Development Of Network Applications Research Articles

Reinforcement learning-based SDN routing scheme empowered by causality detection and GNN.

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning

With the continuous development of the 6G mobile network, computing-intensive and delay-sensitive onboard applications generate task data traffic more frequently. Particularly, when multiple intelligent agents are involved in tasks, limited computational resources cannot meet the new Quality of Service (QoS) requirements. To provide a satisfactory task offloading strategy, combining Multi-Access Edge Computing (MEC) with artificial intelligence has become a potential solution. In this context, we have proposed a task offloading decision mechanism (TODM) based on cooperative game and deep reinforcement learning (DRL). A joint optimization problem is presented to minimize both the overall task processing delay (OTPD) and overall task energy consumption (OTEC). The approach considers task vehicles (TaVs) and service vehicles (SeVs) as participants in a cooperative game, jointly devising offloading strategies to achieve resource optimization. Additionally, a proximate policy optimization (PPO) algorithm is designed to ensure robustness. Simulation experiments confirm the convergence of the proposed algorithm. Compared with benchmark algorithms, the presented scheme effectively reduces delay and energy consumption while ensuring task completion.

Application of Artificial Neural Networks in Polymer Composites: A Review

Artificial neural networks (ANN), which have been a hot topic in the field of artificial intelligence (AI) since the 1980s, are widely applied these years for their strong ability in the field of nonlinear mapping, pattern recognition, robots, automatic control, biology, economy and so on. This review presents and summarizes the history of artificial neural networks, briefly introducing the application of artificial neural networks. After that, the paper focuses on an overview of research advances in neural networks for polymer composites and introduces several classical categories of applications. Finally, we look ahead to the development of neural network applications in polymer composites and provide a future outlook for the application of artificial neural network in polymer composites.

Efficiency metrics for auditory neuromorphic spike encoding techniques using information theory

Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency metrics based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency metrics are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike Coding, Send-on-Delta coding, Ben’s Spiker Algorithm, and Leaky Integrate-and-Fire (LIF) coding. The results show that LIF coding has the overall best performance in terms of coding, computational, and energy efficiency.

Technological Transformation of Telco Operators towards Seamless IoT Edge-Cloud Continuum.

This article investigates and discusses challenges in the telecommunication field from multiple perspectives, both academic and industry sides are catered for, surveying the main points of technological transformation toward edge-cloud continuum from the view of a telco operator to show the complete picture, including the evolution of cloud-native computing, Software-Defined Networking (SDN), and network automation platforms. The cultural shift in software development and management with DevOps enabled the development of significant technologies in the telecommunication world, including network equipment, application development, and system orchestration. The effect of the aforementioned cultural shift to the application area, especially from the IoT point of view, is investigated. The enormous change in service diversity and delivery capabilities to mass devices are also discussed. During the last two decades, desktop and server virtualization has played an active role in the Information Technology (IT) world. With the use of OpenFlow, SDN, and Network Functions Virtualization (NFV), the network revolution has got underway. The shift from monolithic application development and deployment to micro-services changed the whole picture. On the other hand, the data centers evolved in several generations where the control plane cannot cope with all the networks without an intelligent decision-making process, benefiting from the AI/ML techniques. AI also enables operators to forecast demand more accurately, anticipate network load, and adjust capacity and throughput automatically. Going one step further, zero-touch networking and service management (ZSM) is proposed to get high-level human intents to generate a low-level configuration for network elements with validated results, minimizing the ratio of faults caused by human intervention. Harmonizing all signs of progress in different communication technologies enabled the use of edge computing successfully. Low-powered (from both energy and processing perspectives) IoT networks have disrupted the customer and end-point demands within the sector, as such paved the path towards devising the edge computing concept, which finalized the whole picture of the edge-cloud continuum.

Capture of real-time data from electronic health records: scenarios and solutions.

The integration of real-time data (RTD) in the electronic health records (EHRs) is transforming the healthcare of tomorrow. In this work, the common scenarios of capturing RTD in the healthcare from EHRs are studied and the approaches and tools to implement real-time solutions are investigated. Delivering RTD by representational state transfer (REST) application programming interfaces (APIs) is usually accomplished through a Publish-Subscribe approach. Common technologies and protocols used for implementing subscriptions are REST hooks and WebSockets. Polling is a straightforward mechanism for obtaining updates; nevertheless, it may not be the most efficient or scalable solution. In such cases, other approaches are often preferred. Database triggers and reverse proxies can be useful in RTD scenarios; however, they should be designed carefully to avoid performance bottlenecks and potential issues. The implementation of subscriptions through REST hooks and WebSocket notifications using a Fast Healthcare Interoperability Resources (FHIR) REST API, as well as the design of a reverse proxy and database triggers is described. Reference implementations of the solutions are provided in a GitHub repository. For the reverse proxy implementation, the Go language (Golang) was used, which is specialized for the development of server-side networking applications. For FHIR servers a python script is provided to create a sample Subscription resource to send RTD when a new Observation resource for specific patient id is created. The sample WebSocket client is written using the "websocket-client" python library. The sample RTD endpoint is created using the "Flask" framework. For database triggers a sample structured query language (SQL) query for Postgres to create a trigger when an INSERT or UPDATE operation is executed on the FHIR resource table is available. Furthermore, a use case clinical example, where the main actors are the healthcare providers (hospitals, physician private practices, general practitioners and medical laboratories), health information networks and the patient are drawn. The RTD flow and exchange is shown in detail and how it could improve healthcare. Capturing RTD is undoubtedly vital for health professionals and successful digital healthcare. The topic remains unexplored especially in the context of EHRs. In our work for the first time the common scenarios and problems are investigated. Furthermore, solutions and reference implementations are provided which could support and contribute to the development of real-time applications.

