Network Technology Research Articles

ETSI ITS: A Comprehensive Overview of the Architecture, Challenges and Issues

Abstract: Intelligent Transportation Systems (ITS) have attracted the attention of developing nations because of their potential to enhance mobility and road safety, two issues that have become increasingly pressing in recent years. Information and Communication Technology (ICT) has accentuated every industry, and the automobile industry is no exception, allowing vehicles to communicate among themselves and the surrounding infrastructure for information exchange using Vehicle-to-Everything (V2X) communication. Vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-cloud (V2C) connections are made possible through the use of state-of-the-art networking technology. With EVehicles at the forefront, the vision of an ITS expands further. ITS is anticipated to offer wireless network-based services including vehicle occupant entertainment, traffic flow optimization, and accident prevention. Most nations have been trying to standardize the ITS architecture, and the European Union (EU) has been at the forefront by developing and standardizing the ETSI (European Telecommunications Standards Institute) architecture. As the most mature ITS architecture, this paper thoroughly explains the ETSI architecture in a single document for both the researcher's and newcomers' ease. Additionally, the challenges and issues pertinent to adopting and implementing the ITS ecosystem have also been discussed in detail.

Medium access control protocol based on time division multiple access scheme for wireless body area network

In recent years, the demand for wireless body area network (WBAN) technology has increased, driven by advancements in medical and healthcare applications. WBAN consists of small, low-power, and heterogeneous sensor devices attached inside or outside the body for continuous health monitoring. Medium access control (MAC) is pivotal in addressing WBAN challenges by ensuring reliability and energy efficiency under a dynamic environment caused by body movement. Therefore, to tackle these challenges, this paper presents a MAC protocol based on time division multiple access (TDMA) to enhance the WBAN performance. The proposed TDMA-MAC protocol employs a one-periodic scheduled-based access method to provide reliable data transmission while satisfying the WBAN requirements. The proposed protocol is compared to the IEEE 802.15.6 MAC, enhanced packet scheduling algorithm MAC (EPSA-MAC), and concurrent MAC (C-MAC) protocols based on the performance metrics of packet delivery ratio (PDR), network throughput, energy consumption, and average delay. The simulation results show that the TDMA-MAC protocol outperforms its competitors as it could achieve up to 98% PDR, 30% enhanced throughput, 30% energy optimization, and 20% improvement in average delay.

Description and analysis of Sigfox received signal strength indicator dataset by using statistical techniques

Low power wide area network (LPWAN) technology has expanded and is essential in the development of applications for the internet of things (IoT). The Sigfox LPWAN network is characterized by its long-range coverage, low cost and power consumption. In this article, a set of 5174 values is analyzed, containing 1606 null RSSI data, obtained with the Sipy module and MicroPython, which provide a coverage map of several points with a resolution of 200 meters deployed in Quito–Ecuador. It is evaluated the type of distribution to which the set of network measurements is adjusted and an optimal 900 MHz propagation model in suburban environments is determined from the measurements obtained from the known base station. As a result, the lost values of RSSI were predicted using the inverse normal distribution method in the original values, observing that they conform to a logistic distribution. The data from the base station were subjected to a data augmentation algorithm designed in MATLAB, determining that the stanford university interim (SUI) model reduces the precision error in the trend of the curve by not presenting changes greater than 5 dB, achieving a precision of 97% with respect to the fit of the curve of the data.

An efficient and low cost realization of LoRa based real-time forest protection system

The forest is a natural habitat for a variety of fauna and flora, and helps to maintain the ecosystem equilibrium. However, wildfire incidents and deforestation lead to forest degradation. Moreover, most of the existing methods, to preserve the forest resources, are ineffective due to their large establishment cost, more power consumption, and poor coverage. This paper brings out a sustainable solution by developing a forest protection system (FPS) that uses internet of things (IoT) technology together with long range (LoRa) communication. The work focuses on the development of an IoT framework for the detection of any intrusion into the forest as well as the detection of fire incidents in the vicinity of the equipment. Powering the equipment through solar energy makes the system cost-effective. The system is examined in terms of acquisition of data from sensor nodes pertaining to forest protection, relaying the same to the cloud using LoRa wide area network (LoRaWAN) technology and analyzing using cloud based visualization tools. The developed system has been deployed at Eturnagaram Wildlife Sanctuary, Mulugu district, Telangana, India for validation in the forest environment. The obtained results have shown that the system has an accuracy of 97.14% for intrusion detection and 100% for fire detection.

Neural network optimizer of proportional-integral-differential controller parameters

Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.

