Problem Of Data Transmission Research Articles

Multi-scale image compression and reconstruction algorithm for structural health monitoring system

Image data is increasingly common in structural health monitoring systems. However, due to the limitation of hardware resources such as network bandwidth, the data collected by sensors cannot be transmitted efficiently. In order to solve the problem of inefficient data transmission and ensure data recoverability, an end-to-end compression and reconstruction algorithm with an attention-enhanced residual module and a quantization mechanism is proposed. In order to meet the needs of SHM systems for image diversity, multi-scale images of local and global structural damage are used for research. The performance of the proposed algorithm is compared with traditional image compression standards and baseline models. Experimental results show that the peak signal-to-noise ratio of the reconstructed image is greater than 30 dB at different bit rates, effectively reconstructing the original image information; for images with complex textures, the residual block attention module can improve the rate-distortion performance of the proposed algorithm; when the bit rate is less than 0.44 bit per pixel, the peak signal-to-noise ratio and structural similarity of the reconstructed image of the proposed algorithm are better than those of the autoencoder baseline model and Joint Photographic Experts Group compression method. Structural damage detection examples verify the usability of compressed-reconstructed images in engineering applications.

Hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade

The district integrated energy system has the characteristics of multi-layer and multi-subjects. Aiming at the problems of data privacy, conflict of interest, and mismatch of interaction power among these multi-subjects, a hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade is proposed. Firstly, the operational communication architecture of the district integrated energy system based on the cloud-pipe-edge-end is constructed to solve the problems of data transmission, data computation, and data privacy. Secondly, a two-layer distributed scheduling model of district integrated energy system considering waste heat recovery is established based on a laddered carbon trading mechanism. At the upper level, an optimal scheduling model targeting low-carbon and economic performance is developed by considering the participation of district integrated energy system in demand response and the purchase and sale of electricity. At the lower level, an optimal scheduling model considering waste heat recovery for subsystem energy-coupled equipment is developed. Then, the analytical target cascade is used to realize the decoupling of the two-layer model, and the efficient solution is achieved in the process of multiple iterations. Finally, by analyzing the arithmetic examples, it is verified that the proposed method can improve the economy of system operation by 4.38% and the absorption capacity of wind power by 9.89%, while protecting the privacy and interest claims of multiple parties.

A Mobile AD Hoc Network Relay Method based on OLSR Protocol

Aiming at the reliability and performance problems of data transmission in the complex mobile AD hoc network environment, a relay method is designed and implemented in the unstable network environment with complex dynamics such as mobile, multi-hop and no center according to practical needs. By introducing a prior link-state routing protocol, OLSR is introduced. Using range value judgment, sliding window statistics and improved multi-point relay selection technology, it can effectively reduce the redundancy of relay node set, reduce the forwarding frequency of service data in the network, save the network overhead, and ensure the data transmission delay and quality in the large scale and dense mobile AD hoc network.

Optimization of underwater visible light communication transmission using multilevel regression modelling

This paper proposes the 32-Channel Dense wavelength Division Multiplexing (DWDM) system for Underwater Visible Light Communication (UVLC). The proposed DWDM-UVLC system performs the transmission of visible light data under different underwater ocean environmental conditions such as Pure sea, Clear ocean, and Coastal ocean water and ocean pollution such as oil spills, microplastics. The data transmission through visible light in underwater ocean environment is a challenging task due to attenuation in ocean water, which causes variations in water column parameters such as temperature, Ph, salinity, and chemical oil spill contents in water such as water with oil spills, and microplastics. The proposed dense wavelength Division Multiplexing (DWDM) system for Underwater Visible Light Communication (UVLC) is optimized using Bayesian optimized Multiple Regression Learning (BO-MRL) method. The BO-MRL based DWDM for UVLC mitigates the attenuation problems in data transmission, which is in the range of approximately 350m. The proposed DWDM-UVLC system is evaluated through the Performance metrics such as Q-factor, Minimal BER and proper spectral eye characteristics. The proposed BO-MRL based DWDM consists of non-return-to-zero (NRZ) modulation, which performs better in 320 Gbps, transmission in the range of 200m and 350m, and achieves the maximum Q-factor of 19.2364 with a minimal BER of 9.16489 e−083.

