Monitoring System Research Articles

PulseGuard: Health Monitoring and Analysis System

Abstract: The use of wearable devices such as smart watches has led to a major change in the way a person is able to manage their health. In this paper, health monitoring system construction based on a smartwatch is designed which is capable of Health parameters like heartbeat monitoring, counting steps and their physical activity levels at continuous intervals. The system is built on modern web technologies and cloud services for data processing in a real-time manner, thus ensuring that alert messages are delivered immediately in case health parameters go beyond the normal ranges. The goal is to improve personal health management by providing expansion, dependability and availability to the users in areas where there is poor or no health care provision. The paper discusses system architecture, implementation, and the prospect of the system in enhancing preventive medicine and its practice for better health.

Wireless Sensing Network for Implementation of Air Quality Monitoring System and Indoor Air Quality Index Application

Indoor air quality significantly impacts respiratory health and mental activity. This study utilizes a wireless sensing network (WSN) based on the Internet of Things (IoT) to monitor indoor air quality, referred to as an indoor air quality monitoring system. The system was installed and applied on campus at the University of Baghdad. The present study aims to monitor air quality parameters continuously within laboratories. Carbon monoxide, sulfur dioxide, nitrogen dioxide, ammonia, and particulate objects are the pollutants chosen to be monitored by the installed system in this study. These pollutants were selected because they affect indoor facilities' comfort, health, and working conditions. Colored coded data was employed in the monitoring system; defined ranges for each pollutant were also integrated. Sensor nodes, wireless modules that connect to the IoT server, and user applications are the main components of the IAQMS system. LCDs, mobile applications, the ThingSpeak web server, and the LabView platform are examples of techniques used to present data collected by the system. Additionally, the system includes a notification function that alerts students and lab personnel when indoor air quality index IAQI values signal unhealthy indoor air quality. The proactive approach ensures a regulated standard for indoor air quality and pollutants.

Wazuh SIEM for Cyber Security and Threat Mitigation in Apparel Industries

Security of IT infrastructure is critical in the modern digital environment, particularly for industries that manufacture clothing. The application of the Wazuh Security Information and Event Management (SIEM) system to improve security monitoring and compliance for an IT system in the apparel industry is the main topic of this study. To improve the apparel industry cyber threat monitoring system in real-time monitoring, detecting the threat vectors that support the centralized management system, to meet these objectives of the company, an SIEM security management system has been installed and configured with the components of Wazuh manager, indexer, Wazuh agent, and Wazuh dashboard as well. An Oracle virtual lab environment was created to install and configure the Wazuh system a robust Linux operating system was installed at the server level and Windows 10 was installed on the client end to the authenticity failure of the security system. To implement the Wazuh system, the project management guideline was followed which ensured the planning and implementation of the project, testing phases, and along with review and maintenance. Wazuh security management system successfully identified the authentical failure reports and vulnerabilities and generated automated reports from the system that showed the result of 1 critical, 19 high, and 5 medium levels of vulnerabilities shown in Figure 6, and the necessary patch recommended to solve the critical issues. On the other hand, the system also generated security events as well as integrity monitoring system reports. The deployment of the Wazuh security management system ensured the robust cost-effective security management system with high quality for detecting cyber threats which supports the regulatory and compliance requirements and is a viable tool for the apparel industry to protect the stakeholders’ interest.

Shape‐Programmable Lamellar Aerogel Enabling 3D Wireless Point‐of‐Care Electronics for Assistance in Pressure Injury Prevention

AbstractBedridden patients are at high‐pressure injuries risk, and timely turning or moving a bedridden patient is crucial to release localized pressure and reduce pressure injury risk. Pressure‐sensing electronics can collect real‐time pressure signals reflecting the movement of a patient and help carers evaluate pressure injury risk early and intervene timely. However, most pressure‐sensing electronics cannot be seamlessly mounted on the curved surface of the skin, resulting in signal instability and inaccuracy. Besides, detecting small pressures over a wide range is challenging. Herein, a lamellar shape‐memory conductive (LSMC) aerogel is developed to assemble 3D wireless point‐of‐care electronics for assisting pressure injury prevention. The lamellar structure makes the LSMC aerogel highly compressible, endowing the assembled electronics with high sensitivity and low detection limit. Moreover, the excellent shape‐memory effect allows the fabrication of 3D electronics on certain curved surfaces for adaptive wearing and provides sensitive and stable signals. Furthermore, a point‐of‐care monitoring system that integrates the LSMC aerogel‐based 3D electronics with wireless technology is developed. The system records real‐time pressure signals at certain locations (e.g., foot), which facilitates carers to analyze an individual's bedridden time and body movements, assess pressure injury risk, and intervene timely; this greatly benefits pressure injury prevention.