Research on the Teaching Reform of Network Application Development under the Background of First-class Specialty

With the goal of cultivating application-oriented and innovative talents, based on the teaching philosophy of "student-centered, output-oriented, continuous improvement", combined with modern information technology, and based on online and offline mixed teaching has become an inevitable trend of curriculum construction universities. This paper discusses the teaching reform research situation of “Network Application Development” course under the background of first-class Specialty, mixed teaching design, course teaching content and organization, course assessment and course evaluation, comprehensively exploring the new ideas of curriculum reform, and cultivating new engineering innovative talents.

Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network.

In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system's overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries-PyTorch and TensorFlow-and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented.

From Network Functions to NetApps: The 5GASP Methodology

As the 5G ecosystem continues its consolidation, the testing and validation of the innovations achieved by integrators and verticals service providers is of preponderant importance. In this line, 5GASP is a European H2020-funded project that aims at easing the idea-to-market process through the creation of an European testbed that is fully automated and self-service, in order to foster rapid development and testing of new and innovative 5G Network Applications (NetApps). The main objective of this paper is to present the 5GASP's unified methodology to design, develop and onboard NetApps within the scope of different vertical services, letting them use specific 5G facilities. Besides, we examine the whole 5GASP process in a tutorial fashion by adopting a specific use case focusing on the integration of a virtual On-Board Unit (vOBU) service that permits offloading processing from the attached vehicle and serving data-access requests. As demonstrated, the presented workflow permits the agile, rigorous, and safe development, testing and certification of NetApps, which will enable valuable in-network services for 5G and beyond infrastructures.

Analysis of the Development of Computer Network Application in Electronic Information Engineering

Analysis of the Development of Computer Network Application in Electronic Information Engineering

Malicious Encryption Traffic Detection Based on NLP

The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encrypted traffic detection mainly focuses on the characteristics’ analysis of encrypted traffic and the selection of machine learning algorithms. In this paper, a method combining natural language processing and machine learning is proposed; that is, a detection method based on TF-IDF is proposed to build a detection model. In the process of data preprocessing, this method introduces the natural language processing method, namely, the TF-IDF model, to extract data information, obtain the importance of keywords, and then reconstruct the characteristics of data. The detection method based on the TF-IDF model does not need to analyze each field of the data set. Compared with the general machine learning data preprocessing method, that is, data encoding processing, the experimental results show that using natural language processing technology to preprocess data can effectively improve the accuracy of detection. Gradient boosting classifier, random forest classifier, AdaBoost classifier, and the ensemble model based on these three classifiers are, respectively, used in the construction of the later models. At the same time, CNN neural network in deep learning is also used for training, and CNN can effectively extract data information. Under the condition that the input data of the classifier and neural network are consistent, through the comparison and analysis of various methods, the accuracy of the one-dimensional convolutional network based on CNN is slightly higher than that of the classifier based on machine learning.

A Study on Intelligent Tourism App Based on Artificial Intelligence

With the rapid development of science and technology and the rapid development of network applications, the Internet era has been promoted, and the popularity of smart phones has made great changes in people’s way of life. More and more rely on mobile phone applications to deal with the scale of life. At the same time, with the improvement of material living standard, people pay more attention to the pursuit of spiritual life, travel will become the perfect choice to relax body and mind, and smart phone tourism application (tourism app) is the basic way to realize intelligent tourism. The National Tourism Administration of the people’s Republic of China issued a holiday tourism information circular saying that during the 2012 Mid-Autumn Festival National Day Golden week, 119 scenic spots in the country received 34.2456 million tourists, an increase of 40.7 percent over the same period last year, and the number of tourism complaints increased significantly. Tourist satisfaction is not optimistic. Intelligent tourism is the inevitable trend of the development of tourism in the world, and strategic demand for the transformation and upgrading of tourism in China.

