Network Communication Technology Research Articles

Sustainable Agriculture with Self-Powered Wireless Sensing

Agricultural sustainability is becoming more and more important for human health. Wireless sensing technology could provide smart monitoring in real time for different parameters in planting, breeding, and the food supply chain with advanced sensors such as flexible sensors; wireless communication networks such as third-, fourth-, or fifth-generation (3G, 4G, or 5G) mobile communication technology networks; and artificial intelligence (AI) models. Many sustainable, natural, renewable, and recycled facility energies such as light, wind, water, heat, acoustic, radio frequency (RF), and microbe energies that exist in actual agricultural systems could be harvested by advanced self-powered technologies and devices using solar cells, electromagnetic generators (EMGs), thermoelectric generators (TEGs), piezoelectric generators (PZGs), triboelectric nanogenerators (TENGs), or microbial full cells (MFCs). Sustainable energy harvesting to the maximum extent possible could lead to the creation of sustainable self-powered wireless sensing devices, reduce carbon emissions, and result in the implementation of precision smart monitoring, management, and decision making for agricultural production. Therefore, this article suggests that proposing and developing a self-powered wireless sensing system for sustainable agriculture (SAS) would be an effective way to improve smart agriculture production efficiency while achieving green and sustainable agriculture and, finally, ensuring food quality and safety and human health.

Internet of Things Technology for an Autonomous Photovoltaic Solar Energy System Monitoring

This work presents a Photovoltaic (PV) monitoring system using the Internet of Things (IoT). It provides a detailed overview of the key steps involved in designing the IoT-based PV monitoring system, examining the specific architectural components, including different system layers, potential software and hardware platforms, and communication network technologies used in the IoT-integrated PV system. Additionally, it discusses the existing challenges and issues of IoT-based PV monitoring systems. Furthermore, it proposes a low-cost monitoring system designed to monitor a PV system consisting of a PV module, a DC-DC boost converter, and a load.

A New English Education Model Based on 6G and Sliced Network Virtual Reality Platform

ABSTRACTThe information society has led to a shift in traditional English education methods, with the evolution of technology, particularly internet and communication network technologies, reshaping the teaching landscape. This facilitated innovative instructional approaches and enhanced the learning experience. This research introduces a novel virtual learn net architecture (VLNA) within the 6G network layers, which processes the performance of the virtual reality‐based English education system (VR‐EES) model to provide a seamless, personalized learning experience for online learners. This architecture is structured into several layers: The user equipment (UE) layer connects VR headsets to the network with ultrareliable, low‐latency links; the radio access network (RAN) layer, employing massive MIMO and beam forming, enhances connection speed, capacity, and coverage. Edge computing handles latency‐sensitive tasks like speech recognition and adaptive content delivery, reducing the load on the core network. The core network layer (CLN) manages network slices for specific learning tasks such as real‐time interaction, high‐definition multimedia, and computation‐intensive processes, with control plane and user plane separation (CUPS) optimizing network management and security through end‐to‐end encryption. Software‐defined networking (SDN) and network function virtualization (NFV) provide centralized, dynamic control, allowing real‐time resource allocation based on demand. Cloud‐edge integration supports Artificial intelligence (AI)‐driven adaptive learning, optimizing educational content delivery based on individual progress. The study results demonstrate that stimulation of VLNA achieved significant improvements in latency reduction, bandwidth utilization, throughput, packet loss rate, jitter, user engagement, learning efficiency, and user satisfaction. The integration of edge computing and network slicing led to a significant reduction in latency, while the enhanced throughput enabled seamless VR experiences. In this study, latency reduction, bandwidth utilization, and user satisfaction emerge as the most significant factors, with user satisfaction standing out as the top performer due to its substantial impact on enhancing the overall learning experience. The packet loss rate is maintained to a certain level, ensuring reliable data transmission. The VR‐EES model's experimental results also enhanced visual learning, multimedia quality, user pleasure, learning effectiveness, and user engagement.

Delay/Disruption Tolerant Networking Performance Characterization in Cislunar Relay Communication Architecture.

Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component of 7G/8G networks. Therefore, space networks will eventually integrate with V2X communication networks, with both space vehicles (or spacecrafts) and terrestrial vehicles involved. DTN is the only candidate networking technology for future heterogeneous space communication networks. In this work, we study possible concatenations of different DTN convergence layer protocol adapters (CLAs) over a cislunar relay communication architecture. We present a performance characterization of the concatenations of different CLAs and the associated data transport protocols in an experimental manner. The performance of different concatenations is compared over a typical primary and secondary cislunar relay architecture. The intent is to find out which network relay path and DTN protocol configuration has the best performance over the end-to-end cislunar path.

Intelligent Logistics Warehousing Strategy Based on 5G Network and Cloud Computing in the Era of Big Data

ABSTRACTBased on the resource base of “cloud computing,” a super big data storage center was built to exchange data between companies, units, departments and businesses, management, customers and personnel, so as to ensure the timely and accurate reception of all aspects of information. Using radio frequency identification technology, barcode technology, GPS/GIS (Global Positioning System/Geographic Information System) technology, mobile communication technology, and 5G (5th Generation Mobile Communication Technology) network technology, a new business model was realized. At the same time, SOA (Service Oriented Architecture) structure was adopted to combine decentralized business and SOA architecture in the form of loose coupling, and dynamic binding was adopted to realize dynamic access of cloud logistics warehousing platform. Massive data were collected, analyzed, and preprocessed through big data technology and logistics facilities. Real‐time data transmission was realized through 5G network. At the same time, scene acquisition, modeling, analysis, control, feedback, and other technologies were applied to the logistics system to realize the intellectualization of logistics warehousing services. The receiving and sending business process of B2C (Business‐to‐Consumer) e‐commerce enterprises based on “cloud logistics warehousing” is to take the terminal shipper as the seller and the terminal consignee as the buyer. By integrating the information of all links in the logistics process into the “cloud” platform, various stakeholders can conduct information, capital, and other transactions on this platform, thus ensuring the quality of customer logistics services. The advantage of recognized logistics cloud warehouse is that its application value accounts for 43.1%. The scheme proposed in this paper can shorten the storage time of the warehouse, thus speeding up the operation of the warehouse; it can ensure the quality of each link, thus reducing the operating costs of the warehouse; it can increase the use space of the warehouse and save the manpower of the warehouse; it can improve the operation efficiency of the warehouse, thus improving the economic benefits.

Beyond two-octave coherent OAM supercontinuum generation in air-core Ge-doped ring fiber

Orbital angular momentum (OAM) has emerged as a revolutionary technology for communication networks due to its ability to significantly increase the channel capacity. However, traditional optical fibers present significant hurdles to harnessing OAM’s full potential, including dispersion and limited bandwidth, which facilitates investigations on supercontinuum (SC) generation for OAM beams. In this paper, an air-core Ge-doped ring fiber is proposed and designed to support high-order OAM mode up to |l| = 24. To achieve this, the fiber has a high refractive index difference between the ring core and the cladding, enabling stable transmission of high-order OAM modes. A key feature of this design is the OAM24,1 mode, which exhibits near-zero and flat dispersion. This characteristic translates into high coherence and a remarkably broad SC generation. The generated SC spans over two octaves (2336 nm) within the infrared wavelength range (764 nm to 3100 nm) at a power level of −40 dB. Furthermore, by optimizing the structural parameters, we ensure near-zero and flat dispersion characteristics for the other OAM modes (|l| < 24), along with broad SC generation exceeding two octaves. This fiber design holds significant promise for future advancements in OAM beam transmission within the infrared spectrum.

GSADDQN: Combining GraphSAGE and reinforcement learning for routing optimization in software-defined optical transport network

As a result of rapid development of network communication technology, optical transport network (OTN) traffic has also experienced rapid growth in terms of information volume scale, traffic complexity, and spatiotemporal distribution dynamics. Because the OTN traffic demand has complex spatiotemporal fluctuations, the traditional deep reinforcement learning (DRL) algorithm has been applied to the routing optimization of software-defined OTNs. However, the traditional DRL algorithm has problems such as slow convergence, weak generalization ability, and load imbalance when performing routine tasks. To address these issues, we propose a graph sampling and aggregation (GraphSAGE)-based dueling deep Q-network (GSADDQN) algorithm for software-defined OTN routing optimization. First, we design a DRL-based routing decision model to find the best routing strategy for each optical network’s source–destination traffic demand. Second, considering the sparse connection characteristics of optical network nodes, we use sampling neighbors and a deep aggregation mechanism as the neural network model of the Dueling Deep Q-Network (Dueling DQN) algorithm so that the reinforcement learning agent can consciously aggregate important network information and improve the model’s convergence performance and generalization ability. Finally, we design simulation routing experiments based on Gym and evaluate the algorithm’s load balancing and generalization capabilities for different network topologies. The experimental results show that the GSADDQN algorithm has good convergence performance and load balancing ability in routing optimization of optical transmission networks and can generalize new network structures, maintaining good decision-making ability even during network node failures.

