Network Transmission Research Articles

Aka-Net: anchor free-based object detection network for surveillance video transmission in the IOT edge computing environment

Aka-Net: anchor free-based object detection network for surveillance video transmission in the IOT edge computing environment

Detracking Autoencoding Conditional Generative Adversarial Network: Improved Generative Adversarial Network Method for Tabular Missing Value Imputation.

Due to various reasons, such as limitations in data collection and interruptions in network transmission, gathered data often contain missing values. Existing state-of-the-art generative adversarial imputation methods face three main issues: limited applicability, neglect of latent categorical information that could reflect relationships among samples, and an inability to balance local and global information. We propose a novel generative adversarial model named DTAE-CGAN that incorporates detracking autoencoding and conditional labels to address these issues. This enhances the network's ability to learn inter-sample correlations and makes full use of all data information in incomplete datasets, rather than learning random noise. We conducted experiments on six real datasets of varying sizes, comparing our method with four classic imputation baselines. The results demonstrate that our proposed model consistently exhibited superior imputation accuracy.

New 4D hyperchaotic system’s application in image encryption

In order to protect sensitive information from unauthorized access and illegal copy during network transmission, storage and processing, we propose a new four-dimensional hyperchaotic system (4DHS) and apply it to encryption algorithm. Firstly, the dynamical properties of 4DHS are analyzed according to the structure, and the chaotic properties are verified by dissipation, equilibrium point and lyapunov exponent. Secondly, the chaotic sequence combined with Arnold scrambling method is adopted to scramble the pixel values of the plaintext image, and the scrambled pixel matrix is diffused into the ciphertext image matrix by XOR operation. Finally, we conduct the experiments to validate the effectiveness of the proposed encryption algorithm and achieve satisfactory results. At the same time, we compare the proposed encryption algorithm with other encryption algorithms, and the excellent encryption effect of our encryption algorithm can be proved.

Artificial self-attention rabbits battle royale multiscale network based robust and secure data transmission in mobile Ad Hoc networks

In a Mobile Ad hoc Network (MANET), the network structure constantly evolves due to the movement of devices, users, or nodes. The Black Hole Attack (BHA) is recognized as the most impactful threat in MANETs, where an attacker node intentionally discards all data traffic, hence minimizing the network performance. Therefore, developing a secure and robust routing protocol is vital for efficient data transmission in a MANET. Although existing solutions have been designed to counteract malicious nodes, these solution presents drawbacks such as the need for additional hardware, notable delivery delays, limited throughput, increased energy consumption, and minimum packet delivery ratio. Thus, this research work introduces a new updated Ad hoc On-demand Distance Vector (AODV) routing protocol that integrates the benefits of the Artificial Rabbits Battle Royale Optimization (ARBRO), and Self-attention Multiscale Network (SMNet). The role of the ARBRO algorithm is to optimize the route by selecting the most appropriate node properties. Then, SMNet identifies the BHA node using these node properties. The proposed scheme is implemented in MATLAB. Experimental results determines that the introduced approach attains a delay of 0.039 s, a throughput of 94.57 Kbps, and a packet delivery ratio of 98.76 %, which are better than the existing techniques. Simulation results demonstrate that the introduced method improves network performance by enhancing the quality of service in the presence of a BHA.

Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission

One of the important directions for improving modern Wide Area Networks is efficient and secure packet routing. Efficient routing is often based on using the shortest paths, while ensuring security involves preventing the possibility of packet interception. The work is devoted to improving the security of data transmission in IP networks. A new approach is proposed to minimize the number of distrustful nodes on the path of IP packet transmission. By a distrustful node, we mean a node that works correctly in terms of hardware and software and fully implements its data transport functions, but from the point of view of its organizational subordination, we are not sure that the node will not violate security rules to prevent unauthorized access and interception of data. A distrustful node can be either a transit or an end node. To implement this approach, we modified Dijkstra’s shortest path tree construction algorithm. The modified algorithm ensures that we obtain a path that will pass only through trustful nodes, if such a path exists. If there is no such path, the path will have the minimum possible number of distrustful intermediate nodes. The number of intermediate nodes in the path was used as a metric to obtain the shortest path trees. Routing tables of routers, built on the basis of trees obtained using a modified algorithm, provide increased security of data transmission, minimizing the use of distrustful nodes.

