Real Network Applications Research Articles

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

STI: A self-evolutive traffic identification system for unknown applications based on improved random forest

Artificial intelligence technology has proven potential and effective in traffic identification for network management and security. However, the accuracy of its identification is easily influenced by the massive unknown traffic. Given the fact of numerous unknown applications in real networks and even more as time goes by, a promising traffic identification system should have the ability to discover unknown applications and recognize their traffic. In this paper, an innovative and comprehensive traffic identification system, called STI, is proposed, which can achieve fulfilling high precision both on known and unknown traffic identification. More importantly, STI can self-evolve to identify incoming unknown applications and corresponding training samples based on a novel clustering process with minimal manual involvement. In addition, an improved random forest and a novel similarity calculation method are proposed and applied to STI to enhance the classification performance. Experiments on real network traffic demonstrate the core advantages of the proposed system.

Temporal network modeling with online and hidden vertices based on the birth and death process

Complex networks have played an important role in describing real complex systems since the end of the last century. Recently, research on real-world data sets reports intermittent interaction among social individuals. In this paper, we pay attention to this typical phenomenon of intermittent interaction by considering the state transition of network vertices between online and hidden based on the birth and death process. By continuous-time Markov theory, we show that both the number of each vertex’s online neighbors and the online network size are stable and follow the homogeneous probability distribution in a similar form, inducing similar statistics as well. In addition, all propositions are verified via simulations. Moreover, we also present the degree distributions based on small-world and scale-free networks and find some regular patterns by simulations. The application in fitting real networks is discussed.

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

AbstractEffective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management.This article is categorized under: Engineering Water > Methods Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

Complex Network Evolution Model Based on Turing Pattern Dynamics

Complex network models are helpful to explain the evolution rules of network structures, and also are the foundations of understanding and controlling complex networks. The existing studies (e.g., scale-free model, small-world model) are insufficient to uncover the internal mechanisms of the emergence and evolution of communities in networks. To overcome the above limitation, in consideration of the fact that a network can be regarded as a pattern composed of communities, we introduce Turing pattern dynamic as theory support to construct the network evolution model. Specifically, we develop a Reaction-Diffusion model according to Q-Learning technology (RDQL), in which each node regarded as an intelligent agent makes a behavior choice to update its relationships, based on the utility and behavioral strategy at every time step. Extensive experiments indicate that our model not only reveals how communities form and evolve, but also can generate networks with the properties of scale-free, small-world and assortativity. The effectiveness of the RDQL model has also been verified by its application in real networks. Furthermore, the depth analysis of the RDQL model provides a conclusion that the proportion of exploration and exploitation behaviors of nodes is the only factor affecting the formation of communities. The proposed RDQL model has potential to be the basic theoretical tool for studying network stability and dynamics.

On the Development of Overcurrent Relay Optimization Problem for Active Distribution Networks

The goal of this review paper will be to address complex optimization functions in the area of overcurrent relay optimization, to inspect and valorize their objectives and to critically examine their application in real distribution networks. Special emphasis will be put on observing the optimization of discrimination time between primary and backup relay pairs, and methods which prevent the latter becoming penalty functions will be shown. Furthermore, the impact of distributed generation units on the overcurrent relay optimization problem will be elaborated in detail. Protection settings of all the existing and new relays must be thoroughly inspected before the connection of new production units, because contributing fault currents may have a significant effect on the solution of the overcurrent optimization problem. This is because the relays being used are of an inverse-type. Next, meshed network operation will be critically assessed in comparison with the actual practice of radial operation, and some topologies will be highlighted for future research, while others will be discarded. The distribution network in mesh operation implicitly suggests using numerical relays on both sides of protected elements (usually lines), so their total number will be much higher than in radial operation. Finally, a relatively new concept of adaptive distribution network protection will be reviewed, and its potential application to the overcurrent relay optimization problem will be examined. The purpose of this review paper will be to lay a groundwork for future research papers by inspecting the overall development of the overcurrent relay optimization problem for contemporary and future active distribution networks.