Design and Construction Of A River Water Level Monitoring System Based On IOT (Internet Of Things)

The prolonged rainy season due to extreme weather changes that occur in various regions in Indonesia means that people must be more alert to flood disasters. Many losses occurred due to flood disasters. Therefore, an effort is needed to minimize these losses, namely by creating a river level monitoring system based on IoT (Internet of Things). This river water level detection system uses an ultrasonic sensor (water level sensor), NodeMCU ESP8266 to read data and send it via the telegram application and a buzzer which functions to make a sound when the sensor detects water and the potential for flooding. The system will work automatically when water touches the sensor and sends flood detection status information via the telegram application. This system can be implemented in flood-prone areas. With this monitoring system, river water levels can be monitored and the risk of flooding can be reduced.

Effective Air Pollution Monitoring System for Eco Balance

Air pollution in urban areas is increasingly driven by vehicle emissions, which release harmful gases include such things as carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC), and particulate matter (PM). These pollutants pose significant risks to both environmental quality and public health, highlighting the urgent need for effective monitoring and control systems. This project presents the development of an automated Effective Air Pollution Monitoring System for Eco Balance, designed to detect exhaust gas levels in real-time as vehicles pass designated points. The system employs advanced sensors to monitor pollutant concentrations and utilizes a camera- based recognition system to capture the number plates of vehicles that exceed permissible emission levels. The collected data, including pollution metrics and vehicle details, is transmitted to the Regional Transport Office (RTO) for necessary enforcement actions. By promoting compliance with emission standards, this automated system aims to significantly reduce vehicle-related air pollution, fostering a cleaner and healthier urban environment

Dynamic Monitoring the Friction Transmission of Mine Hoist Using Triboelectric Nanogenerators Self‐Powered Sensor Based on Different Surface Structures Embedded in the Friction Lining

The friction lining is a critical component of the friction hoist, serving as the driving force for lifting through its interaction with the wire rope. Monitoring the friction state between the wire rope and the friction lining is crucial as it directly impacts lifting capacity, work efficiency, and overall safety. By employing finite element simulation and creating surface microstructures on triboelectric nanogenerators (TENG) using ultraviolet laser, this study demonstrats that microstructure can improve voltage output. Compared with TENG without morphology, the voltage is increased by nearly seven times, reaching 2.28 V. Moreover, experiments revealed that embedding TENG at the optimal standard point of the friction lining enables effective monitoring of friction transmission under varied conditions, with the voltage signal showing synchronization with friction force. Notably, the voltage reached 520 mV under increasing specific pressures and stabilized around 670 mV with rising sliding speeds. This research represents a significant step toward real‐time monitoring of intelligent mining systems by dynamically observing friction transmission in mine hoists.

Development of methods for monitoring and optimization of underground drainage systems using wireless sensor networks and ultra-wideband antennas

This research focuses on optimizing ultra-wideband (UWB) antennas, which are critical in modern communication systems due to their wide frequency range (3.1–10.6 GHz) and high data transmission capabilities. The study addresses the challenge of optimizing key antenna parameters – such as return loss, peak gain, and radiation efficiency – while also ensuring energy efficiency and network longevity. Traditional optimization methods, such as LEACH-C, often fail to balance these factors, leading to suboptimal performance. To solve this problem, the researchers developed the Generalized Position-based Optimization Neural Network (GPON) for UWB antenna optimization. They also evaluated the Position-based Hybrid Neural Network (PAN) method, comparing its performance with existing algorithms including LEACH-C, Firefly Algorithm (FA), HFAPSO, FA-ANN, and HWOABCA. The GPON model reduced return loss to 25.5 dB at 3.5 GHz and improved peak gain to 4.2 dB i, while maintaining 92 % radiation efficiency. In contrast, PAN demonstrated a 15–25 % improvement in residual energy and extended network lifetime by 20 % compared to LEACH-C. These improvements were due to the integration of advanced neural network techniques in GPON and the effective use of positional data in PAN, enabling more precise and adaptive optimization. The ability to balance multiple performance metrics simultaneously – a challenge previous models struggled with – is a key feature. This balance is crucial for UWB antennas in communication systems where both performance and energy efficiency are vital. The findings are especially relevant for practical applications in wireless sensor networks, mobile communications, and radar systems, requiring long-term network reliability and optimal antenna performance