Design and Optimization of Higher Education Online English Teaching Utilizing Wireless Network Technology in the Context of 5G

The global demand for wireless communication networks is a result of the widespread use of wireless devices and the related surge of wireless service providers. Data transmission speed, coverage areas, cost-effectiveness, resource utilization, security, scalability, and adaptability are all elements expected to be improved in future 5G wireless networks. Since this rapid development of network communication technology affects the delivery of English education in tertiary contexts, English teachers in colleges and universities should make use of network resources—especially the communication tools embedded in the 5G network. File transfer protocol can utilize the advanced artificial bee colony optimization algorithm (AABCOA) to realize classification, and the system proposed in this article is simulated and tested in Matrix Laboratory (MATLAB). The research results indicate that students are unsatisfied with traditional methods of English pedagogy in higher education and are keen to explore the new learning possibilities provided by 5G integrated wireless network technology.

Aspiring and Deprecating Female Influencers: Communicative Capitalism and the Positioning of Women Entrepreneurs

Given the relative ease of joining an e-commerce market via social media platforms, expectations have risen about the role of communication technology and social networks in advancing gender equality in economic participation. South Korean women influencers, in particular, are selling various items using their popularity on social networks. They represent themselves as successful, passionate, gorgeous businesswomen with glamorous lifestyles. These individual influencers exemplify neoliberal ideals, branding and marketing themselves as independent and autonomous creators able to fashion their own destinies in the endless competition of the market. Recently however, heated deinfluencing trends against them have grown in strength and intensity, deprecating these women as nonprofessional, mass-deceptive online vendors. It has become common to describe them with the derogatory term “palyi people.” Against this backdrop, the current study questions the ability of digitalized online technology to empower women coming along with the ideas of neoliberalism and postfeminism. Using the concept of communicative capitalism, this study will analyze the images and texts created and circulated within the social media sphere by six popular female influencers and their followers. Through this data, this study aims to grasp how the free communicative activities of female influencers and their followers on social media negatively position women entrepreneurs in the techno-cultural era.

Start Time Planning for Cyclic Queuing and Forwarding in Time-Sensitive Networks

Time-sensitive networking (TSN) is a kind of network communication technology applied in fields such as industrial internet and intelligent transportation, capable of meeting the application requirements for precise time synchronization and low-latency deterministic forwarding. In TSN, cyclic queuing and forwarding (CQF) is a traffic shaping mechanism that has been extensively discussed in the recent literature, which allows the delay of time-triggered (TT) flow to be definite and easily calculable. In this paper, two algorithms are designed to tackle the start time planning issue with the CQF mechanism, namely the flow–path–offset joint scheduling (FPOJS) algorithm and congestion-aware scheduling algorithm, to improve the scheduling success ratio of TT flows. The FPOJS algorithm, which adopts a novel scheduling object—a combination of flow, path, and offset—implements scheduling in descending order of a well-designed priority that considers the resource capacity and resource requirements of ports. The congestion-aware scheduling algorithm identifies and optimizes congested ports during scheduling and substantially improves the scheduling success ratio by dynamically configuring port resources. The experimental results demonstrate that the FPOJS algorithm achieves a 39% improvement in the scheduling success ratio over the naive algorithm, 13% over the Tabu-ITP algorithm, and 10% over the MSS algorithm. Moreover, the algorithm exhibits a higher scheduling success ratio under large-scale TSN.

