Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges
Smart Innovations in IoT Technology
Real-Time Monitoring and Assessment of the Indoor Air Quality Hazard Index using Deep Learning Approach
A Comprehensive Review of Internet of Things (IoT) in the Automobile Industry and its Diverse Applications
Security Challenges and Measures of IoT Devices and its Networks
This study introduces a new IoT solution for an anti-theft flooring system that uses facial recognition technology to enhance security in homes and workspaces. It comprises sensors, such as piezoelectric pressure sensors placed on the floor, which are sensitive to pressure or movement. When detection occurs, a nearby camera is triggered to capture the individual's face, which is then matched against a facial database using a facial recognition feature. If the face is matched successfully, a four-digit One-Time Password (OTP) is generated by the system, and a GSM module sends it to the registered mobile number. The individual must then enter the correct OTP through a Bluetooth-enabled mobile application governed by the HC-05 module. If the correct OTP is entered before the 60-second timer expires, the system unlocks the secured area using a 12V DC electromagnetic lock. If the OTP is incorrect or the timer expires, the secured area remains locked, and the system displays "Invalid OTP" on the LCD screen. This solution enhances existing security measures, such as motion sensors and cameras, which can be bypassed using tricks like IR cloaks or by exploiting blind spots. Pressure detection, facial recognition, and OTP-based two-factor authentication are employed in the proposed system to monitor the area in real-time, provide access control to officially prescribed personnel, and respond quickly to any unauthorized attempts. This method offers robust security at a low cost and can be effortlessly scaled to many homes, businesses, and highly secured zones.
In developing nations, much of the population resides in rural regions where medical systems lack integration for information exchange. As a result, pregnant women frequently cannot consult physicians throughout their pregnancies. The proposed study aims to develop a Smart and Intelligent Medicine Recommendation and IoT Monitoring System using Raspberry Pi. This system integrates advanced health monitoring technologies, including the MAX30102 sensor for heart rate, temperature, and SpO2 measurements. Through an interactive method, the system gathers additional input details from the user. It is designed to differentiate between pregnant and non-pregnant women, providing personalized medicine recommendations accordingly. An accelerometer sensor detects fetal movement, confirming pregnancy when the interactive method is inconclusive. The temperature, heart rate, accelerometer data, and pregnancy confirmation are displayed in ThingView and on a mobile phone. This system is sensitive and lightweight, making it ideal for home monitoring. While ultrasonography is currently used, it has limitations, such as prolonged duration and high cost. Furthermore, the limitations of ultrasound scanning for fetal subjects need to be better understood, and ultrasound scans are not always recommended.
Electricity is an essential part of human life. The measurement and excessive usage of electricity raise many concerns. To reduce errors and costs in energy consumption, a low-cost wireless sensor network is implemented for digital energy meters. This innovative IoT-based solution for electric meter monitoring is designed to alleviate manual measurement efforts and increase user awareness of excessive electricity consumption. The system integrates with digital energy meters through a cost-effective wireless sensor network and features a mobile application capable of automatically interpreting meter readings. It goes beyond basic monitoring by incorporating carbon footprint calculations, highlighting the environmental impact of energy usage. By reducing human error and cutting down on energy costs, this integrated solution offers a comprehensive approach to efficient energy management in real-time scenarios.
This study presents a low-cost IoT system designed to detect patient head motions and postures using Force Sensing Resistors placed on a pillow. These resistors are connected to a microcontroller that collects patient data during sleep, sends it to the cloud, and makes it accessible to healthcare specialists. The aim of this work is to monitor sleep quality using affordable, easy-to-use pillows in an ambulatory setting. This approach eliminates the need for expensive, dedicated sleeping rooms, which can negatively affect both patient sleep and measurement quality. It is feasible to monitor patients' actions throughout their sleep, which is crucial for identifying factors that could lead to mild head and neck injuries.
In the context of increasing automation and the rise of smart vehicles, Indian roads still struggle to ensure safe transportation due to accidents caused by overspeeding and negligence of traffic rules. Speed limiting can be implemented with a single piece of code in the software for fully automatic and semi-automatic vehicles. In the Indian market, automatic vehicles are still a rarity due to issues of affordability and compatibility with local roads and terrains. This effective solution uses RFID technology and obstacle detection to implement a speed-limiting system on ordinary vehicles through a mounting device on the accelerator, without altering the existing internal infrastructure or hardware components of the vehicle. UHF RFID readers in vehicles read RFID tags placed at roadside points, which contain encrypted speed limit and location information to enforce speed limits. An auxiliary RFID unit is designed to create a foolproof system by storing the last read information from the primary speed-limiting system in a tag, which will be read at traffic checkpoints, thereby identifying malpractices and alerting authorities. Lower speeds can help avoid accidents or at least reduce the severity of accidents.