OPTIMASI INFRASTRUKTUR JARINGAN VLAN TRUNKING PROTOCOL MENGGUNAKAN SIMULASI PACKET TRACER PADA PT. RUKUN SEJAHTERA TEKNIK

Modern companies with many floors and rooms need computer network technology to support operational activities. The computer network at PT Rukun Sejahtera Teknik often causes data transmission failures and unstable computer network systems that cause network performance to fall and become unresponsive. the purpose of this research is to design a Virtual Local Area Network using the VLAN Trunking Protocol (VTP) method and Packet Tracer Simulation to improve network performance and limit the access rights of each user. The type of this research is qualitative with field study methods by conducting observations for two weeks. Observations include the existing Local Area Network (LAN) network and the problem of slow data transmission on the existing network. The results of testing the network design with the configuration of the VLAN Trunking Protocol (VTP) server and client mode, show that the switch configured in VTP client mode cannot add and change the VLAN that has been set. The ping test results show that computers in each room cannot access networks outside of different VLAN networks. It proves that the network design is as planned and running well.

Wire and Cable Quality Traceability System Based on Industrial Internet of Things and Blockchain

Wire and cable are important industrial products involving the national economy and people’s livelihood, which are hailed as the “blood vessel” and “nerve” of the national economy, providing the basic guarantee for the normal operation of modern economy and society. The data traceability of their production and circulation process is a key factor in ensuring their quality and safety management. We aim to solve the problems of unsafe data transmission, weak quality control, and information islands in the process of wire and cable quality traceability in order to improve the production management efficiency of wire and cable manufacturing enterprises and to reduce the cost of consumer quality traceability of wire and cable products. We analyzed the technical characteristics and advantages of Industrial Internet of Things (IIoT) identity resolution and the blockchain. Key technologies are introduced, a traceability method that integrates the two is proposed, and a quality traceability framework based on the IIoT identity resolution system and blockchain technology is constructed. By analyzing the quality information composition of the wire and cable supply chain, a new quality traceability model based on the wire and cable supply chain is established. Finally, through the verification of the developed quality traceability system, the quality traceability function and quality information of each production link of wire and cable are successfully realized. This paper fills a gap in the field of cable product quality traceability using the combination of IIoT and blockchain technology. According to this model, it also has some potential for the traceability of other industrial products.

Power supply station equipment status monitoring and evaluation system based on wireless network technology

With the continuous development of the power industry and the acceleration of the process of intelligence, monitoring and analyzing the status of power supply equipment is crucial for ensuring power supply and safety. Traditional power monitoring systems still have certain problems in data transmission. Using wireless network technology (WNT) to detect and analyze the status of power supply station equipment can not only achieve real-time monitoring and remote control, improve equipment operation and management efficiency, but also achieve automated monitoring and analysis of equipment status, providing technical support for the intelligence of power supply station equipment. In order to verify the effectiveness of the WNT-based power supply station equipment status monitoring and analysis system, a comparative experiment was conducted with traditional wired network monitoring methods. The final experimental results show that the average feedback time for testing the WNT power supply station equipment status monitoring and analysis system is 7.71 s, which is 38.22 s shorter than traditional methods. The average fault location accuracy is 95.98 %, which is 35.91 % higher than traditional methods. The average management efficiency is 92.88 %, which is 34.71 % higher than traditional methods. Through testing data, it can be concluded that the power supply station equipment status monitoring and analysis system based on WNT has greater advantages than traditional power equipment monitoring methods.

A Long Short-Term Memory-Based Model for Kinesthetic Data Reduction

This paper proposes a novel mathematical model for teleoperation over communication networks. For teleoperation over a communication network, a high packet rate can result in inefficient data transmission and cross-traffic problems, leading to extra delay and jitter. This paper proposes an LSTM-based mathematical model which focuses on kinesthetic data reduction without loss of transparency during the transmission process through joint training combined with haptic data and perceptual deadband. Since the LSTM network can deal with a time series of haptic data, we further test the system performance through practically collected data. We investigate the packet rate and perceptual transparency of the proposed mathematical model by comparing with the conventional deadband. Additionally, we compare the proposed mathematical model with the perceptual deadband-based codecs. Simulation results show that the proposed solution further reduces the packet rate when dealing with haptic data without noticeable distortion. Also, comparing with the current JND perceptual threshold, the proposed mathematical model helps improve the practicality of the bilateral teleoperation system without losing transparency.

