基于Packet Tracer的虚拟医疗网络设计与性能分析

在Packet Tracer模拟环境中针对虚拟医疗网络进行设计与性能分析。系统需求与设计部分详细考虑了医疗网络的功能需求、拓扑设计和安全性措施。通过Packet Tracer模拟环境的建立,成功模拟了医院内部网络、医疗设备网络和远程连接,符合实际医疗场景。在性能分析中,使用Packet Tracer工具对带宽、延迟、安全性和可靠性进行了全面评估。通过优化网络配置,确保实时医疗应用的高效运行,同时强调了网络的安全性和可靠性。这一综合性的设计和性能分析为虚拟医疗网络的实际部署提供了有力的支持。

An Effective Error Resilient Packetization Scheme for Progressive Mesh Transmission over Unreliable Networks

When a 3D model is transmitted over a lossy network, some model information may inevitably be missing. Under such situation, one may not be able to visualize the receiving model unless the lost model information has been retransmitted. Progressive model transmission offers an alternative to avoid the ＂all or nothing situation＂ by allowing a model to be visualized with a degraded quality when only part of the model data has been received. Unfortunately, in case some model refinement information is missing, one may still need to wait for such information to be retransmitted before the model can be rendered with a desired visual quality. To address this problem, we have developed a novel error resilient packetization scheme. We first construct a Non-Redundant Directed Acyclic Graph to encode the dependencies among the vertex splits of a progressive mesh. A special Global Graph Equipartition Packing Algorithm is then applied to partitioning this graph into several equal size sub-graphs, which is packed as packets. The packing algorithm comprises two main phases： initial partition phase and global refinement phase. Experimental results demonstrate that the proposed scheme can minimize the dependencies between packets. Hence, it reduces the delay in rendering 3D models with proper quality at the clients.

Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

Research on Sleeve Grouting Density Detection Based on the Impact Echo Method

Grouting defects are an inherent challenge in construction practices,exerting a considerable impact on the operational structural integrity of connections.This investigation employed the impact-echo technique for the detection of grouting anomalies within connections,enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes.A series of grouting completeness assessments were meticulously conducted,taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement.The findings revealed that:(i)the energy distribution for the highstrength concrete cohort predominantly occupied the frequency bands 42,44,45,and 47,whereas for other groups,it was concentrated within the 37 to 40 frequency band;(ii)the delineation of empty sleeves was effectively discernible by examining the wavelet packet energy ratios across the spectrum of frequencies,albeit distinguishing between sleeves with 50%and full grouting density proved challenging;and(iii)the wavelet packet energy analysis yielded variable detection outcomes contingent on the material attributes of the sleeves,demonstrating heightened sensitivity when applied to ultrahigh-performance concrete matrices and GFRP-reinforced steel bars.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

面向智能家居应用的物联网“场景分解式”实训设计

通过PacketTracer模拟物联网智能家居场景,并将所涉及的理论知识进行分解后纳入实训课程,使学生能够循序渐进地掌握智能设备、网络、物联网等方面知识和技术,同时理解雾计算的概念以及在智能家居场景中选择雾计算的必要性,培养解决物联网工程领域复杂工程问题的能力。

基于Packet Tracer的MQTT协议仿真实现

MQTT协议是物联网设备云边传输最常用的通信协议之一。对于物联网相关专业、学科的学生来说,理解MQTT协议的工作原理、数据包结构、消息服务质量等非常重要。通过在Packet Tracer仿真软件上进行MQTT协议网络仿真结构设计、网络连通性配置、程序设计以及相应数据包抓取,仿真MQTT协议的工作过程,分析协议数据包的结构。经过仿真分析与理论分析的对比,进一步加深对MQTT协议的理解。

仿真教学在物联网通信技术课程中的应用

作为物联网工程专业的核心课,物联网通信技术课程教学目前普遍存在重理论轻实践、实验设备不足、学生学习积极性不高等问题。针对这些问题,本文将Simulink、Packet Tracer、OneNet云平台等工具引入到课程教学中,通过可视化的仿真过程及结果,加深学生对知识点的理解,进行项目驱动式案例教学;同时,引入了国家级虚拟仿真实验项目,借助这些工具和平台,学生可以自由地、不受场地及时间限制进行物联网通信技术的仿真实验,提升学生的工程实践能力。结果表明,仿真教学手段使教学效果得到了较大提升。

