Data Communication Research Articles

Development of a wireless smart sensor system and case study on lifting risk assessment

With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.

Performance evaluation framework of MQTT client libraries for IoT applications in manufacturing

The Internet of Things (IoT) is a revolutionary technology in the field of manufacturing that enables real-time data communication and control of various industrial sensors and devices. MQTT (Message Queuing Telemetry Transport) is one of the most popular communication protocols for IoT applications which offers lightweight, efficient, and reliable communication. There are several MQTT client libraries available for implementing MQTT communication for IoT applications. The selection of an appropriate client library can significantly impact the performance of an IoT application. This paper presents a performance evaluation framework for MQTT client libraries for IoT applications in the manufacturing industry. This paper aims to create a standardized virtual testing environment to compare the performance of different MQTT client libraries using various test conditions. It offers a structured and standardized methodology for evaluating performance aspects such as latency, scalability, and resource utilization. By systematically evaluating the performance of MQTT client libraries against these metrics, users can make informed decisions while selecting the most appropriate library for their IoT applications. The framework takes into consideration the unique requirements and constraints of manufacturing applications, such as the need for low-latency communication, secure data exchange, and compatibility with resource-constrained devices. Ultimately, this research contributes to the optimization of IoT applications in the manufacturing industry, facilitating the implementation of efficient, reliable, and scalable data communication.

Construction of Hydrogen Fuel Backup Power Supply System based on Data Communication Technology

The study presents a comprehensive examination of integrating hydrogen gas cells into backup electricity systems, strengthened by superior records verbal exchange technologies. This modern approach targets to address the growing interest for reliable and sustainable strength sources within the context of developing issues about environmental sustainability and the restrictions of traditional fossil gasoline-based totally power structures. On the middle of this studies is the improvement of a hydrogen gasoline cellular-based totally backup strength device. Hydrogen fuel cells, regarded for his or her excessive strength performance and low environmental impact, offer a promising opportunity to standard energy resources. The device leverages the inherent advantages of hydrogen as a clean strength carrier, making sure reduced carbon emissions and greater energy safety. A giant component of this have a look at is the combination of contemporary data communication generation. This integration facilitates real-time tracking and control of the electricity gadget, ensuring surest performance and reliability. Advanced statistics analytics are hired to are expecting energy demand, reveal gas cell health, and optimize the machine’s operation. This approach no longer handiest complements the performance of the energy supply but also ensures a unbroken transition between the number one energy supply and the backup device at some point of outages. The studies methodology encompasses a blend of theoretical analysis and realistic experimentation. Simulation models are used to test the device’s efficacy underneath numerous scenarios, followed via a prototype implementation to validate the theoretical findings. The look at also explores the monetary viability and scalability of the proposed machine, making it relevant for big adoption.

Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.

Technology-Based Interventions to Promote Diabetes Self-Management Behaviors for Persons Newly Diagnosed with Type 2 Diabetes: A Scoping Review.

Type 2 diabetes (T2D) management is complex and requires daily personal involvement and self-management skills to maintain optimal glycemic levels and improve health outcomes. Engagement in self-management behaviors in the early years of diagnosis can be challenging due to prevailing psychosocial factors present during this critical transition period, coupled with a lack of information, support, and skills. Technology-based diabetes self-management interventions can improve access to needed education and support, and their effectiveness in the general T2D population is well documented. This scoping review synthesized evidence on the use of technology for promoting diabetes self-management behaviors and related outcomes among individuals newly diagnosed with T2D (within the first 12months since diagnosis). Twenty-five studies were included. Technology-based diabetes self-management interventions tailored to those newly diagnosed with T2D have grown exponentially in the past five years. Existing evidence, though limited, showed that technologies such as websites, mobile apps, and continuous glucose monitoring combinedwith other communication features, can facilitate patient education, patient-provider communication, and health data monitoring. However, these technologies less commonly involved social support functions. These technologies have the potential to improve diabetes knowledge and positively impactclinical, behavioral, and psychological outcomes. However, small sample sizes, use of non-experimental designs, and the absence of formative research and theoretical foundations limit the strength ofexisting studies. Technology-based self-management interventions for those newly diagnosed with T2D show promise in improving T2D-related outcomes. Future studies should include larger sample sizes, adopt rigorous study designs, and integrate formative work to enhance relevance, adoption, and impact.