Case Study: Free Space Path Loss for Mobile Network and WLAN

Wireless communication systems transmit and receive information through free space. Thus, there are other factors to consider when designing a wireless communication system. One of those factors is Free space Path Loss (FPSL), which refers to the loss of signal strength in the propagation of the signal in free space. In this study, the effects of obstruction and distance on the FSPL of the transmission in mobile networks and Wireless Local Area Networks (WLAN) will be measured. The measured results show that the obstructions have a higher impact on the received signal compared to the distance. In the mobile network setting, the open area has an FPSL of 111 dB, whereas the obstructed office area has a significantly higher FSPL of 158 dB. Furthermore, in the WLAN setting, the FSPL of an obstructed area is 19 dB higher than the line-of-sight propagation. Thus, the layout of the area to implement a wireless communication system must be thoroughly analyzed for obstructions and distance to provide optimal wireless communication service to customers.

Analyse Chinese Teaching Project-Based Learning Design Application Optimization Using Keras Model

Educational methods for data mining may be used to successfully analyze data from a variety of educational platforms, including e-learning, e-admission programs, and automated results management solutions, to provide very helpful insights into students' performance. Chinese language teaching will reach new heights as a result of the rapid advancement of network technology and the constant improvement of network transmission speed. Additionally, the acceptance of online payment methods is growing, simplifying international and web-based trade. Deep learning models have become more significant in many spheres of life recently. It made it possible for investigators to automatically extract superior characteristics from unprocessed data.With the aid of the Keras model, make it is easier for students to rapidly obtain practical knowledge with deep learning structures and boost their confidence and enthusiasm for learning. In this study, we investigate the Long Short-Term Memory (LSTM) deep neural network model with Novel Swarm Optimization (NSO) network to accurately forecast student improvement using historical data. The most cutting-edge LSTM and an optimization mechanism model (NSO) have been employed in this work to solve research issues based on sophisticated feature categorization and prediction. For Chinese academics, institutions, and government agencies to accurately anticipate performance, this study is very important. When paired with the optimization mechanism (NSO), LSTM's better sequence learning capabilities provide performance that is superior to the current state-of-the-art. The prediction accuracy of the suggested approach is 93%.

E-learning and speech dynamic recognition based on network transmission in music interactive teaching experience

The research adopts the network transmission technology to realize the framework of e-learning, and combines the speech dynamic recognition technology to improve the interactive music teaching experience. By establishing an online learning platform, the network is used to transmit learning resources such as audio, video and text. In the application of speech dynamic recognition technology, the modern speech recognition algorithm and artificial intelligence technology are used. Let students sing or play on a device such as a microphone or a mobile phone and transmit their sound signals to the system. Through speech dynamic recognition technology, students 'audio is analyzed and processed in real time, and students' singing or performance is evaluated and feedback. The system can analyze students' intonation, rhythm, timbre and other musical elements, and evaluate and guide them according to the preset scoring standards, so as to help students improve their playing skills. Through the combination of network transmission technology and speech dynamic recognition technology, students can enjoy a more personalized and interactive teaching experience in music learning. The systematic real-time evaluation and guidance function can also help students adjust their performance skills in time and provide targeted learning suggestions.

Smart cost estimation: Empirical case for extra-high voltage transmission towers

Nowadays, electricity has become an integral part of human lives. Most of our daily appliances, tools, and personal belongings are inseparable from electricity. To ensure a proper electricity distribution with an efficient transfer capability, Extra-High Voltage (EHV) transmission towers are needed. To design such a structure, it is of utmost importance to account for the cost of said tower. However, the process to estimate the cost of EHV transmission towers is both time-consuming and strenuous on human labor since a lot of consideration have to be taken. To overcome this, an imperative requirement exists for a prompt, precise, and automated tool to replace the existing manual cost estimation method. This research endeavor aims to craft a tool using support vector regression (SVR) with the capacity to prognosticate construction expenses for projects involving EHV transmission towers. The exploration of pertinent literature has enabled us to amass historical data and delineate the attributes essential for estimating costs linked to EHV transmission tower construction. The investigation delves into a comprehensive dataset spanning the past decade in Taiwan. Within this timeframe, 317 EHV transmission towers were erected between 2009 and 2019. However, 79 of these instances are excluded due to incomplete information, thereby yielding 238 viable datasets (comprising 75 % of the overall total) to underpin the development of the SVR model. By configuring the parameters to C = 0.2 and γ = 0.1, followed by 5-fold cross-validation, the resultant SVR model attains a remarkable prediction accuracy of 97.91 %, on average. As a result, the proposed SVR-based model can effectively and accurately predict the cost of constructing an EHV transmission tower project and reduce the time spent on estimation, thus contributing to the enhancement of the resilience and robustness of the transmission network system.

Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original SNP systems only consider the time delay caused by the execution of rules within neurons, but not caused by the transmission of spikes via synapses between neurons and its adaptive adjustment. In view of the importance of time delay for SNP systems, which are a time encoding computation model, this study proposes SNP systems with adaptive synaptic time delay (ADSNP systems) based on the dynamic regulation mechanism of synaptic transmission delay in neural systems. In ADSNP systems, besides neurons, astrocytes that can generate adenosine triphosphate (ATP) are introduced. After receiving spikes, astrocytes convert spikes into ATP and send ATP to the synapses controlled by them to change the synaptic time delays. The Turing universality of ADSNP systems in number generating and accepting modes is proved. In addition, a small universal ADSNP system using 93 neurons and astrocytes is given. The superiority of the ADSNP system is demonstrated by comparison with the six variants. Finally, an ADSNP system is constructed for credit card fraud detection, which verifies the feasibility of the ADSNP system for solving real-world problems. By considering the adaptive synaptic delay, ADSNP systems better restore the process of information transmission in biological neural networks, and enhance the adaptability of SNP systems, making the control of time more accurate.

Faulty Nodes Detection for Reliable Data Transmission in Intelligent Wireless Sensor Networks

Faulty Nodes Detection for Reliable Data Transmission in Intelligent Wireless Sensor Networks

Comprehensive fault diagnosis for multiple coupled SFCs based on deep learning

Service Function Chains (SFCs) are deployed and executed in a complex network environment, where multiple SFCs are coupled by sharing various types of network functions and transmission links. As a result, diagnosing the state of these SFCs and accurately locating network fault becomes a challenge and plays a fundamental role in SFC management. In this paper, we introduce a comprehensive fault diagnosis mechanism for multiple coupled SFCs by applying the deep learning technique. By formulating the involved network nodes and transmission links into a heterogeneous graph model, we propose the Rendered Service Path Heterogeneous Graph Attention Network (RSP-HAN) model to comprehensively estimate the state of every network node and link. Typically, RSP-HAN first applies Bi-directional Long Short-Term Memory (Bi-LSTM) to normalize features from different types of network entities, then obtains the embedding results through intra-RSP and inter-RSP attention mechanisms. Finally, Multi-Layer Perceptron (MLP) is utilized to estimate the state of these network entities. We also setup an experiment platform and obtain extensive datasets to evaluate our RSP-HAN model. Experimental results show that RSP-HAN performs better in fault diagnosis for various types of network entities than several traditional methods and two deep learning-based baselines, the Graph Convolutional Network (GCN) and the Graph Attention Network (GAT).

PCNP: A RoCEv2 congestion control using precise CNP

With the development of data centers, the traditional TCP/IP network stacks have become inadequate in addressing the high bandwidth and low latency demands of diverse upper-layer applications. Some cloud providers deploy RoCEv2 (RDMA over Converged Ethernet v2) technology to accelerate network transmission in their data centers, and the reliable network transmission in RoCEv2 relies on congestion control protocols. Currently, traditional congestion control protocols primarily employ heuristic rate adjustment strategies, which often exhibit inadequate performance in terms of convergence, stability, and fairness. Furthermore, the latest congestion control protocols rely on the support of costly switch hardware, which hinders the incremental expansion of data centers. This paper proposes PCNP, a precise congestion control protocol, it extends standard CNP (Congestion Notification Packet) and introduces a bounded rate adjustment strategy that leverages receiver information to achieve precise rate adjustment within existing data centers. Simulation results demonstrate that PCNP effectively decreases the rate fluctuation during the convergence process and enhances fairness. In comparison to DCQCN, HPCC and PCN, PCNP reduces the standard deviation of flow completion time under the incast traffic by 90%, 91% and 91%, respectively. Additionally, it can reduce the tail flow completion time by up to 83%, 40% and 46%.

Research on Motion Capture System of Motor Skill Based on Computer Vision Technology

Compared with the existing motion capture system, the inertial conduction based motion capture method proposed in this project can effectively overcome the shortcomings of the current motion capture, such as limited communication distance, low accuracy of data acquisition, low visualization degree of model display software, narrow application range, etc., and improve the practical application value of the method. A method of motion capture and motion control based on wireless network is proposed. Since most of the skeleton of the human body is rigid, the complete expression of the human movement is realized through the study of 17 important joints, and all the movement information of the athlete can be obtained by placing sensors on the corresponding nodes. Through wireless network transmission technology, the player's action information is transmitted to the 3D modeling software to achieve real-time control of the action. When measuring each sensor, strapdown inertial navigation technology is used to improve the accuracy of acquisition. The 3D modeling software is based on a professional motion capture software, and through the forward and inverse kinematics of its motion, the 3D modeling is highly consistent with the wearable device.

Enhancing Logistics Efficiency through Computer and Network Technologies: A Pathway to Industry Informatization

The rapid development of computer and network technology provides a solid foundation for the informatization of logistics industry. Through INTERNET, logistics branches, suppliers, customers, etc. scattered in different geographical locations are connected to form an information network for information transmission and sharing, so that all parties can understand the operation of businesses in different places in real time and improve the operation efficiency of logistics activities. Based on the broad prospects of computers in the logistics industry, this paper conducts in-depth research on logistics modernization and logistics informatization, the impact of computer technology on the logistics industry, and the countermeasures to accelerate logistics industry informatization.