Instagram vixens: the racialized sexual scripts of erotic labor online

ABSTRACT Despite growing interest in the ways information and communication technologies shape labor, few studies have accounted for the racialized sexualization of erotic labor. This study examines how Black women exotic dancers deploy racialized sexual scripts in the content they generate on the mobile social networking application Instagram. As erotic influencers, these women engage in multifaceted and multimodal erotic self-presentations both within and beyond the strip club. This article presents data from a digital ethnography of 73 Black female exotic dancers located in the southeastern United States, as well as a content analysis of the digital media files they created on Instagram. The findings suggest four distinct sexual scripts that these Instagram users perform: virtual cover girl, erotic professional, boudoir baddie, and assets model. Findings demonstrate Black women use smartphone and social networking technological features to create presentations of femininity, desirability, and sensuality that simultaneously subvert and adhere to the controlling images that define Western ideologies on race and sexuality. Ultimately, this article argues for the need to account for the social and cultural contexts in which the affordances of networked communication technologies that permeate contemporary erotic labor operate.

Implementación de un Dispositivo Inteligente para la Asistencia de Personas con Discapacidad Visual en Entornos Universitarios

This project arises in response to the growing global need for inclusive technological solutions aimed at improving the quality of life of people with visual impairments. The motivation is based on addressing the specific barriers these individuals face, with a particular focus on university environments. The methodology adopted focuses on the convergence of web services and networked communication technologies, seeking to promote the autonomy and safety of visually impaired people. A distinctive aspect of this project is the implementation of low power consumption and long-range technology, marking a pioneering approach in this field, which not only addresses the immediate need for inclusive solutions; but contributes to technological advancement by introducing efficient and long-range solutions. Through testing, very short response times are achieved in terms of identifying dangerous movements such as falls and detecting nearby objects, while character recognition still requires further refinement. The key innovation lies in the effective convergence of technologies, setting a new standard for inclusive and sustainable solutions. In other words, this project not only responds to a pressing need, but also marks a significant milestone in improving inclusion for the visually impaired, highlighting its relevance at the local level and contributing to global progress in this crucial field.

Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life, but it also increases the security problem. Information security has become one of the severe challenges faced by people in the digital age. Currently, the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology, the risk of large-scale data transmission, the potential vulnerabilities introduced by emerging technology, and the dilemma of user identity verification. This paper analyzes the frontier challenges of communication technology and computer network security, and puts forward corresponding solutions, hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

Multi-Path Data Transmission System Based on 5G Communication Technology

Due to the current problems of 5G(5th Generation Mobile Communication Technology) networks such as discontinuous coverage, poor coverage performance, and insufficient uplink rate, the real-time transmission performance of 5G networks has been greatly affected, and cannot meet the growing needs of high-speed communication and applications based on the Internet of Things. This paper aims to improve the transmission effect of 5G communication technology by improving the algorithm. By analyzing the problems of existing 5G bandwidth estimation algorithm, this paper proposes a Westwood-based coupling adaptive bandwidth estimation algorithm, and describes the algorithm ideas involved in the algorithm point-by-point. Moreover, this paper uses adaptive bandwidth estimation algorithm to update the slow start threshold in the fast retransmission phase, which makes the congestion window size of path transmission more reasonable. Simulation results show that the congestion control algorithm proposed in this paper improves the accuracy of link bandwidth estimation to a certain extent, improves the link congestion situation and improves the throughput of multipath transmission links. This article can be applied to the algorithm in multipath data transmission to further improve data processing efficiency and processing speed.Therefore, the improved method in this paper can be used in the practice of 5G communication to further improve the efficiency of 5G communication.

A comparative analysis of diversity combining techniques for repetitive transmissions in time spreading SCMA systems

AbstractSparse Code Multiple Access (SCMA) is a recently introduced wireless communication network technology. There are various techniques in SCMA systems to increase the system's efficiency, and one of these techniques is time spreading. By adding repetitive transmission and time spreading into SCMA, it is shown in previous works that the Bit‐Error‐Rate (BER) results are improved convincingly. However, in the previous works, other diversity combining techniques have not been considered. This paper introduces a new approach to further improve the performance of repetitive transmission in SCMA systems with time spreading by adding imperialist competitive algorithm in diversity combining. Alongside, four different combining techniques; equal gain combining, maximal ratio combining, selection combining, and genetic algorithm are considered to bring comparative analysis to show the significance of the new technique. Results show that the proposed method offers up to 2.3 dB gain in terms of BER, under certain conditions.

