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。

Position-aware packet loss optimization on service function chain placement

The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.

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.

Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method

Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration.

基于PacketTracer和Onenet的物联网家居系统

针对实物物联网家居成本过高、搭建过程复杂的问题,文中使用PacketTracer和OneNet云平台设计了一种物联网智能家居系统。该系统使用PacketTracer方法进行了设计,并通过MCU将智能家居设备远程接入OneNet云平台。测试结果表明,与传统的实物物联网家居相比,该系统不仅缩短了设计周期,还能实现对家居设备的远程控制。

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.

基于Packet Tracer的智慧校园网络设计及仿真

为了适应现代教育的发展,对传统校园网络进行智能化改造。针对智慧校园网络的需求,着重介绍了设计思路,并使用Packet Tracer网络仿真软件构建了智慧校园网络模型,利用冗余设计、端口聚合、负载均衡等技术使校园网络更加可靠、稳定。该模型主要利用多种传感器、物联网服务器、SBC/MCU微处理器以及智能网关实现不同的物联网功能,用户可以利用手机或PC终端远程登录物联网服务器,监控校内电子设备、网络设备。该设计能够为智慧校园网络的升级改造提供参考,有力推进校园教育信息化建设。

基于Packet Tracer的VLAN间路由仿真实验设计

每个虚拟局域网(VLAN)在逻辑上都相当于一个独立的网络,拥有自己的广播域,VLAN之间不能直接通信,但是有时候又需要在不同网段间跨VLAN进行通信,因此研究使用Packet Tracer模拟软件实现了不同VLAN间路由的配置。

Cisco packet tracer在计算机网络实训中的应用

随着信息技术的飞速发展,计算机网络实训已成为信息技术教育中不可或缺的一环。在这一过程中,实训工具的选择至关重要,Cisco Packet Tracer提供了一个虚拟的实验环境,用户可以在其中模拟各种网络设备,如交换机、路由器和服务器等,并通过拖拽的方式构建网络拓扑结构。该软件作为一款功能强大的网络模拟软件,凭借其直观的操作界面、丰富的设备库和灵活的组网功能,在计算机网络实训中得到广泛应用。本文就Cisco packet tracer在计算机网络实训中的应用进行探讨。

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。

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。

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.

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.

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。

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.

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.

Quantum Mechanics: Harmonic Wave-Packets, Localized by Resonant Response in Dispersion Dynamics

From a combination of Maxwell’s electromagnetism with Planck’s law and the de Broglie hypothesis, we arrive at quantized photonic wave groups whose constant phase velocity is equal to the speed of light c = ω/k and to their group velocity dω/dk. When we include special relativity expressed in simplest units, we find that, for particulate matter, the square of rest mass , i.e., angular frequency squared minus wave vector squared. This equation separates into a conservative part and a uniform responsive part. A wave function is derived in manifold rank 4, and from it are derived uncertainties and internal motion. The function solves four anomalies in quantum physics: the point particle with prescribed uncertainties;spooky action at a distance;time dependence that is consistent with the uncertainties;and resonant reduction of the wave packet by localization during measurement. A comparison between contradictory mathematical and physical theories leads to similar empirical conclusions because probability amplitudes express hidden variables. The comparison supplies orthodox postulates that are compared to physical principles that formalize the difference. The method is verified by dual harmonics found in quantized quasi-Bloch waves, where the quantum is physical;not axiomatic.

Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity

As energy-related problems continue to emerge,the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration.Renewable energy is becoming increasingly important,with solar power accounting for the most significant proportion of renewables.As the scale and importance of solar energy have increased,cyber threats against solar power plants have also increased.So,we need an anomaly detection system that effectively detects cyber threats to solar power plants.However,as mentioned earlier,the existing solar power plant anomaly detection system monitors only operating information such as power generation,making it difficult to detect cyberattacks.To address this issue,in this paper,we propose a network packet-based anomaly detection system for the Programmable Logic Controller(PLC)of the inverter,an essential system of photovoltaic plants,to detect cyber threats.Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants.The analysis shows that Denial of Service(DoS)and Manin-the-Middle(MitM)attacks are primarily carried out on inverters,aiming to disrupt solar plant operations.To develop an anomaly detection system,we performed preprocessing,such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data.The Random Forest model showed the best performance with an accuracy of 97.36%.The proposed system can detect anomalies based on network packets,identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants,and enhance the security of solar plants.

Robust fault detection for delta operator switched fuzzy systems with bilateral packet losses

Considering packet losses, time-varying delay, and parameter uncertainty in the switched fuzzy system, this paper designs a robust fault detection filter at any switching rate and analyzes the H∞ performance of the system. Firstly, the Takagi-Sugeno(T-S) fuzzy model is used to establish a global fuzzy model for the uncertain nonlinear time-delay switched system,and the packet loss process is modeled as a mathematical model satisfying Bernoulli distribution. Secondly, through the average dwell time method and multiple Lyapunov functions, the exponentially stable condition of the nonlinear network switched system is given. Finally, specific parameters of the robust fault detection filter can be obtained by solving linear matrix inequalities(LMIs). The effectiveness of the method is verified by simulation results.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.