Network Communication Research Articles

Research on reliability mapping of 5G low orbit constellation network slice based on deep reinforcement learning

Reliability mapping of 5G low orbit constellation network slice is an important means to ensure link network communication. The problem of state space explosion is a typical problem. The deep reinforcement learning method is introduced. Under the 5G low orbit constellation integrated network architecture based on software definition network (SDN) and network function virtualization (NFV), the resource requirements and resource constraints of the virtual network function (VNF) are comprehensively considered to build the 5G low orbit constellation network slice reliability mapping model, and the reliability mapping model parameters are trained and learned by using deep reinforcement learning, solve the problem of state space explosion in the reliability mapping process of 5G low orbit constellation network slices. In addition, node backup and link backup strategies based on importance are adopted to solve the problem that VNF/link reliability is difficult to meet in the reliability mapping process of 5G low orbit constellation network slice. The experimental results show that this method improves the network throughput, packet loss rate and intra slice traffic of 5G low orbit constellation, and can completely repair network faults within 0.3 s; For different number of 5G low orbit constellation network slicing requests, the reliability of this method remains above 98%; For SFC with different lengths, the average network delay of this method is less than 0.15 s.

Post-quantum framework for authorized and secure communication in multi-server networking

Post-quantum framework for authorized and secure communication in multi-server networking

Intellicise model transmission for semantic communication in intelligence-native 6G networks

Intellicise model transmission for semantic communication in intelligence-native 6G networks

Reliable communication in dynamic networks with locally bounded byzantine faults

The Byzantine tolerant reliable communication primitive is a fundamental building block in distributed systems that guarantees the authenticity, integrity, and delivery of information exchanged between processes.We study the implementability of such a primitive in a distributed system with a dynamic communication network (i.e., where the set of available communication channels changes over time). We assume the f-locally bounded Byzantine fault model and identify the conditions on the dynamic communication networks that allow reliable communication between all pairs of processes. In addition, we investigate its implementability on several classes of dynamic networks and provide insights into its use in asynchronous distributed systems.

Orthogonal Chirp Division Multiplexing Assisted Dual-Function Radar Communication in IoT Networks

Orthogonal Chirp Division Multiplexing Assisted Dual-Function Radar Communication in IoT Networks

Semantic Communication in Non-Terrestrial Networks: A Future-Ready Paradigm

Semantic Communication in Non-Terrestrial Networks: A Future-Ready Paradigm

Scalable Extraction Based Semantic Communication for 6G Wireless Networks

Scalable Extraction Based Semantic Communication for 6G Wireless Networks

Hardware-realized secure transceiver for human body communication in wireless body area networks

Wireless body area networks (WBANs), featuring wearable and implantable devices for collecting physiological data are increasingly critical in healthcare for enabling continuous remote monitoring, diagnostic improvements, and treatment optimization. Secure communication within WBANs is essential to protect sensitive health data from unauthorized access and manipulation. This paper introduces a novel secure digital (SD)- human body communication (HBC) Transceiver (TR) system, tailored for WBAN applications, that prioritizes security and offers significant enhancements in size, power efficiency, speed, and data transmission efficiency over current solutions. Leveraging a combination of frequency-selective (FS) digital transmission with walsh codes (WCs) or quadrature amplitude modulation (QAM), and incorporating one-round encryption and decryption modules, the system complies with the IEEE 802.15.6 standard, ensuring broad compatibility. Specifically, the QAM-based SD-HBC TR system exhibits a 4% reduction in chip area, a 7.6% increase in operating frequency, a 3.4% decrease in power consumption, a 27.5% reduction in latency, and improvements of 33% in throughput and 35.5% in efficiency. Importantly, it achieves a bit error rate (BER) of up to 10-8 , demonstrating high reliability across communication methods. This research significantly advances secure communication in WBANs, offering a promising approach for enhancing the reliability, efficiency, and security of healthcare monitoring technologies.

A Decade Review of Device-to-Device Communication in mm-Wave Network: Challenges and Solutions

A Decade Review of Device-to-Device Communication in mm-Wave Network: Challenges and Solutions

Research on AdHoc network routing algorithm for link stability prediction

Abstract This study aims to improve network communication efficiency and reliability by researching routing algorithms for predicting link stability in AdHo networks. AdHoc network is a self-organizing, decentralized network structure in which nodes establish temporary connections through wireless communication, making the network resilient and flexible. However, due to the continuous changes in network topology and the instability of wireless links, routing problems in AdHoc networks have become particularly complex. This article proposes a backup path routing algorithm LS-BPR based on link stability prediction. This mechanism takes the path with the minimum delay as the main path, uses the cost function as the standard for selecting backup paths, and then uses the ARIMA model to predict links with good link stability as backup paths. We simulate and compare LS-BPRAODV and AODV protocols using the QualNet simulation platform. The simulation results show that the LS-BPRAODV, with the addition of a prediction mechanism, improves network performance, reduces the number of source node routing discoveries, reduces the number of route breaks, significantly reduces the packet loss rate and information reception delay in the network, and improves the average throughput of the network. This study provides new theoretical and empirical support for the routing algorithm for link stability prediction in AdHo networks, which is of great significance for promoting the development of wireless self-organizing networks.

Effects of packetization on communication dynamics in brain networks.

Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.

An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM)

The Mobile Ad-hoc Networks (MANETs) have gained a significant attention in the recent years due to their proliferation and huge application purposes. To defend from many types of modern cyber dangers like Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), Insider Threats, Ransomware, Zero-Day Exploits, Social Engineering tactics, and etc is not easy when it comes to keeping MANETs security. These complex assaults focus on network infrastructure, take advantage of weaknesses in communication protocols and control user actions. Although there have been improvements in intrusion detection systems (IDS), it is still difficult to fully safeguard MANETs. The purpose of this research is to create advanced methods that can accurately find and decrease attacks inside MANETs. Applying Sensor-based Feature Extraction (SFE) to extract useful network features such as Received Signal Strength Indication (RSSI) and Time of Travel (TOT) from datasets NSL-KDD and CICIDS-2017. Utilizing the fresh method of Precise Probability Genetic Algorithm (PPGA) optimization for removing unrelated details, which enhances precision in detecting attacks. Predicting normal and attacking labels by applying Stacked Recurrent Long Short Term Memory (SRLSTM) method, fine-tuning classifier's parameters in every layer to improve outcomes. In order to authenticate and compare the suggested methods with current attack detection tactics, this study will make use of various evaluation measurements. The NSL-KDD, which is a benchmark dataset in network intrusion detection research, has a wide variety of network traffic data with instances that are labeled as normal and different attacks. CICIDS-2017 is similar because it contains an extensive dataset too - this includes real-world traces from network traffic where there's both regular activity and harmful actions. The purpose is to enhance the existing status of MANET security so as it can withstand more strongly against cyber dangers. According to the outcomes, it is analyzed that the attack detection accuracy has improved greatly 99 % when compared to other methods, as shown by the detailed assessment measurements. Better handling of big datasets with top detection accuracy reduces the time needed 8.9 s for training and testing models. Decrease in misclassification results and better ability to differentiate normal network actions from harmful intrusions. Improved resistance of MANETs to different cyber dangers, guaranteeing the safety and dependability of network communication in changing and non-centralized settings.

A stable satellite routing algorithm based on a-star algorithm in dynamic networks

Abstract This paper focuses on solving the routing optimization problem under the dynamic topology of the satellite communication network. With the rapid development of low-earth-orbit (LEO) satellite network communication technology, the dynamically changing topology of satellite networks has brought about frequent link switching and triggered a large number of routing updates. This paper proposes an improved routing algorithm named the Stable A-Star Routing (SASR) algorithm. The SASR algorithm is based on the A-star algorithm and aims to reduce path-switching frequency and enhance path stability by optimizing the path selection strategy. Through detailed simulation experiments and performance evaluation, we verified the performance advantages of the SASR algorithm in terms of the number of path switches, the number of path hops, the route convergence time, and the occurrences of congestion in business under different business volumes, and conducted comparative studies with classic algorithms. The results show that the SASR algorithm offers obvious advantages in key performance indicators. Compared with the Dijkstra algorithm and the A-star algorithm, the average number of path switches can be reduced by 22% and 10%, respectively. It shows more prominent advantages under a larger business volume, providing an efficient and reliable solution for routing optimization under the dynamic topology of satellite communication networks.

Changes in Spatiotemporal Dynamics of Default Network Oscillations between 19 and 29 Years of Age.

The exploration of functional resting-state brain developmental parameters and measures can help to improve scientific, psychological, and medical applications. The present work focussed on both traditional approaches, such as topographical power analyses at the signal space level, and advanced approaches, such as the exploration of age-related dynamics of source space data. The results confirmed the expectation that the third life decade would show a kind of stability in oscillatory signal and source-space-related parameters. However, from a source dynamics perspective, different frequency ranges appear to develop quite differently, as reflected in age-related sequential network communication profiles. Among other discoveries, the left anterior cingulate source location could be shown to reduce bi-directional network communication in the lower alpha band, whereas it differentiated its uni- and bidirectional communication dynamics to sub-cortical and posterior brain locations. Higher alpha oscillations enhanced communication dynamics between the thalamus and particularly frontal areas. In conclusion, resting-state data appear to be, at least in part, functionally reorganized in the default mode network, while quantitative measures, such as topographical power and regional source activity, did not correlate with age in the third life decade. In line with other authors, we suggest the further development of a multi-perspective approach in biosignal analyses.