Design and research of serial communication system based on virtual instrument technology

Abstract In the measurement and automation control system, serial communication has been connected between the acquisition hardware (lower computer) and the upper computer between the easiest to realize and the most economical way. In view of the traditional instrument itself is relatively bulky, inconvenient to carry, this paper adopts the virtual instrument development environment to realize the realization of serial communication, in order to complete the subsequent data acquisition. When compared with other software development environment, LabWindows/CVI platform comes with a function library to make the program writing simple and flexible. In this paper, we designed the realization method of serial communication based on virtual instrument Labwindows/CVI environment, completed the design and realization of serial communication between PC and lower computer (microcontroller) based on Labwindows/CVI environment, and applied it to the example of serial communication with microcontroller to realize the data communication function. Practical results prove that the serial communication system based on virtual instrument technology has the advantages of friendly user interface, high stability, convenience and reliability, and can reliably and accurately complete the communication, and provide strong technical support for the system to follow up data acquisition.

Micro-milling digital twin for real-time tool condition monitoring

Micro-milling is a promising manufacturing process due to its wide material compatibility with work materials and ability to fabricate complex micro features. However, the monitoring of tool condition is challenging because of the stochastic wear behavior these micro tools exhibit, which can drastically change due to instances of tool chipping. This paper presents the development of a high-fidelity digital twin of a micro-milling machine. High throughput data communication over OPC UA was used to communicate instantaneous sensor data to a high-performance computer. Using a trained deep learning model, we demonstrate real-time tool condition prediction over OPC UA. The deep learning model used was found to be in good agreement with post-machining tool wear measurements. The presented work highlights the importance of developing sound machine communication protocols and lays the foundation for integrating machine learning with micro-milling in real-time.

Design and Implementation of a Ka-band Folding Rib Mesh Antenna for CubeSats

In the past decade, CubeSats have undergone a revolution, moving from university research projects to enabling industry opportunities and government missions. In 2014, the Jet Propulsion Laboratory, California Institute of Technology (JPL/Caltech) initiated a research and technology development effort to advance CubeSat communication capabilities. One of the critical thrusts was the Ka-band parabolic deployable antenna (KaPDA). This antenna started with the ambitious goal of fitting a 42 dB, 0.5 m, 35 GHz antenna in a 1.5U canister. At that time, there had been minimal development in high-gain CubeSat antennas, critical for high-data-rate communications and remote sensing science. A Ka-band high-gain antenna would provide a 10,000 times increase in data communication rates over an X-band patch antenna and a 100 times increase over state-of-the-art S-band parabolic antennas. This paper discusses designing, building, integrating, and operating the flight antenna from a mechanical perspective, its final performance, and lessons learned. KaPDA enabled the RainCube mission, the first Earth Science CubeSat to have an active instrument. RainCube was launched in May 2018, making KaPDA the second deployable parabolic antenna to fly on a CubeSat and the first to operate in Ka-band, enabling follow-on opportunities for high-rate antenna communications and remote sensing science.

Wide-sense stationary discrete-time random processes: Analyses in the discrete-Fresnel and discrete-frequency domains

This article investigates the expectation and autocorrelation functions of random processes in the discrete-Fresnel and the discrete-frequency domains, originating from wide-sense stationary (WSS) random processes in the discrete-time domain. Through mathematical deduction, it is demonstrated that WSS random processes in the discrete-time domain exhibit the same expectation and autocorrelation functions as transformed to the discrete-Fresnel domain. In contrast, this property is not observed in the discrete-frequency domain. White, pink, and brown random processes are considered to illustrate these behaviors in the discrete-Fresnel and discrete-frequency domains. Furthermore, this study numerically compares data communication systems based on single-carrier with cyclic prefix, orthogonal chirp-division multiplexing, and orthogonal frequency-division multiplexing schemes, which are respectively designed in the discrete-time, discrete-Fresnel, and discrete-frequency domains. The numerical results show that orthogonal chirp-division multiplexing and single-carrier with cyclic prefix schemes achieve comparable communication performance under any additive noise modeled by a wide-sense stationary random process, regardless of the chosen linear equalization or the absence of it.

SIMULASI JARINGAN VPN ROUTER DENGAN METODE LINK STATE ROUTING PROTOCOL DI SMK KORPRI MAJALENGKA

At this time the progress of information technology is growing so rapidly. The internet is a product of advances in information technology that can be accessed by anyone and anywhere. The increasingly sophisticated information technology helps a lot in human life today, but not a few also have a negative impact on the individual and group levels. Therefore, it is necessary to have a data and information security system. SMK Korpri Majalengka is an education provider institution in Majalengka Regency that has not carried out a data communication security system that is not optimal, such as confidential data that can still be accessed by just anyone. The agency needs a local network that has a wide range, cannot be accessed by just anyone, but only people who have access rights are connected to the local network so that agency security can be maintained properly. To solve this problem, a Virtual Private Network (VPN) network is needed. Therefore, the author intends to answer the above problems by simulating the "VPN Router Network with the Link State Routing Protocol Method" with the aim of maintaining the data and information security system at SMK Korpri Majalengka Regency.

A Multi-Method Approach to Assess the Adoption of Precision Agriculture Technology in Brazil

Precision Agriculture (PA) application aims to increase crop productivity while minimizing environmental impacts. We analyzed the topics most studied in the advancement of crop production in Brazil by applying the concepts of PA using the systematic literature review (SLR). A multi-method approach combined an SLR applying the PRISMA method and secondary data analysis. We found five clusters of technologies using the PA concept related to hardware development and four clusters related to applying technologies to software development in the PA concept. Most topics focused on using sensors to control water (soil and environment), soil electrical conductivity, and data communication. The focus on sustainability led researchers to reduce chemical products related to fertilizers and pesticides using Variable Rate Fertilizers (VRT) and reducing the environmental loading. According to the research results, it was evident that PA technology might help farmers make more accurate decisions about cultivation, production, harvest, and soil management. The availability of decision support systems powered by big data and artificial intelligence to select the best crop for a given season and soil might assist Brazil's sustainable growth of food production.

보육교사가 학부모와 소통에서 느끼는 어려움과 노력

This study tried to investigate the difficulties experienced in communication between childcare teachers and parents at early childhood education institutions and the efforts of teachers to overcome them. To this end, we conducted in-depth interviews from June 26 to September 31, 2023 with 10 care teachers currently working at early childhood education institutions and analyzed the data collected. The results of the study are as follows. First, childcare teachers had a lot of difficulties communicating with parents while working at an early childhood education institution. Second, care teachers made various efforts to communicate smoothly with their parents and made efforts to build trust with their parents. Third, care teachers wanted the director to act as a supporter and become a reliable person, and they wanted various support to protect teachers at a national level by demanding various educational opportunities. These results can be used as meaningful data for smooth communication between care teachers and parents at early childhood education institutions.

Teledentistry in the Time of Conflict in Sudan

Background: The War in Sudan between the Sudanese armed forces and rapid support forces has left undeniable impacts on the healthcare system which is already compromised by decades of neglect. Hospitals were bombed or occupied by conflict parties. Healthcare providers were killed or assaulted, citizens were forced to displace, and access to healthcare was impaired in many cities. Teledentistry is a modern and promising way to provide dental consultations. It is an easy, safe, and affordable solution for both patients and dental personnel in times like war. This short communication aims to urge the need to implement teledentistry to facilitate access to dental services during the current conflict in Sudan. Methods: This article discusses teledentistry, which according to the American Teledentistry Association (ADA) is the use of electronic information, imaging, and communication technologies, such as audio, video, and data communications for oral care delivery, diagnosis, consultation, treatment, and education. The healthcare system in Sudan is currently facing many challenges such as a lack of healthcare providers, economic crisis, inability to access dental health centers, and political instability. The application of teledentistry will facilitate access to oral healthcare and provide optimum solutions during the current conflict in Sudan. Conclusion: Implementation of teledentistry during military conflicts is a golden solution for both patients and dental care providers as it is a time-saving, cost-effective, safe, and quick way to access oral healthcare services.

ARCHITECTURAL AND CIRCUIT MODEL OF NAVIGATION DEVICE FOR ULTRA-LIGHT LAUNCH VEHICLE

The paper considered the architectural and circuitry model of a navigation device for an ultra-lightweight launch vehicle designed to improve the accuracy and reliability of positioning and orientation in flight conditions. The study utilized modern approaches to the integration of global navigation satellite system (GNSS) and inertial navigation system (INS) to achieve high accuracy and stability of the system. The key components of the device including UM980 module, LSM6DSV32X inertial measurement module, STM32 F7 series microcontroller, E220-400T30S radio module, LMR50410XDBVR converter based power supply and RS422 interface were described in detail. The UM980 module, including SAW and LNA filter blocks, demonstrated the ability to efficiently process incoming RF signals. The NebulasIV main function block combined GNSS BB, GNSS RF, interfaces and power management unit (PMU) modules, supporting multiple communication interfaces such as UART, SPI and I²C, as well as RTK and PPS signals. The study analyzed the integration and accuracy aspects of the module in navigation systems. The signal processing scheme of the inertial navigation system module was also presented, including digital filters, free-fall and activity/inactivity detection functions, and interfaces for data communication. User bias functions and filter modes are considered to ensure the accuracy and stability of the navigation device. The work lays the groundwork for further improvements in navigation technology, especially in the context of increasing availability and accuracy for small scale space launches.

Analyzing Resampling Techniques for Addressing the Class Imbalance in NIDS using SVM with Random Forest Feature Selection

The purpose of Network Intrusion Detection Systems (NIDS) is to ensure and protect computer networks from harmful actions. A major concern in NIDS development is the class imbalance problem, i.e., normal traffic dominates the communication data plane more than intrusion attempts. Such a state of affairs can pose certain hazards to the effectiveness of detection algorithms, including those useful for detecting less frequent but still highly dangerous intrusions. This paper aims to utilize resampling techniques to tackle this problem of class imbalance in NIDS using a Support Vector Machine (SVM) classifier alongside utilizing features selected by Random Forest to improve the feature subset selection process. The analysis highlights the combativeness of each sampling method, offering insights into their efficiency and practicality for real-world applications. Four resampling techniques are analyzed. Such techniques include Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling (RUS), Random Over-sampling (ROS) and SMOTE with two different combinations i.e., RUS SMOTE and RUS ROS. Feature selection was done using Random Forest, which was improved by Bayesian methods to create subsets of features with feature rankings determined by Cumulative Feature Importance Score (CFIS). The CIDDS-2017 dataset is used for the performance evaluation, and the metrics used include accuracy, precision, recall, F-measure and CPU time. The algorithm that performs best overall in the CFIS feature subsets is SMOTE, and the features that give the best result are selected at the 90% level with 25 features. This subset accomplishes a relative accuracy enhancement of 0.08% than the other approaches. The RUS+ROS technique is also fine but somehow slower than SMOTE. On the other hand, RUS+SMOTE shows relatively poor results although it consumes less time in terms of computational time compared to other methods, giving about 50% of the performance shown by the other methods. This paper's novelty is adapting the RUS method as a standalone test for screening new and potentially contaminated datasets. The standalone RUS method is more efficient in terms of computations; the algorithm returned the best result of 98.13% accuracy at 85% at the CFIS level of 34 features with a computation time of 137.812 s. It is also noted that SMOTE is considered to be proficient among all resampling techniques used for handling the problem of class imbalance in NIDS, vice 90% CFIS feature subset. Future research directions could include using these techniques in different data sets and other machine learning and deep learning methods together with ROC curve analysis to provide useful pointers to NIDS designers on how to select the right data mining tools and strategies for their projects.

A novel secure privacy-preserving data sharing model with deep-based key generation on the blockchain network in the cloud

Cloud computing is currently emerging as a developing technology in which a Cloud Service Provider (CSP) is a third-party organization that provides effective storage of data and facilities to a large client base. Saving information in a cloud offers users the satisfaction of accessing it without the need for direct knowledge of the distribution and management of an infrastructure. The primary objective is to develop a novel, secure, and privacy-preserving data-sharing model that utilizes deep-based key generation on blockchain in the cloud. Data communication is done using multiple entities. The research aims to develop a collaborative data-sharing method in the cloud for the authentication scheme for cloud security on blockchain and smart contracts. Initialization, registration, key generation, authentication of data sharing, and validation are carried out here. The proposed data-sharing model involves a revenue distribution model that depends on Multiple Services (MS) models to improve multiple cloud services. The security parameters namely passwords, hashing functions, key interpolation, and encryption are used for preserving the Data privacy and here the SpinalNet is used for generating keys. Furthermore, the devised SpinalNet_Genkey obtained a value of 45.001 MB, 0.002, and 0.003 sec for memory usage, revenue, and computation cost.

Critical elements of successful partnership in public health-A case study.

Intersectoral action is integral in promoting population health and health equity. In South Australia, public health partnership authorities (PHPAs) are one mechanism to promote intersectoral collaboration for addressing complex public health issues. This case study examined contemporary evidence on the critical elements of successful partnerships, explored the role of formalisation in success, and examined how identified critical elements were operationalised over time in an existing PHPA. In the first phase, a scoping review was conducted. The second phase was a retrospective longitudinal case study of a government-academic PHPA agreement, involving partnership document review and key informant interviews. Data triangulation and iterative thematic analysis were performed. The critical elements of successful partnership, triangulated through multiple data sources, are mutuality, governance, communication, resources, leadership, and interactions and relationships. These elements have a high degree of interdependence. Trust supports and is reinforced by these elements, particularly during times of change or crisis. Formalisation of a partnership supports success in many ways but is insufficient on its own. Operating in dynamic environments, a partnership requires dedicated time and effort to be successful. Attention to the strength and interconnectedness of the critical elements over the course of a partnership is crucial. SO WHAT?: This case study describes how the elements of successful partnership can be applied to progress beyond transactional relationships towards authentic collaboration and overcome cross-border and cross-boundary public health challenges to promote population health and well-being.

