Experimental Evaluation of TCP and ICMP in Network Measurement

Network measurement is an important approach to understand network behaviors, which has been widely studied. Both Transfer Control Protocol （TCP） and Interact Control Messages Protocol （ICMP） are applied in network measurement, while investigating the differences between the measured results of these two protocols is an important topic that has been less investigated. In this paper, to compare the differences between TCP and ICMP when they are used in measuring host connectivity, RTT, and packet loss rate, two groups of comparison programs have been designed, and after careful evaluation of the program parameters, a lot of comparison experiments are executed on the Internet. The experimental results show that, there are significant differences between the host connectivity measured using TCP or ICMP; in general, the accuracy of connectivity measured using TCP is 20%- 30% higher than that measured using ICMP. The case of RTT and packet loss rate is complicated, which are related to path loads and destination host loads. While commonly, the RTF and packet loss rate＂ measured using TCP or ICMP are very close. According to the experimental results, some advices are also given on protocol selection for conducting accurate connectivity, RTF and packet loss rate measurements.

HSCP:a hardware and software combined platform to improve precision in active network measurement

In this paper,an active network measurement platform is proposed which is a combination of hardware and software.Its innovation lies in the high performance of hardware combined with features that the software is easy to program,which retains software flexibility at the same time.By improving the precision of packet timestamp programmable hardware equipment,it provides packet sending control more accurately and supports the microsecond packet interval.We have implemented a model on the NetMagic platform,and done some experiments to analyze the accuracy difference of the user,the kernel and hardware timestamp.

SDNMS: A Software Defined Network Measurement System for NFV Networks

As a promising technology to completely transform how we architect, deploy, operate and manage various networks, software-based Network Function Virtualization(NFV) enables hardware-independent, flexible, fast and efficient network service provision. With the increasing popularity of NFV paradigm, the Internet has also transformed to be a hybrid environment where NFV-based network entities coexist with traditional network devices. To facilitate our understanding, design, evaluate and manage of such novel network environment, there is a great need for a new NFV-compatible network measurement system which is still in absent so far. To bridge this gap, a system, named Software Defined Network Measurement System(SDNMS), is presented in this paper. Firstly, the architecture of SDNMS is proposed. In this architecture, a formal method to describe the working mode of the network measurement is defined. This method can also be utilized to generate a network measurement middle box(NMMB) in a specific location of the NFV network according to the customized description file, and to flexibly deploy the network measurement function. Secondly, the technology of virtual network measurement function(VNMF) combined with LXC is studied to form NMMB function. Thirdly, a control method is presented to control the start, stop, and update NMMB to form a specific network measurement system function. Finally, a prototype of SDNMS with network monitoring function to monitor network performance anomalies and locate faults is introduced. Experimental results have shown that SDNMS architecture and related technologies are feasible and effective to deploy and control network measurement functions in NFV networks. We hope SDNMS could provide a new method for practitioners to conduct network management and research at the age of NFV.

A Measurement Study of the Ethereum Underlying P2P Network

This work carried out a measurement study of the Ethereum Peer-to-Peer(P2P)network to gain a better understanding of the underlying nodes.Ethereum was applied because it pioneered distributed applications,smart contracts,and Web3.Moreover,its application layer language“Solidity”is widely used in smart contracts across different public and private blockchains.To this end,we wrote a new Ethereum client based on Geth to collect Ethereum node information.Moreover,various web scrapers have been written to collect nodes’historical data fromthe Internet Archive and the Wayback Machine project.The collected data has been compared with two other services that harvest the number of Ethereumnodes.Ourmethod has collectedmore than 30% more than the other services.The data trained a neural network model regarding time series to predict the number of online nodes in the future.Our findings show that there are less than 20% of the same nodes daily,indicating thatmost nodes in the network change frequently.It poses a question of the stability of the network.Furthermore,historical data shows that the top ten countries with Ethereum clients have not changed since 2016.The popular operating system of the underlying nodes has shifted from Windows to Linux over time,increasing node security.The results have also shown that the number of Middle East and North Africa(MENA)Ethereum nodes is neglected compared with nodes recorded from other regions.It opens the door for developing new mechanisms to encourage users from these regions to contribute to this technology.Finally,the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

Distributed sampling measurement method of network traffic in high-speed IPv6 networks

With the advent of large-scale and high-speed IPv6 network technology, an effective multi-point traffic sampling is becoming a necessity. A distributed multi-point traffic sampling method that provides an accurate and efficient solution to measure IPv6 traffic is proposed. The proposed method is to sample IPv6 traffic based on the analysis of bit randomness of each byte in the packet header. It offers a way to consistently select the same subset of packets at each measurement point, which satisfies the requirement of the distributed multi-point measurement. Finally, using real IPv6 traffic traces, the conclusion that the sampled traffic data have a good uniformity that satisfies the requirement of sampling randomness and can correctly reflect the packet size distribution of full packet trace is proved.