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

Automatic Noise Monitor and Control in Library

Abstract: This paper introduces imperative to sustain a study-friendly atmosphere in libraries, as noise levels can greatly influence patron satisfaction. In order to reduce noise levels in libraries, this project suggests designing and implementing an autonomous noise monitoring and control system. The device constantly measures ambient noise levels by comparing them to predetermined thresholds using sound sensors and an Arduino. The technology reminds users to keep the environment quiet by sounding alerts and displaying visual cautions when acceptable limits are exceeded. To further reinforce silent norms, automatic audio announcements can be incorporated.

Numerical Analysis of Steel Frame Using Electro-Mechanical Impedance Technique

Nowadays, piezoelectric materials are most widely used in structural health monitoring (SHM) system for nondestructive evaluation. In this paper the damage study using electro-mechanical impedance (EMI) technique, which is nondestructive method of SHM. This paper presents a numerical analysis of four-story steel frame and multiple damages were induced in the steel frame by utilizing smart material like Lead Zirconate Titanate (PZT). A PZT-4 piezoelectric material was embedded in the beam of the frame. The type of damping used in steel frame model was Rayleigh damping. The numerical analysis was done in COMSOL MULTIPHYSICS 6.2 and frequency domain analysis was done, from that admittance values were find out for 30-300KHz frequency range. The real part of the admittance gives conductance values. Then, conductance verses frequency graph was created that will indicate the structural health and performance. Moreover, root mean square deviation (RMSD) index was found out for each case that provides a quantitative measure of deviation.

Industry Monitoring & Security System Using IoT

This paper presents an IoT-based industrial monitoring and security system using the NodeMCU ESP8266 and multiple sensors (MQ2, flame sensor, DHT11, LDR module, PZEM-004T) for real-time detection of hazards like gas leaks, fires, and electrical overloads. The system automates lighting and machinery control, sending alerts via Telegram for immediate response. It is cost-effective, scalable, and modular, ensuring reliable operation through a stabilized power supply, making it suitable for diverse industrial applications.

Raspberry Pi based IOT Weather Station

This paper presents a real-time system for monitoring and updating weather conditions online. The system tracks three key parameters: temperature, humidity, and rainfall. These values are displayed on an LCD screen and stored in a database. When the area is dry, the rainfall reading shows zero. If raindrops are detected, the rainfall value updates accordingly. As the temperature changes, it also reflects on the display. Users can check the weather status of a specific area from anywhere. We use an ARM-based Raspberry Pi 3 board, running the Raspbian operating system with a Linux kernel. The programming is done in Python, which is supported by the IDLE environment. This system provides users with a clear understanding of local weather conditions, making it a valuable tool for monitoring the climate in a specific area.

Non-Destructive Prediction of Rosmarinic Acid Content in Basil Plants Using a Portable Hyperspectral Imaging System and Ensemble Learning Algorithms

Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for the RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. The best RA prediction model was the LightGBM model with feature selection by the AdaBoost algorithm and spectral pre-processing through logarithmic transformation and second derivative. This model performed satisfactorily for practical screening with R2P = 0.81 and RMSEP = 3.92. From in-field HSI data, the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage.

Co-Designing in Cultural Tourism: TExTOUR ICT Services and Performance Monitoring System

The enhancement of cultural heritage for tourism can greatly benefit from the opportunities offered by Information and Communication Technologies (ICTs), promoting greater interaction and integration between all stakeholders and service providers. In recent years, ICT applications and platforms have tended to provide a platform to translate the strategic goals and performance metrics of cultural heritage tourism into comprehensive services tailored to stakeholders’ needs by participating stakeholders. A successful co-designed platform integrates explicit services—requiring specialized software tools—and implicit services, facilitating evaluation processes. This adaptability ensures continuous improvement and alignment with evolving strategies and business objectives. As part of the European project, the development of the TExTOUR open ICT platform marks a significant advancement in leveraging technology within cultural tourism. It exemplifies the creation of a sustainable and adaptable framework, co-designed specifically to support and amplify the strategic objectives of cultural heritage tourism. This approach not only fosters better engagement and management within the sector but also sets a new standard for innovation in cultural tourism practices.