Efficient octagonal compression of multimedia data using using LZW-OMCA compressor for secured data transmission

Data compression is the ancestor of image compression, which uses fewer bits to represent the same picture. It is categorised as lossy or lossless depending on the quality required. In a lossless compression situation, no information is lost during the decompression process. Data loss is possible with the lossy technique since it is not reversible. In an effort to boost compression efficiency while maintaining a high xiv reconstruction quality of picture, near lossless approaches have evolved. The medical pictures consist of a large number of items, each of which may be described in detail and utilised for a variety of purposes. The clinically relevant item in 2D medical pictures is referred to as the Region of Interest (ROI), whereas in 3D images, it is referred to as the Volume of Interest (VOI). Saving energy is crucial since it is one of the most limited resources in these networks. However, DTN has an additional difficulty since communication between nodes is maintained so long as they are in physical proximity to one another. However, because to the nodes’ mobility, this may not be long enough to provide the necessary multimedia data transmission. Wireless networks are susceptible to security assaults, and traditional security solutions are computationally demanding, making them unsuitable for networks that constantly need to recharge their batteries. All of these are reasons for tackling the problems of multimedia data processing and transmission via wireless networks in this dissertation. With this in mind, it has been attempted to investigate low-overhead and safe multimedia data compression as a solution to the issue that energy-constrained nodes in these networks limit complex multimedia processing while keeping at least basic security features. LZW-OMCA compression using the Octagonal Multimedia Compression Algorithm is part of the suggested method. The purpose of this is to improve the compression ratio. The proposed approach uses a little bit of crypt to compress data, which makes the data unreadable to anybody except the intended receiver, hence providing network security. The previous proposed works analysed the performance of several compression algorithms applied to multimedia material. Performance assessment utilising MSE, SSIM, and other metrics are used to show the pros and cons of each segment.

Context-Aware Mobile Edge Resource Allocation in OFDMA Downlink System

Advanced sensing, data analysis, and communication techniques have promoted the emergence and tremendous development of the Intelligent Industrial Internet of Things (Intelligent IIoT). Intelligent IIoT-enabled 5G communication networks improve overall efficiency and open up a new market opportunity and economic growth era. In particular, for the ultra-reliable and low-latency communication (URLLC) scenario, The system requires a lightweight computing algorithm to ensure transmission reliability while providing rapid radio resource allocation. A proactive downlink system framework, supported by the reinforcement learning-based online model-free algorithm, is proposed to meet the upcoming challenge. The proactive task data transmission problem is decomposed into three sub-problems. With the help of anticipatory mobility management and virtual cell, the system gains high reliability through multipath diversity. The anchor node forwards the task data to multiple access points and manages radio resource allocation among and under the access points. The simulation justifies that the proposed framework handles the URLLC scenario well.

Fractional Factor Model for Data Transmission in Real-Time Monitoring Network

Modeling data transmission problems in graph theory is internalized to the existence of fractional flows, and thus can be surrogated to be characterized by a fractional factor in diversified settings. We study the fractional factor framework in the network environment when some sites are damaged. The setting we focus on refers to the lower and upper fractional degrees described by two functions on the vertex set. It is determined that G is fractional (g,f,n) critical if δ(G)≥⌊a2+b2+2ab+2a+2b−34a⌋+n and I(G)>n+⌊(a+b−1)22a+2b−1a⌋2, where 1≤a≤b and b≥2.

Internet of things sports information collection and sports action simulation based on cloud computing data platform

In recent years, cloud computing technology has shown exponential growth, and the upgrading of hardware and the improvement of computing performance have brought significant changes to the Internet of Things industry. With the changes of the times and the emergence of many new demands, data platforms under cloud computing platforms must make corresponding changes according to the new demands. Among them, the construction of cross regional data centers is particularly important, especially in commercial environments. How to reduce the cost of data centers on cloud computing platforms while ensuring business quality has become a crucial issue. Based on the above situation, this article has optimized the bandwidth cost of the data center and solved the problem of big data transmission based on delayed big data windows and multi delay windows. A mathematical model for optimizing bandwidth cost under multi delay windows is proposed. This article also studied sports action simulation, which plays an important role in sports research, film animation, and virtual reality. Simulation actions are usually implemented based on data capture methods. However, these methods typically do not have interactivity with the environment. To enhance the authenticity and interactive ability of simulation action information collection, this article adopts reinforcement learning method for training and design, and applies a system of functions such as collecting human sports data processing. This article applies cloud computing data platforms and sports information collection to sports action simulation, making progress in the development of sports action simulation.