A Cross-System Invocation Platform Based on Distributed Network Performance Measurement System

The Distributed Network Performance Measurement Sys-tem provides functions to derive performance indices of networks and services, which are significant for Network Management System. To make these two systems cooperate, we realize this cross-system invocation platform, using Web Service, a mechanism which allows two systems to exchange data over the internet through publishing interfaces [1]. There are several mature Web Service frameworks, Apache Axis2, Apache CXF etc. In this paper we choose Apache Axis2 to achieve the objective that the Network Management System can invocate the net-work performance measurement functions via the Web Services.

Behavior Measurement Model Based on Prediction and Control of Trusted Network

In order to construct the trusted network and realize the trust of network behavior,a new multi-dimensional behavior measurement model based on prediction and control is presented.By using behavior predictive equation,individual similarity function,group similarity function,direct trust assessment function,and generalized predictive control,this model can guarantee the trust of an end user and users in its network.Compared with traditional measurement model,the model considers different characteristics of various networks.The trusted measurement policies established according to different network environments have better adaptability.By constructing trusted group,the threats to trusted group will be reduced greatly.Utilizing trusted group to restrict individuals in network can ensure the fault tolerance of trustworthiness of trusted individuals and group.The simulation shows that this scheme can support behavior measurement more efficiently than traditional ones and the model resists viruses and Trojans more efficiently than older ones.

A Trusted Measurement Scheme Suitable for the Clients in the Trusted Network

The trusted network connection is a hot spot in trusted computing field and the trust measurement and access control technology are used to deal with network security threats in trusted network.But the trusted network connection lacks fine-grained states and real-time measurement support for the client and the authentication mechanism is difficult to apply in the trusted network connection,it is easy to cause the loss of identity privacy.In order to solve the abovedescribed problems,this paper presents a trust measurement scheme suitable for clients in the trusted network,the scheme integrates the following attributes such as authentication mechanism,state measurement,and real-time state measurement and so on,and based on the authentication mechanism and the initial state measurement,the scheme uses the realtime state measurement as the core method to complete the trust measurement for the client.This scheme presented in this paper supports both static and dynamic measurements.Overall,the characteristics of this scheme such as fine granularity,dynamic,real-time state measurement make it possible to make more fine-grained security policy and therefore it overcomes inadequacies existing in the current trusted network connection.

Deep learning-assisted common temperature measurement based on visible light imaging

Real-time,contact-free temperature monitoring of low to medium range(30℃-150℃)has been extensively used in industry and agriculture,which is usually realized by costly infrared temperature detection methods.This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network(CNN).A mean-square error of<1.119℃was reached in the temperature measurements of low to medium range using the CNN and the visible light images.Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN.Moreover,the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training.Compared to the conventional machine learning algorithms mentioned in the recent literatures,this real-time,contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm

Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.

Network Fault Analysis from Passive Measurement

A new passive method for automatic dis-covery and bcation of network failure is proposed. This method employs a passive measurement to collect infonmtion and events from network traffic, and em-ploys a rrodel-based reasoning system to detect and locate network faults. Measurement points are de-ployed in a backbone network to capture the traffic and then evaluate the Quality of Service （QoS） metrics of end-to-end IP conversations. A muting rrodel is al-so established for the observed network to simulate the attributes and activities of reuters and links. This muting model also deduces the muting path for each IP conversation, and thus the QoS metrics of IP con-versations are mapped into the metrics of paths. With the inforrmtion of shared links of overlapping paths and network torrography technique, the QoS metrics of links can also be estimated, and the poorly rated links are picked out as failure points. This method is imple-mented in a tool named FaultMan, which is deployed in a campus network. Test results have shown its availa-bility in rriddle-scale networks.