Advancement in Neural Network Technology for Precise Interpretation of Hand Gestures in Sign Language: A Systematic Review

Abstract: This systematic study investigates the latest developments in neural network technology for effectively understanding hand motions in sign language. It is based on a thorough investigation of 100 academic articles published between 2013 and 2020. Sign language recognition is essential for enabling effective communication and accessibility for those who have hearing problems. In the last ten years, many research articles have presented several neural network models that aim to accurately identify sign language motions. These models have been trained on various datasets, including the American Sign Language (ASL) alphabet and others. The main goal of this study is to conduct a comprehensive evaluation of the effectiveness of current neural network methods in understanding hand movements in sign language. This will be achieved by synthesizing information from reliable and reputable sources in the field. This paper provides a thorough analysis of model architectures, training datasets, assessment measures, and obstacles to provide a complete understanding of the current cutting-edge methodologies. It also highlights possible directions for future research. This research seeks to contribute to the growth of technology-driven solutions for improving communication accessibility and inclusiveness for those with hearing impairments by carefully examining the strengths and limits of neural network-based sign language interpretation systems.

LoRaWAN: The dilemma of optimal transmission parameter

Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRaWAN, have gained significant attention for their ability to facilitate long-range communication with low power consumption, making them ideal for Internet of Things (IoT) applications. However, achieving optimal performance in LoRaWAN systems requires careful selection of transmission parameters such as spreading factor, bandwidth, and coding rate. This paper addresses the dilemma faced by network designers and operators in determining the optimal transmission parameters for LoRaWAN deployments. We provide an overview of the key parameters and their impact on network performance, considering factors such as latency, packet delivery rate, and energy efficiency. Furthermore, we discuss the trade-offs involved in parameter selection. Through simulations, we evaluate the performance of different parameter configurations under various network conditions and provide insights into achieving the best balance between coverage, capacity, and energy consumption in LoRaWAN deployments. Findings from the study established that using high coding rate and bandwidth only provides minimal improvement in packet delivery rate particularly at large distances but at the cost energy consumption while spreading factor remains the most important transmission parameter in LoRa networks.

The poverty of sustainability: behavioral choices of drinking water in Kenya and Germany

Worldwide countries are engaged in technological improvements, changes in legislation, economic incentives, strengthened administrative structures, and political strategies to allocate and distribute water among different sections of the population. These macro measures treat water as an object of social, cultural, and environmental production unit for distribution and allocation, distancing it from the micro behavioral practices of drinking water. The poverty of sustainability stems from the gap between macro instrumental measures and the inner human dimensions that determine the micro practices. This paper explores the disconnect between macro sustainability measures and the micro practice of drinking water choice among individuals and their motives in the city of Nakuru in Kenya and the state of North Rhine-Westphalia in Germany. Both Kenya and Germany have developed top-down policies with a fix-it mindset. Kenya aims to provide drinking water to its population through piped network technology despite being a water-scarce region with inadequate housing and governance. In contrast, Germany, which is rich in water resources with highly regulated governance arrangements, spends billions treating water to attain drinking water quality only for most citizens to buy bottled water to drink. Despite water resources being finite, both of these nations source additional water through instrumental approaches to satisfy the demands of the materialistic world. In this context, the challenge of overcoming the poverty of sustainability remains a topic of debate that can be ameliorated by complementing scientific approaches with nonscientific knowledge and practices.

5G- Analysis and Characteristics in Comparison with Previous Generations

This paper explores the architecture and remarkable advantages of 5G technology in the current industry of wireless communication. The paper covers details of the recently developed 5th generation of networks and how its benefits overpower its drawbacks, majorly being its impact on health and environment. This study helps understanding 5G and highlights how this recent advancement has better chances of helping with wide area network connectivity, latency, economic growth, enhanced security features etc. While advancements in the cellular network technology continue to be one of the most important developments in the economic, telecom and healthcare sector among many others, many common beliefs about 5G which can be listed as its “drawbacks” have been brought to people’s attention recently, and with proper facts and studies, it can be easily proved how 5G, till this date, has no significant harm to any area, and simply has a few complexities which are natural with any advancement in technology. The paper covers the characteristics of the 5G network, and addresses common misconceptions and concerns surrounding health, security, and infrastructure.

Study of Fixed Point Message Scheduling Algorithm for In-Vehicle Ethernet

With the rapid development of advanced driver assistance systems (ADASs) and autonomous driving technology, in-vehicle networks are facing huge challenges in real-time operation and data loss. Traditional vehicle bus network systems such as LIN, CAN, and FlexRay are insufficient to meet the real-time requirements of intelligent connected vehicles. In-vehicle Ethernet meets the requirements of high reliability, low electromagnetic radiation, low power consumption, bandwidth allocation, low latency, and real-time synchronization of intelligent connected vehicles. In-vehicle Ethernet has become one of the trends in the next generation of in-vehicle network architecture. This research focuses on the delay problem existing in the real-time data transmission process of in-vehicle Ethernet, and innovatively proposes a fixed point message scheduling algorithm (FPMS) based on time-sensitive network (TSN) technology. By building an experimental platform based on the CANoe simulation tool, the high-efficiency message transmission performance of the fixed point message scheduling algorithm was verified. Experimental results show that the fixed point message scheduling algorithm proposed in this study improves message transmission efficiency by 66%, laying a solid foundation for improving the real-time and reliability performance of in-vehicle Ethernet.