Simple: Statistical Inference on Membership Profiles in Large Networks

AbstractNetwork data are prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference on membership profiles in large networks (SIMPLE) in the setting of degree-corrected mixed membership model, where the null hypothesis assumes that the pair of nodes share the same profile of community memberships. In the simpler case of no degree heterogeneity, the model reduces to the mixed membership model for which an alternative more robust test is also proposed. Both tests are of the Hotelling-type statistics based on the rows of empirical eigenvectors or their ratios, whose asymptotic covariance matrices are very challenging to derive and estimate. Nevertheless, their analytical expressions are unveiled and the unknown covariance matrices are consistently estimated. Under some mild regularity conditions, we establish the exact limiting distributions of the two forms of SIMPLE test statistics under the null hypothesis and contiguous alternative hypothesis. They are the chi-square distributions and the noncentral chi-square distributions, respectively, with degrees of freedom depending on whether the degrees are corrected or not. We also address the important issue of estimating the unknown number of communities and establish the asymptotic properties of the associated test statistics. The advantages and practical utility of our new procedures in terms of both size and power are demonstrated through several simulation examples and real network applications.

NETWORK APPLICATION-LAYER PROTOCOL CLASSIFICATION BASED ON FUZZY DATA AND NEURAL NETWORK PROCESSING

A technique of network packet classification on the application layer is proposed. It is based on fuzzy data processing and artificial neural networks to define the network packet belongingness to one of the known network protocols. In the suggested technique, two main data processing stages are distinguished. At the first stage data is preprocessed by fuzzy logic methods. At the second stage the packets are classified by means of an artificial neural network. An artificial neural network having the proposed architecture allows one to determine the following aspects: the type of secure network protocol, the internal state of the network protocol based on the application of logical decision rules, and the type of network application using the identified protocol. The architecture of the bench environment for field tests is considered. During the experiments, the traffic of real network applications that are used around the world was used. Experimental assessment of the offered technique showed rather high quality and work speed of the developed classifier.

A Web-Based Honeypot in IPv6 to Enhance Security

IPv6 is a next-generation IP protocol that replaces IPv4. It not only expands the number of network address resources but also solves the problem of multiple access devices connected to the Internet. While IPv6 has brought excellent convenience to the public, related security issues have gradually emerged, and an assessment of the security situation in IPv6 has also become more important. Unlike passive defense, the honeypot is a security device for active defense. The real network application and the fake network application, disguised by the honeypot, are located on a similar subnet, and provide a network application service; but, in both cases, behavior logs from unauthorized users are caught. In this manner, and to protect web-based applications from attacks, this article introduces the design and implementation of a web-based honeypot that includes a weak password module and an SQL inject module, which supports the IPv6 network to capture unauthorized access behavior. We also propose the Security Situation Index (SSI), which can measure the security situation of the network application environment. The value of SSI is established according to the different parameters that are based on honeypots. There is a firewall outside the test system environment, so the obtained data should be used as the real invasion data, and the captured behavior is not a false positive. Threats can be spotted smartly by deploying honeypots; this paper demonstrates that the honeypot is an excellent method of capturing malicious requests and can be measured with the SSI of the whole system. According to the information, the administrator can modify the current security policy, which can improve the security level of a whole IPv6 network system.