Analysis of the Communication Network Among Citizens on Twitter in Violence Victims in Indonesia

The high level of violence in Indonesia, especially against women and children, requires special attention from all parties. It needs to be improved not only from the government’s side but also from the community and in any form. This research aims to find out the communication network of civilians on Twitter as one of the social media platforms for dealing with victims of violence in Indonesia. A qualitative analysis method was employed to collect data and information on the communication and interaction of KontraS in its role as a non-governmental organization. This research also utilized Software NVivo 12 Plus to analyse the data to find out the form of the network communication of KontraS in dealing with the violence victims in the state through Twitter. The research results have revealed that KontraS utilizes Twitter for communication and interaction to build organizational networks involving several parties, where information on human rights issues and violence is posted with hashtags to help disseminate information that leads to the formation of the organizational power of KontraS to support the advocacy of policy that is pro-human rights- and violence-related issue handling in Indonesia. The communication and interaction of KontraS on Twitter impact the dynamic of discussion on the issues of human rights and violence in Indonesia. This research, however, only focused on Twitter data. Thus, further research is expected to involve more data on other social media, such as Facebook and YouTube. Keywords: Twitter, human rights, violence victims, policy, communication.

Advanced Servo Control and AI Integration in 3-DoF Platforms for Enhanced Simulation Interactivity

This paper proposes a new approach to enhance the realism and interactivity of shooting simulation systems by integrating a three-degree–of–freedom (3-DoF) platform with sensory and interactive elements, as well as digital content. The system employs visual effects computers and servo controls, utilizing network packet messages for communication based on different scene definitions. When the control handle sends commands, the visual effects computer transmits control parameters to the image generator. Additionally, AI-controlled aircrafts act as enemy planes, autonomously determining flight paths, tracking targets, and engaging in combat, thereby enhancing realism in interactive mechanisms. An iterative learning control (ILC) is designed to provide the platform with good dynamic response, load capacity, and tracking ability when operated by a manual control handle. The core control uses a TMS320F28377D digital signal processor from Texas Instruments, integrated with visual effects computers for three-axis control, controller computation, finite state machines, and network communication operations. Experimental results demonstrate the feasibility and effectiveness of the developed three-axis shooting platform, achieving immersion and coordination with AI enemy aircrafts.

Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm.

Rhizomic Interdisciplinary Model in the Research into the Phenomenon of “New Sincerity”

The article localizes the problem of interscientific system methodology in the study of a new social practice domain, which manifests itself in the form of the trend towards sincerity and frankness in language interaction. The purpose of the article is to consider the issues that arise within the overlapping scientific approaches in the context of polyparadigmality in linguistics. The article describes vectors and scenarios of discipline interaction, as well as the examples of interdisciplinary transfer practice. The author presents the concept of rhizomic interdisciplinarity as a nonlinear development of scientific research that contains the potential for differentiation and integration. The system methodology application has enabled the author to build the core part of the model for describing linguistic facts. It includes some clusters of scientific knowledge that are undergoing a development phase. The article outlines the problems of “new sincerity” with an emphasis on network communication. For illustration, data from social events with elements of a flash mob, cases of cancel culture and eco-alarmism were used. The patterns that form a social community in the form of thematic fashion, Internet communication algorithms, and mobilizing potential are indicated. The author presents several clusters of research into the phenomenon of “new sincerity” from the perspective of the science of language. Being indicative of the dynamics of interaction amongst currently existing approaches and theories, they can form the core of the research model.

Resilient enhancements of routing protocols in MANET

Resilient of routing processes is one of the biggest challenges for data transmission in mobile networks without infrastructure. Communication under current routing protocols is through a communication path that, although the shortest, may not perform satisfactorily in terms of resilient. Routing and communication within such a path may take place using nodes that are malicious or inappropriate in the communication process due to malicious or poor technical state. This paper presents a new algorithm for various uses of mobile ad hoc networks not only in edge networks with infrastructure but also with the possibility of being used in the cloud solutions. We have modified decentralized blockchain technology and artificial intelligence using deep learning methods that have been implemented in routing processes. The objective of this algorithm was to select the most resilient communication path from the source to the destination node. Such a communication path selection consisted of selecting the nodes that were most suitable in terms of resilience, where the selection nodes was provided through a network and technical parameters. The key quality of service metrics, throughput, total delay, number of delivered signaling and data packets and the ratio between them were used to evaluate the proposed resilient routing algorithm. Modified resilient routing protocols achieved improvement in all the analyzed parameters compared to the original routing protocols. The improvement in these parameters led to an increase in the resilience of the routing process based on the actual data obtained from each node in the network and previous communications.

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.