Intrusion Detection Algorithm and Simulation of Wireless Sensor Network under Internet Environment

As an effective security protection technology, intrusion detection technology has been widely used in traditional wireless sensor network environments. With the rapid development of wireless sensor network technology and wireless sensor network applications, the wireless sensor network data traffic also grows rapidly, and various kinds of viruses and attacks appear. Based on the temporal correlation characteristics of the intrusion detection dataset, we propose a multicorrelation‐based intrusion detection model for long‐ and short‐term memory wireless sensor networks. The model selects the optimal feature subset through the information gain feature selection module, converts the feature subset into a TAM matrix using the multicorrelation analysis algorithm, and inputs the TAM matrix into the long‐ and short‐term memory wireless sensor network module for training and testing. Aiming at the problems of low detection accuracy and high false alarm rate of traditional machine learning‐based wireless sensor network intrusion detection models in the intrusion detection process, a wireless sensor network intrusion detection model combining two‐way long‐ and short‐term memory wireless sensor network and C5.0 classifier is proposed. The model first uses the hidden layer of the bidirectional long‐ and short‐term memory wireless sensor network to extract the features of the intrusion detection data set and finally inputs extracted features into the C5.0 classifier for training and classification. In order to illustrate the applicability of the model, the experiment selects three different data sets as the experimental data sets and conducts simulation performance analysis through simulation experiments. Experimental results show that the model had better classification performance.

Feature Contour Construction Methods Based on Neural Network for Network Abnormal Behavior Detection

With the development of network application, how to detect the abnormal behaviors from network data messages gets to be a hotspot of network security management. This paper puts forward network abnormal behavior detection methods based on neural network. As a typical network abnormal behaviors, network intrusion has attacted much more attention in the past years. In order to detect network intrusion, the feasibility and effectiveness of neural network are investigated in detail. On the basis of neural network, two feature contour construction methods are presented to conduct the network intrusion detection. Experiment results indicate that the proposed methods can be used to detect the network abnormal behavior effectively. In the future, comparisons with other abnormal behavior detection methods will be scheduled.

“Heterogeneous couplings”: Operationalizing network perspectives to study science‐society interactions through social media metrics

AbstractSocial media metrics have a genuine networked nature, reflecting the networking characteristics of the social media platform from where they are derived. This networked nature has been relatively less explored in the literature on altmetrics, although new network‐level approaches are starting to appear. A general conceptualization of the role of social media networks in science communication, and particularly of social media as a specific type of interface between science and society, is still missing. The aim of this paper is to provide a conceptual framework for appraising interactions between science and society in multiple directions, in what we call heterogeneous couplings. Heterogeneous couplings are conceptualized as the co‐occurrence of science and non‐science objects, actors, and interactions in online media environments. This conceptualization provides a common framework to study the interactions between science and non‐science actors as captured via online and social media platforms. The conceptualization of heterogeneous couplings opens wider opportunities for the development of network applications and analyses of the interactions between societal and scholarly entities in social media environments, paving the way toward more advanced forms of altmetrics, social (media) studies of science, and the conceptualization and operationalization of more advanced science‐society studies.

POX Controller and Open Flow Performance Evaluation in Software Defined Networks (SDN) Using Mininet Emulator

Growing popularity of Internet demands for the agility as well as the flexibility of computer networks. Traditional networking system is unable to satisfy recent computing needs. Proprietary devices configured manually create an error-prone situation in addition they are incapable to fully utilize the capability of physical network infrastructure. This has resulted in the paradigm shift in the networking industry and it is known as Software defined networking. Advantages such as programmability, task virtualization and easy management of the network can be provided by employing SDN platform, on the other hand POX is defined as a Python based open source OpenFlow SDN Controller mainly used for faster development and prototyping of new network applications basically comes pre-installed with the mininet virtual machine. POX controller can turn dumb OpenFlow devices into hub, switch, load balancer and firewall devices. In this paper Performance metrics such as service Delay, utilized bandwidth, received packets and bytes were measured and recorded using network monitoring tools like iperf and D-ITG in order to analyze the functionality of the POX controller as well as to evaluate the operation performance of POX controller for SDN environment. The results of this research were the recommendation of using POX controller in for rapid development and prototyping of network control systems as well as being the framework for the interaction with Open Flow switches.

Green lighting intelligent control system with web services based on back propagation algorithm

In order to solve the problems of large energy consumption in many stations, confusion lines and lighting source not accord with environmental protection requirement in the present, we put forward the scheme of automatic control system of green lighting based on PROFIBUS and PLC. In order to realise the inherent limitation of lighting control system in information sharing, remote control and other aspects, web services technology is combined with the original system to make full use of its technical advantages in distribution, openness and flexibility, so as to explore a better way for the development of industrial lighting network application. By constructing the communication network of the station lighting system with the PROFIBUS communication protocol and designing the hardware circuit and software program of the system bottom with programmable controller technology; simultaneously, the proposed BP neural network algorithm in the control system can realise intelligent control. Simulating with MATLAB of green computing for multimedia big data, the result shows that the station green lighting control system has very important significance of energy saving and provide the passenger for healthy, comfortable environment.