Ant Colony Based Data Aggregation in Real Time Wireless Sensor Networks

Sensing the natural phenomenon, sensing the eventual activities, sensing the abrupt change in human developed systems has a wide number of applications. To sense, one needs the tiny sensor devices called sensor nodes. To collect the data from these devices, one has to arrange these tiny devices into a intelligently managed network, called as sensor networks. Due to mostly unattended application areas and zones, these devices are supposed to be wireless which adds one more dimension to our discussion, known as wireless sensor networks. So basically these are an intelligent arrangement of small, tiny sensing devices with wide array of attributes, namely, sensing ability, low communication range mechanism, limited power sources, small processing ability, small memory. With these limitations, even handling a small data generated/sensed in the network becomes crucial to handle. Therefore there has to be an effective data aggregation technique. The prime goal of any data aggregation algorithms is to collect/retrieve/gather the data in an efficient possible way so that the effort to communicate the data over the network can be reduced. Less work from the network means less energy dissipated, thus lifetime can be enhanced. An effective data aggregation technique reduces the latency also. Small network latency is crucial for time critical applications in real time wireless sensor networks. Ant colony based optimization provides us such a possibility to find an optimal route for efficient data aggregation with significantly reduced network latency. In our work, Ant colony based Data Aggregation in Real Time wireless sensor networks (ACDAR), Ants use their pheromones to establish a new path or follow already established path by other ants. Similarly data packets in wireless sensor networks can take advantage of freshly established paths by Route Ants. As the results received, it is verified that number of packets successfully delivered to the sink have increased and the miss ratio, cumulative packets consumption have improved, network latency has decreased.

Determination of experimental sensitivity capacities against crustal movements of dense-geodetic networks: a real GPS network application

The classical densification geodetic networks, the permanent or the fundamental densification GPS/GNSS networks, and the deformation monitoring networks must provide the precision and reliability requirements. These networks should also be able to detect the possible earth crust movements. In designed densification networks, if the total observation weights at a network point rise, the sensitivity capacity of the point increases at the same rate. However, the controllability of these observations becomes weaker in comparison with the neighbor observations. This fact is a fundamental design problem that must be solved in the evaluation stage of geodetic networks. In this study, a query algorithm is recommended for a solution of the described design problem.

A functional form with a physical meaning for the macroscopic fundamental diagram

The macroscopic fundamental diagram (MFD) relates vehicle accumulation and production of travel in an urban network with a well-defined and reproducible curve. Thanks to this relationship, the MFD offers a wide range of applications, most notably for traffic control. Recently, more and more empirical MFDs have been documented, providing further insights and facilitating their application in real urban networks. So far, however, no generally accepted functional form has been identified.This paper proposes a functional form for the MFD that is based on the smooth approximation of an upper bound of technologically feasible traffic states (uMFD). In this functional form, the uMFD can be either estimated from MFD measurements or defined a-priori, either analytically or with additional measurements in the network, while the smoothing to the uMFD is quantified with a single parameter λ. The uMFD can in principle be any multi-regime function, but we find that a trapezoidal shape with only four parameters, all physically meaningful, models the familiar shape of the MFD very well as shown with empirical data from Marseille, London, Lucerne, Yokohama, and Zurich. Further, we point to novel applications and analyses based on the interpretation of λ that would otherwise not be possible without this new functional form.

FEP: High Fidelity Experiment Platform for Mobile Networks

To make experiments more convenient and then quickly deploy theoretical research results to practical systems, e.g., porting multipath transmission control protocol (MPTCP) to Android, or testing real network protocols or network applications in real mobile operating systems (MOS) in mobile networks, we propose and design a high-fidelity experiment platform for mobile networks (FEP) which seamlessly connects academia and industry in the research area of mobile networks. By using FEP, engineers and researchers can share the same set of code. We implement FEP by adopting Fedora, NS-3, VirtualBox, Android, and Docker. FEP consists of two parts: real world and virtual world. The real world includes commercial off-the-shelf smartphones with MOS installed. The virtual world includes a simulation platform, and most of experiment and development works are conducted on the platform. The key point of connecting the two parts is to port a real MOS to VirtualBox, and consequently, the modified code on the simulation platform can be almost directly used in the real MOS, which is why we call it high fidelity. To the best of our knowledge, it is the first time to do that. To demonstrate the high fidelity, we describe the process of porting MPTCP to Android and show a demonstration of Information System for Disaster Relief in MANET. Experimental results show that FEP has the practical value and achieves high accuracy and code high fidelity.

A priori sensitivity analysis for densification GPS networks and their capacities of crustal deformation monitoring: a real GPS network application

Abstract. The main purpose of this study is to investigate the possibilities of monitoring crustal movement, during collective evaluation of first- and second-order GPS densification networks. The new criteria, which are independent from the network datum, are widely used for the evaluation and quality processing of geodetic networks. In this study, the values of detectable minimum displacement for a real GPS network are investigated as well as how sensitivity levels are affected by additional datum conditions, which are added to the adjustment model.

