Transmission Packet Loss Research Articles

An Optimization Method of Large-Scale Video Stream Concurrent Transmission for Edge Computing

Concurrent access to large-scale video data streams in edge computing is an important application scenario that currently faces a high cost of network access equipment and high data packet loss rate. To solve this problem, a low-cost link aggregation video stream data concurrent transmission method is proposed. Data Plane Development Kit (DPDK) technology supports the concurrent receiving and forwarding function of multiple Network Interface Cards (NICs). The Q-learning data stream scheduling model is proposed to solve the load scheduling of multiple queues of multiple NICs. The Central Processing Unit (CPU) transmission processing unit was dynamically selected by data stream classification, as well as a reward function, to achieve the dynamic load balancing of data stream transmission. The experiments conducted demonstrate that this method expands the bandwidth by 3.6 times over the benchmark scheme for a single network port, and reduces the average CPU load ratio by 18%. Compared to the UDP and DPDK schemes, it lowers the average system latency by 21%, reduces the data transmission packet loss rate by 0.48%, and improves the overall system transmission throughput. This transmission optimization scheme can be applied in data centers and edge computing clusters to improve the communication performance of big data processing.

LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers.

Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing power of edge devices and the existing methods for feature extraction pose a significant challenge to performing diagnosis on the edge. In this paper, we propose a fast Lightweight Fault Diagnosis method for power transformers, referred to as LightFD, which integrates several technical components. Firstly, before feature extraction, we design an asymmetric Hamming-cosine window function to reduce signal spectrum leakage and ensure data integrity. Secondly, we design a multidimensional spatio-temporal feature extraction method to extract acoustic features. Finally, we design a parallel dual-layer, dual-channel lightweight neural network to realize the classification of different fault types on edge devices with limited computing power. Extensive simulation and experimental results show that the diagnostic precision and recall of LightFD reach 94.64% and 95.33%, which represent an improvement of 4% and 1.6% over the traditional SVM method, respectively.

CGCNImp: a causal graph convolutional network for multivariate time series imputation

BackgroundMultivariate time series data generally contains missing values, which can be an obstacle to subsequent analysis and may compromise downstream applications. One challenge in this endeavor is the presence of the missing values brought about by sensor failure and transmission packet loss. Imputation is the usual remedy in such circumstances. However, in some multivariate time series data, the complex correlation and temporal dependencies, coupled with the non-stationarity of the data, make imputation difficult.MehodsTo address this problem, we propose a novel model for multivariate time series imputation called CGCNImp that considers both correlation and temporal dependency modeling. The correlation dependency module leverages neural Granger causality and a GCN to capture the correlation dependencies among different attributes of the time series data, while the temporal dependency module relies on an attention-driven long short term memory (LSTM) and a time lag matrix to learn its dependencies. Missing values and noise are addressed with total variation reconstruction.ResultsWe conduct thorough empirical analyses on two real-world datasets. Imputation results show that CGCNImp achieves state-of-the-art performance when compared to previous methods.

Edge intelligence based digital twins for internet of autonomous unmanned vehicles

AbstractIt aims to explore the efficient and reliable wireless transmission and cooperative communication mechanism of Internet of Vehicles (IoV) based on edge intelligence technology. It first proposes an intelligent network architecture for IoV services by combining network slicing and deep learning (DL) technology, and then began to study the key technologies needed to achieve the architecture. It designs the cooperative control mechanism of unmanned vehicle network based on the full study of wireless resource allocation algorithm from the micro level. Second, in order to improve the safety of vehicle driving, deep reinforcement learning is used to configure the wireless resources of IoV network to meet the needs of various IoV services. The research results show that the accuracy rate of the improved AlexNet algorithm model can reach 99.64%, the accuracy rate is more than 80%, the data transmission delay is less than 0.02 ms, and the data transmission packet loss rate is less than 0.05. The algorithm model has practical application value for solving the data transmission related problems of vehicular internet communication, providing an important reference value for the intelligent development of unmanned vehicle internet.

Design of Distance Learning System for Dance Movement Based on Wireless Network Communication Technology

The traditional teaching system uses local area network to realize online teaching, which leads to high stability and hardware load of the teaching system and makes it difficult to meet the teaching demand. To solve the above problems, a dance movement distance learning system based on wireless network communication technology is designed. On the B/S architecture, the hardware control module and communication module of the system are designed. The fuzzy set principle is used to evaluate the students’ dance cognitive ability, so as to personalize the recommended teaching contents. The teaching video is compressed according to H.264/AV compression standard to reduce the system transmission and processing load. The system functionality test results show that the maximum transmission packet loss rate of the designed system is 8.3%, and the lost data does not interfere with teaching, has low computer memory consumption, and has superior performance.

An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks.

With the emergence of vehicular Internet-of-Things (IoT) applications, it is a significant challenge for vehicular IoT systems to obtain higher throughput in vehicle-to-cloud multipath transmission. Network Coding (NC) has been recognized as a promising paradigm for improving vehicular wireless network throughput by reducing packet loss in transmission. However, existing researches on NC do not consider the influence of the rapid quality change of wireless links on NC schemes, which poses a great challenge to dynamically adjust the coding rate according to the variation of link quality in vehicle-to-cloud multipath transmission in order to avoid consuming unnecessary bandwidth resources and to increase network throughput. Therefore, we propose an Adaptive Network Coding (ANC) scheme brought by the novel integration of the Hidden Markov Model (HMM) into the NC scheme to efficiently adjust the coding rate according to the estimated packet loss rate (PLR). The ANC scheme conquers the rapid change of wireless link quality to obtain the utmost throughput and reduce the packet loss in transmission. In terms of the throughput performance, the simulations and real experiment results show that the ANC scheme outperforms state-of-the-art NC schemes for vehicular wireless multipath transmission in vehicular IoT systems.

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

Today, Smart Grids (SGs), as the goal of the next-generation power grid system, span extremely wide aspects from power generation to end-user utilities. In smart grids, Energy and Information flows are mutually dependent and performance degradation of one side may have a high impact on the other side. In this work, we introduce our architecture for monitoring of Wind Turbine (WT) farms in smart grids. In our proposed system an industrial camera is embedded on a Wireless Cognitive Radio node for each WT to capture appropriate images and stream videos to the cognitive coordinator. Any packet loss in transmission between an embedded cognitive node and the coordinator can degrade peak signal-to-noise ratio (PSNR) of the received images. The image streaming is a delay sensitive transmission which should be done in harsh environments in SGs. To tackle these challenging issues, we introduce our efficient model, called JOPSS, for joint optimization of both packet size and Number of Spectrum Sensing Iterations (NSSI) during image transmission in time-restricted conditions. We define our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT). In addition, we introduce our methods based on the Minimum of Overhead Time Channel Selection (MOTS) for the efficient channel selection along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). The obtained results show that noticeable improvements in both PSNR and feature-similarity (FSIM) can be achieved on our models JOPSS and JOPSS-SAFE, respectively.

Distributed <inline-formula> <tex-math notation="LaTeX">$H_\infty$ </tex-math> </inline-formula> State Estimation for a Class of Filtering Networks With Time-Varying Switching Topologies and Packet Losses

In this paper, the distributed ${H_{\infty }}$ state estimation problem is investigated for a class of filtering networks with time-varying switching topologies and packet losses. In the filter design, the time-varying switching topologies, partial information exchange between filters, the packet losses in transmission from the neighbor filters and the channel noises are simultaneously considered. The considered topology evolves not only over time, but also by event switches which are assumed to be subjects to a nonhomogeneous Markov chain, and its probability transition matrix is time-varying. Some novel sufficient conditions are obtained for ensuring the exponential stability in mean square and the switching topology-dependent filters are derived such that an optimal ${H_{\infty }}$ disturbance rejection attenuation level can be guaranteed for the estimation disagreement of the filtering network. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical results.

Design of a wireless measurement system based on WSNs for large bridges

The structural vibration detection for large bridges is critical. A wireless measurement system based on Wireless Sensor Networks (WSNs) for large bridges was developed in this paper. The overall architecture of the wireless system is introduced; the design of wireless nodes and the principle of the central station are given in detail. Experiments are conducted in the laboratory and the Nanjing IV Yangtze River Bridge to verify the performance of the designed system. Experimental results show that the performances of the designed system are improved in the transmission distance. The system anti-jamming ability and data transmission packet loss rate are also improved compared to traditional wireless sensor systems. Meanwhile, with the external sensing interfaces, the system has a superior performance in characteristics of high sensitivity and ultra low frequency. It can easily and quickly carry out structural vibration detection for a large bridge in its construction, future operation, and maintenance periods with strong practicality and high application value.

Ad Hoc网络中的视频传输方法与技术综述<br>Overview of Video Transmission Methods and Techniques in Ad Hoc Networks

Ad Hoc网络由于拓扑变化以及节点的移动导致路径经常性中断，使得Ad Hoc网络信道误码率和传输丢包率高，这给视频传输带来了新的挑战。因此，研究适合Ad Hoc网络的视频编码和传输方法成为了当今一大热点。在结合Ad Hoc网络传输特性的基础上，从视频编码、传输和接收端同时入手，对目前Ad Hoc网络视频传输的研究现状和主要方法进行了系统的论述，并对适合Ad Hoc网络传输视频的新机制及其关键技术进行了展望。 Topology change and nodes move in Ad Hoc networks leads to regular interruption of the path, making Ad Hoc network channel bit error rate and transmission packet loss rate very high .This brings a new challenge for video transmission. As a result, research on video coding and transmission methods that suitable for Ad Hoc networks has become a hot spot today. On the basis of transmission characteristics of Ad Hoc networks, it starts with video encoding, video transmission and the receiver all together, gives a systematic introduction to the current research status and main methods of video transmission in Ad Hoc networks, moreover forecasts the future of the key technologies and new mechanisms which are suitable for video transmission in Ad Hoc networks.

Modeling and stabilization for a class of nonlinear networked control systems: A T-S fuzzy approach

The stabilization problem for a class of nonlinear networked control systems (NCSs) is investigated in this paper. A Takagi–Sugeno (T-S) fuzzy model is employed to represent the nonlinear controlled plant in the NCSs. Based on parallel distributed compensation (PDC) technique, a novel model of the nonlinear NCSs is established firstly in consideration of both network-induced delay and packet losses in transmission. Then a stability condition of the nonlinear NCSs is presented in terms of linear matrix inequalities (LMIs) by using the Lyapunov–Krasovskii functional method and the delay input approach. Furthermore, the control design of the nonlinear NCSs is developed by means of LMIs. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

Observer Detection Performance in Radiology Using a Retransmission-Free Network Communication Protocol

We measured the effect of image data loss on diagnostic accuracy to test the possibility of using a retransmission-free network communication protocol for transferring radiologic images. Mammograms transferred over a simulated network with 0%, 15%, and 25% transmission packet loss were presented randomly to 10 observers who typically read mammograms. Observers reported on the presence or absence of microcalcification clusters and the number of calcifications per cluster. Performance with 15% loss did not differ from performance with 0% loss. The 25% loss condition resulted in a significant decrease in performance. Accuracy of counting individual microcalcifications was high in all three loss conditions. Up to 15% packet loss can be tolerated without affecting diagnostic performance. These data suggest that in some radiologic applications retransmission-free communication protocols may be useful.