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Enhancing Machining Efficiency: Real-Time Monitoring of Tool Wear with Acoustic Emission and STFT Techniques

Tool wear in machining is inevitable, and determining the precise moment to change the tool is challenging, as the tool transitions from the steady wear phase to the rapid wear phase, where wear accelerates significantly. If the tool is not replaced correctly, it can result in poor machining performance. On the other hand, changing the tool too early can lead to unnecessary downtime and increased tooling costs. This makes it critical to closely monitor tool wear and utilize predictive maintenance strategies, such as tool condition monitoring systems, to optimize tool life and maintain machining efficiency. Acoustic emission (AE) is a widely used technique for indirect monitoring. This study investigated the use of Short-Time Fourier Transform (STFT) for real-time monitoring of tool wear in machining AISI 4340 steel using carbide tools. The research aimed to identify specific wear mechanisms, such as abrasive and adhesive ones, through AE signals, providing deeper insights into the temporal evolution of these phenomena. Machining tests were conducted at various cutting speeds, feed rates, and depths of cut, utilizing uncoated and AlCrN-coated carbide tools. AE signals were acquired and analyzed using STFT to isolate wear-related signals from those associated with material deformation. The results showed that STFT effectively identified key frequencies related to wear, such as abrasive between 200 and 1000 kHz and crack propagation between 350 and 550 kHz, enabling a precise characterization of wear mechanisms. Comparative analysis of uncoated and coated tools revealed that AlCrN coatings reduced tool wear extending tool life, demonstrating superior performance in severe cutting conditions. The findings highlight the potential of STFT as a robust tool for monitoring tool wear in machining operations, offering valuable information to optimize tool maintenance and enhance machining efficiency.

Framework for Identification of Bridges in Need of Monitoring Based on Maintenance History

Recently, advanced Internet-of-Things (IoT) technology has been implemented in structural health monitoring to help increase safety of infrastructure and the reliability of maintenance decisions. In this context, preliminary studies are required to identify infrastructure in need of monitoring. This study establishes a basis for the identification of bridges that require monitoring and selects the bridges for measurement in order to build an efficient IoT-based monitoring system for small and medium bridges. The bridges selected based on various maintenance histories and risk factors are expected to be utilized in comprehensively assessing the characteristics of the overall bridge network.

Hybrid Duplex Medium Access Control Protocol for Tsunami Early Warning Systems in Underwater Networks

Tsunamis are devastating natural phenomena that cause extensive damage to both human life and infrastructure. To mitigate such impacts, tsunami early warning systems have been deployed globally. South Korea has also initiated a project to install a tsunami warning system to monitor its surrounding seas. To ensure reliable warning decisions, various types of data must be combined, but efficiently transmitting heterogeneous data poses a challenge due to the unique characteristics of underwater acoustic communication. Therefore, this paper proposes a Hybrid Duplex Medium Access Control (HDMAC) protocol designed for a tsunami warning system, with a specific focus on heterogeneous data transmission. HDMAC efficiently handles both seismic and environmental data by utilizing hybrid duplexing, which combines frequency duplex for seismic data with time duplex for environmental data. The protocol addresses the distinct transmission requirements for each data type by optimizing channel utilization through a group Automatic Repeat request (ARQ) scheme and packet size adjustment. Theoretical analysis predicts that HDMAC can achieve a channel utilization of up to 0.91 in smaller networks and 0.64 in larger networks. HDMAC is validated through simulations, and the simulation results closely match these predictions. The simulation results demonstrate the efficiency of HDMAC in supporting real-time submarine earthquake monitoring systems.

Indoor Air Quality Monitoring System

The awareness of the risks associated with contaminated indoor air quality is often lacking among individuals. This study employs a prototyping method, utilizing an Arduino Uno integrated with various sensors to monitor air quality. A sound buzzer alerts users when the sensor readings indicate hazardous conditions. Additionally, a Wi-Fi module connects the Arduino Uno to the internet, facilitating data uploads to the IoT platform ThingSpeak.