Multi-granularity fusion resource allocation algorithm based on dual-attention deep reinforcement learning and lifelong learning architecture in heterogeneous IIoT

Deep reinforcement learning (DRL) is a promising technology to address the resource allocation problem for efficient data transmission in complex network environments. However, most DRL-based resource allocation algorithms suffer from limited feature extraction capabilities and lack scalability and generalization, especially in heterogeneous Industrial Internet of Things (IIoT) environments. In this paper, we develop a lifelong learning architecture that can integrate artificial intelligence (AI) algorithms into the heterogeneous IIoT network for efficient data transmission. Based on this, we propose an intelligent resource allocation algorithm based on dual-attention DRL (DADR) for forwarding node selection and channel access slot allocation in a specific network environment. The proposed DADR algorithm combines the advantages of multi-dimension convolutional attention and multi-head self-attention mechanisms. It can provide local- and global-feature fusion capabilities for distributed nodes while maximizing the performance of data transmission. Furthermore, we present a lifelong federated meta reinforcement learning (LFMRL) that can effectively utilize prior knowledge and enable the DRL agent quickly adapt to a new environment. Specifically, LFMRL adopts a federated meta learning-based knowledge fusion algorithm to fuse the knowledge of learned DADR algorithms and iteratively update the shared model, thereby improving the scalability and generalization of the shared model in heterogeneous IIoT environments. In addition, a simple and efficient knowledge transfer mechanism is enabled to accelerate the DRL model convergence by transferring the knowledge of the shared model to the new environment. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of energy efficiency, data transmission reliability, and network stability. Compared to DADR and FedAvg algorithms, LFMRL algorithm can further reduce the energy consumption, training time, and average forwarding node switching times, while improving packet delivery rate to 99.2%.

Design of High-Speed Data Transmission System Based on USB 3.0

The development of high-speed and high-precision ADC promotes the development process of waveform digitization in nuclear physics, which in turn brings the digitization process forward. Waveform digitizer preserves the complete pulse waveform data for reconstruction, placing greater demands on data transmission speed. This paper introduces an optimized data transmission implementation routine based on USB 3.0. A complete USB transmission system design includes USB firmware design, FPGA logic design, and upper USB controller design. The software and hardware of the system are specially designed to realize high-speed bidirectional communication between FPGA and PC. Through actual test, USB 3.0 transmission is high-speed and reliable, which solves a key problem of data transmission and storage of digitizer. This study has promising applications in nuclide measurements.

Energy-efficient data transmission with proportional rate fairness for NANs of smart grid communication network

In smart grid, the neighborhood area network (NAN) serves as a bridge connecting smart meters and meter data management system (MDMS) and is one of the most important components of the smart grid communication network. In this work, we study the energy-efficient data transmissions from NAN gateways (i.e., routers) to the concentrator that connects the MDMS. Particularly, to assure that each router associated with diverse number of associated smart meters can achieve a required data rate, a set of proportional rate fairness constraints are imposed into the optimization problem. In this case, an optimization problem that balances energy efficiency and fairness is formulated. Due to the fractional form of objective function, the combinatorial constraints on channel allocation variables, and the proportional rate fairness requirements, the formulated energy-efficient data transmission problem is non-convex and extremely computationally complex. In order to solve the problem efficiently, firstly, a subtractive transformation is introduced to handle the objective function, and an iterative algorithm based on Dinkelbach method is proposed; then for the inner loop optimization problem in each iteration, a low-complexity suboptimal algorithm that separates channel allocation and power distribution is developed. Finally, numerical results demonstrate that the proposed algorithm converges in a few steps and achieves a near-optimal energy efficiency performance.