Summary of Research on Highway Bridge Vehicle Force Identification

Vehicle force identification is one of the core technical problems to be solved urgently in the management of transportation infrastructure, and it has also been a research hotspot in recent years. To promote the application of vehicle force identification technology on bridges and explore its development direction, the development status of indirect vehicle force identification methods based on bridge response is reviewed during this study. The basic theories of two major methods, including bridge weigh-in-motion (BWIM) and moving force identification (MFI), are described in detail in this study, and then, the key technical principles of bridge force identification are revealed. Secondly, the development status of BWIM in recent years is reviewed from three aspects, including test accuracy, applicability and test efficiency. Combined with a variety of theories, the current status of MFI is analyzed from the establishment of the solution to the equation. Finally, the development direction of an artificial neural network and machine vision technology are prospected in this study. The BP neural network has good self-learning ability and self-adaptive ability, but the algorithm needs to be improved. The identification method based on machine vision represents the current development direction in vehicle force identification, with great potential.

Individual Intervention and Assessment of Students' Physical Fitness Based on the "Three Precision" Applet and Mixed Strategy Optimised CNN Networks

With the development of network technology and intelligent application platforms, the "Three Precision" applet as a method of individual intervention for students' physical fitness can not only enable students to obtain the improvement of physical fitness and lifelong sports habits, but also establish a new bridge of cooperation between home and school. The analysis method of student physical fitness individual intervention assessment is affected by a variety of factors such as the framework design of the WeChat applet platform and the subjectivity of the intervention, which leads to the inefficiency of the student physical fitness individual intervention assessment method. To address this problem, we analyse the mode and content of students' physical fitness individual intervention based on the "Three Precision" applet, extract the feature vectors of students' physical fitness individual intervention, construct a system of students' physical fitness individual intervention assessment indexes, and establish a method of students' physical fitness individual intervention assessment based on big data technology and WeChat applet by combining the mushroom propagation optimization algorithm and convolutional neural network. Individual intervention assessment method based on big data technology and WeChat applet. The effectiveness and robustness of the proposed method are verified by using the data recorded in the "Three Precision" applet as the input data of the model. The results show that the proposed method meets the real-time requirements and improves the prediction accuracy of the individual intervention assessment method, which significantly improves the efficiency of the individual intervention assessment of students' physical fitness.

Analysing the influencing factors of on-line studying engagement of preparatory international students: A case study of the science and technology Chinese course

With the rapid development of network technology, cross-regional on-line higher education is becoming one of the mainstream directions of distance education development. The effectiveness of on-line studying is significantly influenced by the level of learning engagement, and research on this topic can help learners by providing them with process-oriented learning support and targeted teaching interventions. Using the example of on-line Science and Technology Chinese Course for preparatory international students at four universities in China, this study collected self-reported data from 463 students using a survey method. The questionnaire was divided into two parts: the on-line studying engagement scale and the factors influencing on-line studying engagement. Descriptive statistical analysis and differential testing were conducted on the data using SPSS software, and the structural validity of the questionnaire was tested using AMOS software. With the assistance of Smart PLS, a model of the influencing factors of on-line studying engagement was constructed to explore the relationship between on-line studying engagement and its influencing factors. The study found that: The overall level of engagement in on line studying for preparatory international students is above average. In demographic factors, apart from gender, all other variables have a significant impact on on-line studying. Student-level factors will have a more significant positive impact on the degree of on-line studying engagement of preparatory international students. Among them, academic self-efficacy, basic psychological needs satisfaction, the difficulty of the on-line course, and completion of the assignment have a significant positive effect. The influence of teacher-level factors on behavioral engagement and emotional engagement is stronger than that of student-level factors. However, the emotional support of teacher-level factors, the on-line environment of environmental-level factors, and the perceived usefulness of technology of teacher-level factors have a certain negative impact on engagement in online studying. According to the research results, this paper puts forward the optimization strategies of accelerating the update frequency of Chinese learning materials, strengthening the input of on-line emotional support, and holding language learning activities with cultural characteristics. This study makes an in-depth analysis of the influencing factors of preparatory international students’ on-line Chinese studying engagement, and puts forward the optimization strategies to improve the quality of on-line studying, to provide theoretical and practical reference for the development of cross-regional on-line higher education.