IRS-assisted UAV wireless powered communication network for sustainable federated learning

Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are indispensable potential technologies in the future sixth-generation mobile communication technology (6 G). Utilizing the high beamforming gain of IRS and the high mobility of UAV can achieve ubiquitous network coverage and ensure a high-quality communication environment. Wireless Powered Communication Network (WPCN) is an emerging green communication technology network that converts received radio frequency (RF) signals into electrical energy to power Federated Learning (FL) users. FL users perform local computing and model transmission through the collected energy, ensuring the sustainability of FL. In order to solve the problems of complex communication environment, privacy protection, and energy constraints on terminal devices, we design an FL system based on an IRS-assisted UAV wireless power communication network, which minimizes the UAV transmission energy by jointly optimizing UAV location, IRS phase shift, and resource allocation strategies. We use a low-complexity iterative algorithm to solve this complex non-convex problem. The simulation results show that the performance of the proposed algorithm is obviously better than that of other benchmark schemes, indicating that joint optimization plays an essential role in improving the performance of the system.

The simulation of wireless body temperature monitoring system based on Internet of Things

Traditional body temperature monitoring technology has some problems, such as inaccurate manual reading, unable to monitor continuously for a long time, and difficult data storage and analysis. To solve these problems, this paper designs an application scenario of Internet of Things in body temperature monitoring, which integrates Internet of Things, network communication, network programming and other technologies, designs the overall architecture, topology structure and IP address allocation scheme of the system, and realizes real-time acquisition, transmission, storage, analysis, and display functions based on the Packet Tracer simulation platform. The performance evaluation and optimization are also carried out. The system realizes wireless monitoring and analysis of the body temperature data collected by the temperature sensor through the home gateway and single chip microcomputer and other devices. After testing, the designed system is stable, which provides a solution for the application of Internet of Things in body temperature monitoring.

Long-distance Tele-robotic-assisted Percutaneous Coronary Intervention: A Case Report of First-in-patient in China

Robotic-assisted percutaneous coronary intervention (R-PCI) is an innovative way of performing percutaneous coronary intervention (PCI) whereby the operator can manipulate coronary intraluminal guidewires and catheter devices by using remotely controlled technology. Performing tele-R-PCI from a remote location via fifth generation network communication technology has never been reported in China; however, if this were possible, the technique could be used to treat many patients with coronary artery disease who would otherwise not have the opportunity of treatment. The case of a 73-year-old male patient with coronary artery disease who underwent successful tele-R-PCI at 800 km from the operators is presented. Performing long-distance tele-R-PCI in patients with coronary artery disease is feasible with predictably successful outcomes when reliable network connectivity and local cardiac catheterization facilities are present.

Young people’s ‘post-digital’ relationships during COVID-19 ‘lockdowns’ in England

The lockdown imposed in England in response to the COVID-19 pandemic involved an unprecedented ‘shift to digital’, including in relationships between non-cohabiting individuals. This article examines young people’s perspectives on and experiences of using networked communication technologies (NCTs) in romantic relationships during lockdown, based on 14 focus groups (n = 80) and interviews (n = 38) conducted with young people in England during 2021–2022. Using critical realist theory, we identify interplays between lockdown as a condition, NCT affordances and wider norms, meanings and expectations for relationships. Participants were ambivalent about interacting online during lockdown, with interlocking risks and opportunities specific to and transcending lockdown as a condition. Implications are discussed regarding meanings and experiences of post-digital relationships for young people, both during and post-pandemic.

Prediction of novel malware using hybrid convolution neural network and long short-term memory approach

The rapid evolution of network communication technologies has led to the emergence of new forms of malware and cybercrimes, posing significant threats to user safety, network infrastructure integrity, and data privacy. Despite efforts to develop advanced algorithms for detecting malicious activity, constructing models that are both accurate and reliable remains a challenge, especially in handling vast and dynamically shifting data patterns. The prevalent bag-of-words (BOW) method, while widely used, falls short in capturing crucial spatial and sequence information vital for detecting malware patterns. To address this challenge, the work presented in this paper proposes hybrid convolution neural network-long short-term memory network (CNN-LSTM) combination models, leveraging CNN's spatial information extraction and LSTM's temporal modeling capabilities. Focused on predicting the infiltration of malicious software into personal computers, the proposed hybrid CNN-LSTM model considers factors such as location, firmware version, operating system, and anti-virus software. The proposed models undergo training and evaluation using Microsoft's malware dataset, demonstrating superior performance compared to traditional CNN and LSTM models. The CNN-LSTM model achieves an impressive accuracy of 95% on the Microsoft malware dataset, highlighting its effectiveness in malware detection.