Application of New Type BP Neural Networks for Magnetic Measurement

Magnetic measurement is a typical inverse problem in Biomedical field. In this kind of problem we always need to locate the positions and moments of one or more magnetic dipoles. Although using the traditional methods to solve this kind of inverse problem has all kinds of shortcomings, BPNN (Back Propagation Neural Networks) method can be used to solve this typical inverse problem fast enough for real time measurement. In the traditional BPNN method, gradient descent search method is performed for error propagation. In this paper the authors propose a new algorithm that Newton method is performed for error propagation. For the cost function is highly nonconvex in the magnetic measurement problem, the new kind of BPNN can get convergent results quickly and precisely. A simulation result for this method is also presented.

Facing a Problem of Electrical Energy Quality in Ship Networks-measurements, Estimation, Control

In this paper, electrical energy quality and its indices in ship electric networks are introduced, especially the meaning of electrical energy quality terms in voltage and active and reactive power distribution indices. Then methods of measurement of marine electrical energy indices are introduced in details and a microprocessor measurement-diagnosis system with the function of measurement and control is designed. Afterwards, estimation and control of electrical power quality of marine electrical power networks are introduced. And finally, according to the existing method of measurement and control of electrical power quality in ship power networks, the improvement of relative method is proposed.

Estimation of spatially distributed processes using mobile sensor networks with missing measurements

This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing measurements.The randomly missing measurements are introduced in order to better reflect the reality in the sensor network.To improve the estimation performance for the spatially distributed process,a network of sensors which are allowed to move within the spatial domain is used.We aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that,for all possible missing measurements,the estimation error system is globally asymptotically stable in the mean square sense.By constructing Lyapunov functionals and using inequality analysis,the guidance scheme of every sensor and the convergence of the estimation error system are obtained.Finally,a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.

EQUIVALENT TWO-PORT MEASUREMENT EQUATIONS FOR RECIPROCAL MULTIPORT NETWORK AND THEIR APPLICATIONS

Equivalent two-port measurement equations for reciprocal n-port network are de-rived.As an example,applications of these equations,measurement details and data-processingmethods are discussed for any three-port network.To realize rapid and automatic measure-ments of S-parameters of any reciprocal multiport network,a set of measurement system whichis composed of auto-slotted line and program controlled movable shorts is presented in this paper.Experimental data are given for an H-plane Tee three-port network.

Fault location by one-terminal measurement in distribution network

Presents the theory behind, the system design of the acquisition of parameters for and the experiment on the fault location by one terminal measurement in actual distribution network, and some of laws governing the on site acquisition of parameters and fault location established through experimental research on actual power distribution lines.

Application of Wireless Network Positioning Technology Based on GPS in Geographic Information Measurement

Based on the analysis of the advantages and disadvantages of GPS positioning system in practical application,this paper proposes the combination of wireless network positioning technology and GPS positioning system to overcome the low accuracy of GPS positioning system in the case of occlusion.This paper introduces in detail the principle of the application of wireless network positioning technology based on GPS positioning system in geographic information measurement,and illustrates its practical application in production by taking coal mine positioning as an example.

New Approach for 3D Shape Measurement Based on Color-Coded Fringe and Neural Network

A new 3D surface contouring and ranging system based on digital fringe projection and phase shifting technique is presented. Using the phase-shift technique, points cloud with high spatial resolution and limited accuracy can be generated. Stereo-pair images obtained from two cameras can be used to compute 3D world coordinates of a point using traditional active triangulation approach, yet the camera calibration is crucial. Neural network is a well-known approach to approximate a nonlinear system without an explicit physical model, in this work it is used to train the stereo vision application system to calculating 3D world coordinates such that the camera calibration can be bypassed. The training set for neural network consists of a variety of stereo-pair images and the corresponding 3D world coordinates. The picture elements correspondence problem is solved by using projected color-coded fringes with different orientations. Color imbalance is completely eliminated by the new color-coded method. Once the high accuracy correspondence of 2D images with 3D points is acquired, high precision 3D points cloud can be recognized by the well trained net. The obvious advantage of this approach is that high spatial resolution can be obtained by the phase-shifting technique and high accuracy 3D object point coordinates are achieved by the well trained net which is independent of the camera model works for any type of camera. Some experiments verified the performance of the method.