Asynchronous FIFO Design Based on Verilog

With the rapid development of integrated circuits, asynchronous First Input First Output (FIFO) is often used to solve the problem of data transmission across the clock domain. This paper mainly studies the key problem of asynchronous FIFO design - the generation of empty - full signal. To solve this problem, it is necessary to realize the synchronization of signal across the clock domain and convert binary code into gray code to reduce the probability of metastable state. The null and full signals generated by the asynchronous FIFO designed in this paper are false null and false full, but this does not affect the function of the asynchronous FIFO, and will only lose part of the performance. Through Modelsim simulation verification, the designed asynchronous FIFO can realize first-in, first-out of data and correctly generate empty and full signal, which meets the design requirements. The research of this paper is helpful for further application of asynchronous FIFO in data transmission across clock domains.

Research on the High Precision Synchronous Control Method of the Fieldbus Control System

The synchronization control performance of the Fieldbus control system (FCS) is an important guarantee for the completion of multi-axis collaborative machining tasks, and its synchronization control accuracy is one of the decisive factors for the machining quality. To improve the synchronization control accuracy of FCS, this paper first makes a comprehensive analysis of the factors affecting synchronization in FCS. Secondly, by analyzing the communication model of linear Ethernet, a distributed clock compensation method based on timestamps is proposed to solve the asynchronous problem of communication data transmission in the linear ethernet bus topology. Then, based on the CANopen application layer protocol, the FCS communication and device control task collaboration method is proposed to ensure the synchronous control of multiple devices by FCS. Finally, an experimental platform is built for functional verification and performance testing of the proposed synchronization method. The results show that the proposed synchronization method can achieve a communication synchronization accuracy of 50 ns and a device control synchronization accuracy of 150 ns.

Deep reinforcement learning‐based full‐duplex link scheduling in federated learning‐based computing for IoMT

SummaryThe rapid developments in mini‐hardware manufacturing and wireless network communications have enabled the Internet of Medical Things (IoMT) to provide continuous healthcare services over the Internet. Federated learning (FL) combined with blockchain technology has been a popular way to resolve privacy‐preserving data sharing in IoMT‐based wireless body area networks (WBANs), on the other side, communication payloads become much heavier than traditional healthcare sensor network, because central server should aggregate the model updates and orchestrate the training tasks. The high latency will lead to FL's low system efficiency. However, the existing studies on FL mainly focus on the system design and algorithm optimization, which ignore a critical problem of data transmission in the FL system. To improve the communication performance, we proposed a two‐tier scheduling algorithm in which a full‐duplex (FD) multiple access based scheduling algorithm is employed to improve channel utility and network throughput, and decrease the delay in tier II. A deep reinforcement learning (DRL) framework is used to generate the FD links between hubs and access points (APs) which jointly considers the channel state, fairness, and delay. Therefore, the DRL‐based FD Link Scheduling (R‐FDLS) algorithm is proposed. When the traffic volume is different or in various distribution scenarios, the evaluation results demonstrate that the proposed algorithm significantly improves the network communication quality, as well as has obvious advantages compared to several baselines.

Observer‐based adaptive event‐triggered neural tracking control for nonlinear cyber‐physical systems with incomplete measurements

SummaryIn this paper, an adaptive event‐triggered neural networks (NNs) tracking control problem is investigated for cyber‐physical Systems (CPSs) with incomplete measurements. The state variables can get unavailable or distorted in incomplete measurements because of data transmission problems, which can degrade the performance of the system. To solve these problems, the radial basis function neural networks (RBF NNs) control is used to approximate the unknown nonlinear function in CPSs, and the Butterworth Low‐pass Filter (LPF) is used to construct the NNs observer, which can estimate the immeasurable states. By using the Lyapunov function, the tracking error of the controller has limited to a small boundary. Based on backstepping control theory and event‐triggered theory, the control signal of the fixed threshold strategy is obtained and two adaptive controllers for CPSs are established, it can ensure that all the closed‐loop signals are uniformly ultimately bounded (UUB) in mean square and avoid the Zeno‐behavior. The simulation results confirm the feasibility and effectiveness of the controller.

Link-State Aware Hybrid Routing in the Terrestrial–Satellite Integrated Network

In this paper, we study data transmission in the Terrestrial-Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial-satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space-time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space-time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial-satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn-Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.