric fingerprint verification and One-Time Password (OTP) validation. The system employs an ESP32 microcontroller for processing and communication, integrating an R307S Optical Fingerprint Sensor, HC-SR04 Ultrasonic Sensor, and a 12V DC Solenoid Lock. When the user approaches the vault, proximity detection activates the authentication sequence. After successful fingerprint verification, an OTP is generated and sent to the registered mobile number using the Twilio API over Wi-Fi. The vault is unlocked only when both authentication steps are successfully validated. The proposed design enhances the reliability, scalability, and security of vault systems, making it a cost-effective solution for residential, commercial, and institutional applications.

The rapid growth of urban transportation systems demands innovative solutions for efficient fare collection, passenger management, and data-driven operation. This paper presents the design and implementation of an IoT-based Smart Bus Ticketing System integrating ESP32 microcontroller, RFID technology, GPS tracking, OLED display, Firebase Realtime Database, and Twilio SMS API, combined with an ultrasonic sensor for passenger detection and a servo-controlled door automation mechanism. The proposed system introduces a seamless, contactless ticketing process wherein passengers authenticate using RFID cards, enabling automatic fare calculation based on GPS-derived travel distance. Real-time data synchronization with Firebase ensures continuous cloud connectivity, while Twilio facilitates instant SMS notifications for fare deductions and trip updates. The BUS2go mobile application, developed as a Progressive Web App (PWA), provides an intuitive interface for users to monitor balance, recharge cards, and review travel history. Experimental evaluation demonstrates high accuracy in RFID detection, reliable GPS performance, and over 95% precision in passenger counting. The system offers a scalable, transparent, and automated public transportation solution, enhancing operational efficiency and passenger convenience in smart city ecosystems.

Health Information Technology (HIT) is globally recognized as essential for enhancing patient safety and optimizing care delivery. However, its successful implementation in Low- and Middle-Income Countries (LMICs), faces persistent technical, financial, and organizational barriers. This study investigated the core barriers to HIT implementation in LMICs and identified targeted strategies to enhance digital transformation within resource-limited healthcare systems. A qualitative single-case study design was employed. Data were collected through semi-structured interviews with 40 diverse healthcare specialists and stakeholders. Template analysis was performed using the NVIVO software. Nine major barriers were identified: inadequate HIT policies, financial shortages, cultural and privacy concerns, structural characteristics of the healthcare sector, insufficient qualified personnel, infrastructure limitations, weak data integration, resistance to change, and poor system design. Human, organizational, and contextual factors have emerged as central determinants of HIT success or failure. Digital transformation in LMICs cannot advance without comprehensive policy reform, sustainable financing models, stronger governance, interoperability standards, and structured workforce capacity building. Human factors, not technology alone, remain the decisive element in HIT adoption. This research provides evidence-based, actionable strategies to guide policymakers and healthcare managers in Jordan and other LMICs toward sustainable and high-quality healthcare digitalization. This study addresses a critical knowledge gap by providing empirically grounded, context-specific recommendations essential for accelerating HIT maturity in LMIC healthcare systems.

The convergence of fashion and technology has created new opportunities for creativity, convenience, and sustainability through the integration of computer vision and artificial intelligence. This systematic review, following PRISMA guidelines, examines 200 studies published between 2017 and 2025 to analyze computational techniques for garment design, accessories, cosmetics, and outfit coordination across three key areas: generative design approaches, virtual simulation methods, and personalized recommendation systems. We comprehensively evaluate deep learning architectures, datasets, and performance metrics employed for fashion item synthesis, virtual try-on, cloth simulation, and outfit recommendation. Key findings reveal significant advances in Generative adversarial network (GAN)-based and diffusionbased fashion generation, physics-based simulations achieving real-time performance on mobile and virtual reality (VR) devices, and context-aware recommendation systems integrating multimodal data sources. However, persistent challenges remain, including data scarcity, computational constraints, privacy concerns, and algorithmic bias. Actionable directions for responsible AI development in fashion and textile applications has been proposed, emphasizing the need for inclusive datasets, transparent algorithms, and sustainable computational practices. This review provides researchers and industry practitioners with a comprehensive synthesis of current capabilities, limitations, and future opportunities at the intersection of computer vision and fashion design.