Artificial Intelligence Adoption and Project Success: A Mixed-Method Study

AI's growing acceptance is changing project management's human-centric approach. Project management is using AI to automate and support duties. This change could improve workflows, decision-making, and project efficiency. The full influence of AI on project success is unknown. There is little empirical evidence linking AI use to project outcomes. This ignorance highlights the necessity to study AI's impact on project management. The project management AI industry is expected to expand 38% annually. Since the late 1980s, AI has improved project management by providing more intelligent and autonomous help. Data privacy, accountability, strategic leadership, communication, innovation, and emotional intelligence are important ethical issues. This study examines how AI adoption affects project success through communication and feedback. This mixed-method study examines how AI adoption affects project success. The quantitative phase measured AI communication, feedback, and project progress via a predefined questionnaire. The sample includes construction, IT, manufacturing, healthcare, and finance project managers and team members. Multiple regression analysis and structural equation modelling were employed in IBM SPSS AMOS to examine AI adoption and project success measures. A qualitative phase of semi-structured interviews with respondents contextualised the quantitative data. Thematic analysis gleaned insights from interview transcripts. AI's impact on project success was examined using integrated data, with ethics in mind. The study examined AI tool-project success relationships using a structural equation model. Communication mode, feedback style, and frequency explained 3% of project success variance. Quantitative research showed that AI communication frequency improves project success, whereas mode and style negatively impact it. Participants' qualitative comments indicated six themes that match quantitative findings, and their replies enhance quantitative results and recommend improvements. The study concluded that AI communication frequency positively increases project success, while mode and style negatively affect it. The mixed-methods approach showed that AI tools alone cannot ensure project success; communication style and frequency are. The study recommended among others that organisations should integrate AI tools into project management systems, match AI communication modes to project team preferences, optimise feedback styles, and provide regular updates to improve AI communication. Project teams need ongoing training.

Detecting dynamic interaction of multiple subjects in policy information communication in China

PurposeAs an important part of national governance, the online communication of education policies usually attracts the attention of many subjects, including the public and the media. Existing research mainly focuses on analysing communication behaviour of a single subject. However, with the rapid development of social media, policy information communication is often accompanied by the participation of multiple subjects and forms diversified communication behaviours and interaction patterns. The comprehensive identification of multiple subjects and their interactions can accurately depict the communication process and effectively support the efficient communication of policies. Therefore, this paper aims to conduct fine-grained analysis on the multiple subjects in information communication of the education policy.Design/methodology/approachThis paper explored the communication and interaction process of the education policy via multidimensional analysis. Specifically, the authors firstly obtained multi-source communication data to identify key communication subjects. Secondly, the authors mined the communication contents generated by communication subjects to measure the diversified correlations between subjects. Finally, the authors depicted the interaction of subjects in policy information communication.FindingsThe experimental results reveal that there are multiple key subjects in the policy information communication, and the communication roles of the subjects change with the communication process, including dominance role, one-way or two-way effect role. This further indicates the need to allocate resources dynamically in the process of policy communication.Originality/valueAnalysing the process of policy communication and identifying the dynamic interaction between communication subjects can provide more a comprehensive and detailed decision-making basis for policy formulation and implementation. In addition, the research ideas and methods presented in this paper expand the perspective of information communication research.

Automatic detection of problem-gambling signs from online texts using large language models.

Problem gambling is a major public health concern and is associated with profound psychological distress and economic problems. There are numerous gambling communities on the internet where users exchange information about games, gambling tactics, as well as gambling-related problems. Individuals exhibiting higher levels of problem gambling engage more in such communities. Online gambling communities may provide insights into problem-gambling behaviour. Using data scraped from a major German gambling discussion board, we fine-tuned a large language model, specifically a Bidirectional Encoder Representations from Transformers (BERT) model, to predict signs of problem-gambling from forum posts. Training data were generated by manual annotation and by taking into account diagnostic criteria and gambling-related cognitive distortions. Using cross-validation, our models achieved a precision of 0.95 and F1 score of 0.71, demonstrating that satisfactory classification performance can be achieved by generating high-quality training material through manual annotation based on diagnostic criteria. The current study confirms that a BERT-based model can be reliably used on small data sets and to detect signatures of problem gambling in online communication data. Such computational approaches may have potential for the detection of changes in problem-gambling prevalence among online users.