基于Cisco Packet Tracer的虚拟仿真软件在“计算机网络技术”实验教学中的应用研究

作者介绍了Cisco Packet Tracer虚拟仿真软件的功能和优点,并根据当前“计算机网络技术”课程的实验教学现状,以“交换机虚拟局域网”实验为例说明Cisco Packet Tracer如何在“计算机网络技术”课程中实现虚拟仿真实验教学。通过问卷调查可知,虚拟仿真实验能够改善实验教学效果,降低实验成本,培养学生的实践操作能力和分析解决问题能力。

智能家居物联仿真实验教学设计

培养学生智能物联系统设计能力的关键是在教学中设计一系列综合性物联仿真实验,提供贴近真实项目的沉浸式训练环境。为此,将智能物联实验教学过程框架运用于开发智能家居物联实验,通过模拟真实项目实施流程的方式实现了实验全过程。首先,通过“场景设想”方式规划了智能家居需求分析和总体设计的实验要点,同时示范性地给出了实验的具体内容和要素。最后,介绍了使用Packet Tracer仿真平台完成智能家居仿真实施的实验内容。这种接近实战的实验教学,融入了复杂的物联系统建设过程,有助于学生掌握物联系统的建设方法,为智能物联实践教学指出了有效执行路径。

不同虚拟局域网间通信实验的设计与实现

针对当前高校网络课程教学实验设备短缺的现状,对比几种主流的网络模拟仿真软件,结合虚拟局域网技术的实用性,提出基于Cisco Packet Tracer的不同虚拟局域网间通信的实验设计,给出组网拓扑和配置指令,实现了利用单臂路由技术、多臂路由技术、SVI+TRUNK技术和三层交换技术的四个实验过程,并做优劣对比和实验结果验证,帮助学生更好地从理论和实践角度掌握虚拟局域网技术。

A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

High impedance fault detection in distribution network based on S-transform and average singular entropy

When a high impedance fault(HIF)occurs in a distribution network,the detection efficiency of traditional protection devices is strongly limited by the weak fault information.In this study,a method based on S-transform(ST)and average singular entropy(ASE)is proposed to identify HIFs.First,a wavelet packet transform(WPT)was applied to extract the feature frequency band.Thereafter,the ST was investigated in each half cycle.Afterwards,the obtained time-frequency matrix was denoised by singular value decomposition(SVD),followed by the calculation of the ASE index.Finally,an appropriate threshold was selected to detect the HIFs.The advantages of this method are the ability of fine band division,adaptive time-frequency transformation,and quantitative expression of signal complexity.The performance of the proposed method was verified by simulated and field data,and further analysis revealed that it could still achieve good results under different conditions.

基于Packet Tracer的网络仿真应用研究

计算机网络体系结构复杂、网络设备价格昂贵等因素造成网络的规划与设计在真实场景下构建与验证是不可行的。基于Pa cket Tracer的网络仿真在网络规划需求多样化的今天变成一种可行的技术手段。文章将通过某校园网的网络规划与仿真案例,展示基于Packet Tracer的网络仿真软件的应用。

“网络仿真平台+项目实训”教学策略探究

利用网络模拟仿真工具Packet Tracer进行实训教学,根据数学建模和对应的算法来仿真网络协议的工作原理,通过构建网络园区中的虚拟网络模型来搭建组网拓扑,较好地解决了物理环境中的客观因素限制,阶段实作评价达到了优化网络参数和提高教学效果的目的,以期优化高职学生个性化学习环境。

A consensus time synchronization protocol in wireless sensor network

Time synchronization is one of the base techniques in wireless sensor networks(WSNs).This paper proposes a novel time synchronization protocol which is a robust consensusbased algorithm in the existence of transmission delay and packet loss.It compensates for transmission delay and packet loss firstly,and then,estimates clock skew and clock offset in two steps.Simulation and experiment results show that the proposed protocol can keep synchronization error below 2μs in the grid network of 10 nodes or the random network of 90 nodes.Moreover,the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.

Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

This paper is concerned with the finite-time dissipative synchronization control problem of semi-Markov switched cyber-physical systems in the presence of packet losses, which is constructed by the Takagi–Sugeno fuzzy model. To save the network communication burden, a distributed dynamic event-triggered mechanism is developed to restrain the information update. Besides, random packet dropouts following the Bernoulli distribution are assumed to occur in sensor to controller channels, where the triggered control input is analyzed via an equivalent method containing a new stochastic variable. By establishing the mode-dependent Lyapunov–Krasovskii functional with augmented terms, the finite-time boundness of the error system limited to strict dissipativity is studied. As a result of the help of an extended reciprocally convex matrix inequality technique, less conservative criteria in terms of linear matrix inequalities are deduced to calculate the desired control gains. Finally, two examples in regard to practical systems are provided to display the effectiveness of the proposed theory.

Network traffic identification in packet sampling environment

With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and security.In recent years,packet sampling has been widely used in most network management systems.In this paper,in order to improve the accuracy of network traffic identification,sampled NetFlow data is applied to traffic identification,and the impact of packet sampling on the accuracy of the identification method is studied.This study includes feature selection,a metric correlation analysis for the application behavior,and a traffic identification algorithm.Theoretical analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling environment.Meanwhile,in this paper,the correlation analysis results in different trends according to different features.However,as long as the flow number meets the statistical requirement,the feature selection and the correlation degree will be independent of the sampling ratio.While in a high sampling ratio,where the effective information would be less,the identification accuracy is much lower than the unsampled packets.Finally,in order to improve the accuracy of the identification,we propose a Deep Belief Networks Application Identification(DBNAI)method,which can achieve better classification performance than other state-of-the-art methods.

Packet Scheduling in Rechargeable Wireless Sensor Networks under SINR Model

Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio model.They allocate different transmission slots to conflicting packets and overcome the challenges caused by the fact that the channel state changes quickly and is uncontrollable.The first algorithm proposes a prioritybased framework for packet scheduling in rechargeable sensor networks.Every packet is assigned a priority related to the transmission delay and the remaining energy of rechargeable batteries,and the packets with higher priority are scheduled first.The second algorithm mainly focuses on the energy efficiency of batteries.The priorities are related to the transmission distance of packets,and the packets with short transmission distance are scheduled first.The sensors are equipped with low-capacity rechargeable batteries,and the harvest-store-use model is used.We consider imperfect batteries.That is,the battery capacity is limited,and battery energy leaks over time.The energy harvesting rate,energy retention rate and transmission power are known.Extensive simulation results indicate that the battery capacity has little effect on the packet scheduling delay.Therefore,the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.

Minimizing Age of Information in the Uplink Multi-User Networks via Dynamic Bandwidth Allocation

Improving the information freshness is critical for the monitoring and controlling applications in the cellular Internet of Things(IoT).In this paper,we are interested in optimizing the bandwidth allocation dynamically to improve the information freshness of the short packet based uplink status updates,which is characterized by a recently proposed metric,age of information(Ao I).We first design a status update scheme with channel distribution information(CDI).By relaxing the hard bandwidth constraint and introducing a Lagrangian multiplier,we first decouple the multi-MTCD bandwidth allocation problem into a single MTCD Markov decision process(MDP).Under the MDP framework,after variable substitution,we obtain the single-MTCD status update scheme by solving a linear programming problem.Then,we adjust the Lagrangian multiplier to make the obtained scheme satisfy the relaxed bandwidth constraint.Finally,a greedy policy is built on the proposed scheme to adjust the bandwidth allocation in each slot to satisfy the hard bandwidth constraint.In the unknown environment without CDI,we further design a bandwidth allocation scheme which only maximizes the expected sum Ao I drop within each time slot.Simulation results show that in terms of AoI,the proposed schemes outperform the benchmark schemes.