Smart Home Security: An IoT-Driven Facial Recognition Door Lock Using ESP32-CAM
Ardino Based Smart Irrigation System
IoT-Enhanced Cardiotocography; Real-Time Integration of Vital Parameters for Comphrensive Fetal Monitoring
Internet of Things Based Patient Monitoring System for Comatose Patient
IOT Based Wearable Muscle Strain Detector
AgroDefend: Smart Fire Prevention Solutions
Smart Innovations in IoT Technology
Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges
Smart Water Management by Smart Sensors and IoT: Enhancing Efficiency and Sustainability with Automatic Pump Control Systems
A Comprehensive Review of Internet of Things (IoT) in the Automobile Industry and its Diverse Applications
Implementation of IoT Based Forest Fire Detection and Prevention System
The Smart Door Lock leverages advanced facial recognition technology to enhance access control. This system replaces traditional keys or passwords by recognizing and verifying individuals through facial feature analysis. It utilizes the ESP32-CAM module, an affordable development board equipped with a camera, Wi-Fi, Bluetooth, and low-energy BLE capabilities. Its compact structure makes it well-suited for IoT applications. When an authorized individual approaches, the door unlocks automatically, offering both security and ease of use. The setup also incorporates an Arduino Uno microcontroller, capable of handling multiple inputs and outputs for added flexibility. For improved security, an optional fingerprint sensor can be integrated, enabling dual layer authentication through biometric verification.
This paper describes an Arduino-based irrigation system that uses a relay module, DHT11, soil moisture, and rain sensors to construct an intelligent agricultural irrigation setup. To optimize irrigation, the system gathers and analyses data on temperature, humidity, soil moisture, and rainfall. It guarantees that plants get enough water without being overwatered or wasted. This economical and effective solution promotes sustainable water management, increases agricultural productivity at various scales, and reflects the concepts of precision agriculture
This paper proposes an IoT-based fetal monitoring system using Arduino Uno, aiming to remotely monitor fetal movement, SpO2, BPM, pressure, and temperature. The system utilizes a combination of sensors including accelerometers, pulse oximeters, pressure sensors, and temperature sensors, interfaced with Arduino Uno. Data is transmitted wirelessly to a cloud server using I2C protocol for real-time monitoring and analysis.Validation tests demonstrate the system's accuracy in capturing fetal parameters and maternal vitals, enabling timely anomaly detection and intervention. The system offers a cost-effective solution for remote prenatal care, with potential applications in underserved regions. Future work involves integrating machine learning for predictive analytics and refining the user interface for improved usability.
Internet-of-Things and machine learning are paving the way for a new era in healthcare. Physiological data can now be collected from anyone, anywhere, at any time through innovative technologies such as wearable and implantable medical devices (IWMDs). By using this capability, routine and clinical settings can be analyzed for patterns and predictions of healthcare outcomes, extending the reach of healthcare beyond traditional clinical settings. With these advancements, passive data collection is replaced with proactive decision-making, thereby improving patient care to a great extent. The sensors analyze vital parameters like blood pressure, temperature, heart rate, oxygen levels, and brain activity in an Internet of Things-based comatose patient monitoring system. The data is gathered and operated by an embedded microcontroller before being transmitted over Wi-Fi to a cloud platform. Real-time warnings and notifications of any unusual behaviors are sent to healthcare practitioners via web dashboards or mobile apps. To increase prediction and recommendation accuracy and guarantee prompt interventions, the system employs machine learning methods. This ongoing observation improves patient care and offers a dependable way to manage comatose patients' healthThese technologies are an enormous breakthrough in healthcare since they enable constant patient monitoring and early detection of prospective problems. By facilitating rapid detection and treatment for a variety of medical diseases, this not only boosts the standard of care for people in critical circumstances but also has the ability to improve public health in general. As these technologies develop further, they have the potential to completely transform the healthcare system by increasing the effectiveness and availability of high-quality care.
Wearable technology has emerged as a powerful tool for real-time health monitoring, offering innovative solutions for various medical and fitness applications. This paper presents an IoT-based wearable muscle strain detector designed to monitor and analyze muscle strain in real time. The system integrates advanced sensors, including surface electromyography (sEMG) sensors and strain gauges, to detect abnormal muscle activity and strain levels. Data collected by the sensors is processed using a microcontroller and transmitted wirelessly to cloud platform for visualization and analysis. The proposed device is lightweight, portable, and user-friendly, making it suitable for athletes, rehabilitation patients, and individuals prone to muscle-related injuries. The IoT connectivity enables continuous monitoring and integration with machine learning algorithms for predictive analysis and early intervention. This system aims to reduce the risk of severe muscle injuries, enhance athletic performance, and improve recovery outcomes in clinical settings. The development and testing of this wearable demonstrate its potential as a reliable tool for personalized health monitoring, providing valuable insights for users and healthcare providers.
The IoT-based Fire Detection and Prevention System in Farmland" project seeks to develop an innovative and robust solution to minimize the risks posed by fire outbreaks in agricultural areas. Leveraging the capabilities of the Internet of Things (IoT), this system provides continuous, real-time monitoring of farmland conditions to enable early detection of fire hazards. A network of interconnected sensors is strategically deployed across the farmland to measure environmental parameters such as temperature, and smoke levels. These sensors transmit the collected data to a central control unit, where it is processed by an advanced algorithm designed to detect potential fire threats. Upon detection, the system triggers an automatic response using relays that activate fire suppression mechanisms such as sprinkler systems or water pumps using motors, effectively containing and preventing the spread of fire. Additionally, the system includes a display module that provides real-time alerts and updates to farmers and relevant authorities, ensuring they can take immediate action when necessary. This solution not only enhances safety but also reduces crop damage and financial losses by addressing fire threats before they escalate.