Over the past few years, Artificial Intelligence (AI) has grown significantly in modern education, and that has opened up further opportunities for learning analytics across various educational domains, intelligent tutoring and assessment systems as well as datafied personalization. In the last few years, some surveys and systematic reviews are starting to demonstrate how educational systems based on artificial intelligence are evolving from experimental prototypes to practical applications with a clearer impact in higher education and online learning [1], [2]. With such advancements, it is even more important to take a closer look at Explainable Artificial Intelligence (XAI) that targets explanation of AI-based decisions for educators and students [3, 4] beyond the trust and transparency but also focusing on fairness. More immediately, the rapid adoption of generative AI and large language models has accelerated integration of AI in education by making it easier to generate content, interactively tutor students, or provide personalized feedback -- while simultaneously raising questions about responsible use and academic integrity [5], [6]. Focusing on survey research, real-world applications, XAI based educational systems and generative AI for learning solutions, this paper provides a focused review of the AI in education literature dated from 2020 to 2025. To this end, in this review we discuss current challenges and open research directions to facilitate transparent, ethical and human-centric AI use in education by channeling key focus points on research trends, application areas as well as methodological paradigms.

During extensive field surveys in the Kalaburagi region of Karnataka, India, a Calotropis population exhibiting a consistent combination of morphological features differing from those typically reported for Calotropis procera and Calotropis gigantea was documented. This population is provisionally referred to here as Calotropis prefina. The present study evaluates its taxonomic position using an integrative framework combining detailed morphological assessment, leaf and stem anatomical analysis, biochemical characterization of latex (milk coagulation activity and temperaturedependent pH profiling), chloroplast DNA barcoding (rbcL), and herbarium-based authentication. Comparative analyses revealed a stable and diagnostic suite of characters involving plant stature, stem pubescence, latex viscosity, leaf morphology, floral architecture, pollinia structure, vascular organization, and laticifer distribution. Calotropis prefina exhibited markedly reduced pollen viability (57.5%) compared with C. procera (82.5%) and C. gigantea (80%), indicating reproductive differentiation. Biochemical assays showed reduced milk clotting activity and a consistently alkaline, thermally stable latex pH profile in C. prefina relative to C. procera and C. gigantea, indicating functional divergence. Molecular characterization using the chloroplast rbcL barcode confirmed the placement of the investigated taxon within the genus Calotropis. NCBI BLAST analysis revealed the highest sequence similarity with Calotropis procera (98.04% - 97.86%), while C. gigantea was also retrieved among the closest matches with comparable identity values. Independent identification through the BOLD Systems database similarly failed to achieve unambiguous species-level discrimination, returning both C. procera (97.83%) and C. gigantea as closest reference taxon with closely comparable percentage similarity. These results indicate strong genetic affinity within Calotropis while highlighting the Phylogenetic interpretation placed C. prefina within the Calotropis clade, closely allied to C. procera and C. gigantea, yet forming a distinct lineage. Taken together, the convergence of morphological, anatomical, biochemical, and molecular evidence supports the interpretation of Calotropis prefina as a distinct taxonomic entity within the genus Calotropis. The provisional designation is applied pending formal nomenclatural validation in accordance with the International Code of Nomenclature, with herbarium authentication currently underway at the Botanical Survey of India.

Social conflict at the community level remains a serious problem in Indonesia, particularly in areas experiencing social heterogeneity and economic pressure. The urgency of this research arises from the increasing frequency of horizontal conflicts that cannot be fully explained through the government's structural approach, so that a deeper understanding of the role of community social and cultural values in preventing conflict is needed. This study offers novelty by integrating three social and cultural capital variables of social tolerance, social cohesion, and local wisdom into a single analytical model moderated by local leadership capacity, something that has rarely been tested empirically in the context of Indonesian communities. Using a quantitative method based on SEM PLS, this study examines data from communities that have the potential to experience social conflict. The results of the study indicate that social tolerance, social cohesion, and local wisdom do not have a significant direct influence on social conflict prevention. However, local leadership capacity was shown to act as a moderating variable that strengthens the influence of social tolerance on social conflict prevention. Conversely, local leadership moderation did not occur in the relationship between social cohesion and local wisdom on conflict prevention. This novel finding confirms that social and cultural capital do not automatically function as conflict prevention instruments without the active involvement of local leaders who are able to manage social dynamics adaptively. The implications of this study emphasize the importance of investing in strengthening local leadership capacity and revitalizing social and cultural values as communitybased strategies for sustainable conflict prevention.