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Distributed formation control for leader‐follower quadrotor unmanned aerial vehicles system based on practical sliding mode and event‐triggered mechanisms

AbstractThis article investigates the distributed formation trajectory tracking problem of a homogeneous quadrotor unmanned aerial vehicles (UAVs) system using a leader‐follower consensus protocol in three‐dimensional space. To enable the follower UAVs in the swarm to track a single leader UAV and form a desired formation under undirected topology, a six‐degree‐of‐freedom dynamic model with bounded time‐varying disturbances is established, which is divided into a tracking subsystem and a stabilizing subsystem. A practical sliding mode control (PSMC) based event‐triggered algorithm is proposed to achieve distributed formation control for a leader‐follower quadrotor UAVs system and reduce the network transmission resources. To address the potential issue of inter‐UAVs collisions during formation, an artificial potential field (APF) method is introduced in the tracking subsystem, and an disturbance observer is introduced in the stabilizing subsystem to estimate the unknown external disturbances. The boundedness of signals in the closed‐loop system is demonstrated using Lyapunov stability theory, and the exclusion of Zeno behavior can be ensured by having a positive lower bound on the inter‐event time between two consecutive triggering events. Finally, the effectiveness of the proposed event‐triggered algorithm is validated through simulation examples.

Leveraging the Synergy of IPv6, Generative AI, and Web Engineering to Create a Big Data-driven Education Platform

The rapid advancement of network technology in China has significantly accelerated the implementation of information technology in higher education. Through the utilization of computer technology, multimedia technology, big data technology, artificial intelligence technology, and network communication technology, the integration of these technologies in university teaching has become widespread. This paper presents an analysis and discussion on the utilization of the latest IPv6 network transmission protocol technology to enhance the application of data collection in university education, with a specific focus on gathering information related to university faculties. By leveraging web engineering and multimedia technology as fundamental components, the network facilitates the sharing of educational resources among students, thereby enabling the reform of management approaches, fostering educational progress in China, and establishing a comprehensive big data-driven education platform specifically tailored to colleges and universities. Additionally, the incorporation of big data visualization and analysis tools allows for easy retrieval of existing university educational information, facilitates the creation of data charts, and expedites the utilization of data for its inherent value. Finally, the proposed approach employs generative AI to collect and analyze feedback from students and educators, followed by the application of web engineering techniques to continuously enhance the online education platform based on this feedback.

Multi-UAV and IRS placement for secure data transmission in NOMA-enabled wireless networks

In this paper, we investigate multi-unmanned aerial vehicle (UAV)-enabled intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) downlink networks for secure data transmission in the presence of an intruder. Since Line of Sight (LoS) communication is obstructed, UAVs are utilized as aerial base stations to transmit confidential data to ground users with the assistance of IRS. The objective is to optimize the placement of UAVs and IRS, phase shift of the IRS, and transmit beamforming of the UAVs in order to maximize the network secrecy rate. The formulated optimization problem is non-convex in nature, posing a challenge in finding the optimal solution using conventional techniques. To tackle this problem, we propose a novel algorithm called balanced grey wolf optimizer and differential evolution (B-GWODE). This algorithm not only leverages the advantages of both the GWO and DE algorithms but also incorporates parameter modifications to mitigate the issue of premature convergence and to strike a suitable balance between exploration and exploitation. This hybrid approach efficiently search the solution space and obtain the optimal configuration that maximizes the network secrecy rate. Simulation results demonstrate the superiority of our proposed B-GWODE algorithm over the benchmark methods.

Channel prediction method based on the data-driving for distribution automation main station

A data-driven channel prediction method for distribution automation master is proposed to address the poor quality of communication network and communication system transmission problems in distribution network communication. In this paper, an adaptive broad learning network (ABLN) consisting of a standard broad learning network and a hybrid learning network is introduced to predict the channel state information of the communication system. Among them, the hybrid learning network is used to solve the ill-conditioned solution problem when estimating the output weight matrix of the standard broad learning network. Therefore, the ABLN produces sparse output weight matrices and provides excellent prediction performance. In the simulation analysis, the outdoor and indoor scenes are considered based on OFDM system. The prediction performance of ABLN is subsequently evaluated in one step prediction and multistep prediction. The results show that for the prediction performance is concerned, the maximum improvement of ABLN is about 96.49% as compared to other evaluation models, indicating that the CSI is effectively predicted by the ABLN to support the adaptive transmission of the main station of the distribution automation and to satisfy the quality of the communication network of the distribution network.