Screening of drug targets for tuberculosis on the basis of transcription factor regulatory network and mRNA sequencing technology

Screening of drug targets for tuberculosis on the basis of transcription factor regulatory network and mRNA sequencing technology

Cyber-Physical Systems as a New Frontier for Economic Security: The Impact of Advanced Data Capturing and Networking Technology on National Economies

This study investigates the burgeoning interface between cyber-physical systems (CPS) and economic security, a vital yet underexplored domain. With the advent of sophisticated data capturing and networking technology, the paradigm within which national economies operate is shifting. This research delineates the ramifications of such technologies on economic stability, employment trends, and national gross domestic product (GDP), alongside the emergent cybersecurity implications. The methodology encompasses a cross-sectional analysis of various economies that have adopted CPS at differing scales and capacities. Through quantitative data, the study reveals trends and patterns in economic performance metrics post-adoption of advanced data capturing and networking infrastructures. Complementarily, qualitative assessments from expert interviews offer insights into the broader economic and security implications. Preliminary findings suggest that the integration of CPS correlates with a multifaceted impact on economic security, including the creation of new industries and the obsolescence of traditional sectors. A notable rise in efficiency and productivity across industries utilizing CPS is counterbalanced by the challenges of increased cybersecurity threats and job market disruptions. The study contributes to the discourse on economic security by framing CPS adoption not just as a technological upgrade but as a critical economic event with widespread and significant consequences.

Cost-Effective Optical Wireless Sensor Networks: Enhancing Detection of Sub-Pixel Transmitters in Camera-Based Communications.

In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera's utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies.

Comprehensive analysis on three-phase imbalance management technology of low-voltage distribution network

Energy production and use is one of the major sources of global greenhouse gas emissions. To combat climate change, many countries and organizations are pushing to reduce the use of fossil fuels, and the use of renewable energy sources such as solar, wind, and hydro is growing rapidly. The conversion of these renewable energy into electricity requires a large amount of power electronics. Power electronics have higher energy conversion efficiency, greater adjustability and control, and smaller size and weight than traditional generators. These characteristics make power electronic equipment have a wide range of application prospects in the field of energy conversion and power control. There may be cost, reliability, debugging, and three-phase imbalance issues. Based on the harm of three-phase imbalance to the economic operation and safe and stable operation of distribution network, the importance of three-phase imbalance in low-voltage distribution network is expounded, and the relationship between three-phase unbalance current and power factor is elaborated on the construction of three-phase imbalance. Reduce the harm of three-phase imbalance to the distribution network and reduce the loss of distribution network lines.

Disentangling composite influences of social connectivity and system interactivity on continuance intention in mobile short video applications: The pivotal moderation of user-perceived benefits

The expeditious proliferation of wireless network technology has caused the widespread integration of mobile short video apps into our daily routines, offering unprecedented opportunities for information dissemination and interactive communication. Despite extensive researches on the interactivity of mobile short video app, there has been a limited focus on understanding the underlying factors that contribute to users' perceived benefits and continuance intention. Leveraging insights from preceding studies, this study develops a conceptual research model that investigates how two distinct facets of interactivity, namely social interactivity and system interactivity, influence users' perceived benefits from mobile short video apps, ultimately impacting their intention to continue using them. This analysis rigorously scrutinizes data garnered from 808 users of mobile short video applications, employing structural equation modeling to validate the proposed hypotheses. The findings conclusively reveal that interactivity significantly affects users’ perceived benefits from mobile short video apps. Moreover, the study elucidates that perceived benefits from these apps are the significant predictors of continuance intention. Furthermore, interactivity could indirectly influence continuance intention via the mediation of perceived benefits. These findings possess the potential not only to augment our comprehension of perceived benefits and continuance intention from the interactivity perspective but also to furnish valuable insights for scholars in the field. The research sheds light on effective methodologies to encourage the adoption of mobile short video applications and to boost user engagement in a society oversaturated with mobile technology.

A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks.

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.

An Intelligent Detection System for Wheat Appearance Quality

In the realm of commercial trade, the appearance quality of wheat is a crucial metric for assessing its value and grading. Traditionally, evaluating wheat appearance quality is a manual process conducted by inspectors, which is time-consuming, laborious, and error-prone. In this research, we developed an intelligent detection system for wheat appearance quality, leveraging state-of-the-art neural network technology for the efficient and standardized assessment of wheat appearance quality. Our system was meticulously crafted, integrating high-performance hardware components and sophisticated software solutions. Central to its functionality is a detection model built upon multi-grained convolutional neural networks. This innovative setup allows for the swift and precise evaluation and categorization of wheat quality. Remarkably, our system achieved an exceptional overall recognition accuracy rate of 99.45% for wheat grain categories, boasting a recognition efficiency that was approximately five times faster than manual recognition processes. This groundbreaking system serves as a valuable tool for assisting inspectors, offering technical support for customs quarantine, grain reserves, and food safety.