Study and Design on Vulnerability Discovering of Network Protocols

Discovering the vulnerabilities in network protocols is the key for network attacks and defenses. Basing on the analysis of some currently used vulnerability discovering methods, a method combining reverse engineering and fuzzy testing is put forward for vulnerability discovering of network protocols, where reverse engineering method is first used to analyze the details of the communication protocol, then fuzzy testing is used to test the target software. This combination can promote the efficiency of the fuzzy testing. By using this method to test a real network application, several vulnerabilities are discovered, which verifies the validity of this method.

Belief functions in telecommunications and network technologies: an overview

In the last few years, evidence theory, also known as Dempster-Shafer theory or belief functions theory, have received growing attention in many fields such as artificial intelligence, computer vision, telecommunications and networks, robotics, and finance. This is due to the fact that imperfect information permeates the real-world applications, and as a result, it must be incorporated into any information system that aims to provide a complete and accurate model of the real world. Although, it is in an early stage of development relative to classical probability theory, evidence theory has proved to be particularly useful to represent and reason with imperfect information in a wide range of real-world applications. In such cases, evidence theory provides a flexible framework for handling and mining uncertainty and imprecision as well as combining evidence obtained from multiple sources and modeling the conflict between them. The purpose of this paper is threefold. First, it introduces the basics of the belief functions theory with emphasis on the transferable belief model. Second, it provides a practical case study to show how the belief functions theory was used in a real network application, thereby providing guidelines for how the evidence theory may be used in telecommunications and networks. Lastly, it surveys and discusses a number of examples of applications of the evidence theory in telecommunications and network technologies.

Real road network application of a new microsimulation tool: TRITONE

The aim of the paper is to carry out a comparative study of 13 different traffic flow models available in TRITONE, a new road traffic simulator that specializes particularly in quantitative road safety assessment. After a short introduction on a traffic flow modelling, a description of the TRITONE functionality is given and various types of behavioural models available in this tool are presented in brief. Then a part of Poznan (Poland) network that served as the study area, was illustrated. The following section lists all the models used in the research and provides a comparison of the results obtained with these models. The article ends with conclusions on the results’ quality of individual models.

Navigation in spatial networks: A survey

The study on the navigation process in spatial networks has attracted much attention in recent years due to the universal applications in real communication networks. This article surveys recent advances of the navigation problem in spatial networks. Due to the ability to overcome scaling limitations in utilizing geometric information for designing navigation algorithms in spatial networks, we summarize here several important navigation algorithms based on geometric information on both homogeneous and heterogeneous spatial networks. Due to the geometric distance employed, the cost associated with the lengths of additional long-range connections is also taken into account in this survey. Therefore, some contributions reporting how the distribution of long-range links’ lengths affects the average navigation time are summarized. We also briefly discuss two other related processes, i.e. the random walk process and the transportation process. Finally, a few open discussions are included at the end of this survey.

EstiNet openflow network simulator and emulator

In this article, we introduce the EstiNet OpenFlow network simulator and emulator, and present its support for testing the functions and evaluating the performances of software-defined networks? OpenFlow controller?s application programs. EstiNet uses an unique kernel reentering simulation methodology to enable unmodified real applications to run on nodes in its simulated network. As a result, without any modification, real NOX/POX or Floodlight OpenFlow controllers can readily run on a host in an EstiNet simulated network to control thousands of simulated OpenFlow switches. EstiNet has the characteristics of a simulator and an emulator at the same time. It combines the advantages of the simulation and emulation approaches without their respective shortcomings. EstiNet uses real OpenFlow controller programs, real network application programs, and the real TCP/IP protocol stack in the Linux kernel to generate correct, accurate, and repeatable SDN application performance results. In this article, we compare EstiNet with ns-3 and Mininet regarding their capabilities, performance, and scalability.

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided.