Large Number Of Network Nodes Research Articles

RETRACTED ARTICLE: Network security threat detection technology based on EPSO-BP algorithm

With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e − 02, 4.805e − 02, 4.914e − 01, 1.066e − 01, 1.577e − 01, 1.343e − 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.

Edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction in social media data streams

AbstractEdge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for future cyber‐physical‐social systems (CPSS), which provides the ability to analyze data at edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyper‐connected smart sensing devices. As a part of future CPSS, online social networks are large‐scale complex networks that consist of a large number of network nodes and links. The dynamic discovery of communities, especially overlapping communities, is important to understand the evolution of online social networks. However, traditional community discovery algorithms cannot effectively discover overlapping communities in social networks. In order to address this challenge, an edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction model (EIDEP) is proposed in this article. This model encompasses a label propagation algorithm based extension (LPAE) algorithm, which is able to efficiently discover the user community structures in online social networks. Based on the LPAE community discovery algorithm, a user interest behavior based evolution prediction (UIBEP) algorithm is incorporated in our EIDEP model in order to realize a fast yet accurate community evolution for online social networks, by considering the interest similarity of unlinked nodes in a given community. The performance of our proposed LPAE and UIBEP models is validated and evaluated against notable state‐of‐the‐art community discovery algorithms, through extensive experiments conducted based on a Twitter dataset.

How Can Platform Leader Achieve Sustainable Development in Platform-Based Ecosystem?

Commercial competition paradigm is changing from competitive advantage which based internal resources to the eco-advantage which from ecosystem consisted of internal and external value network under the background of the Internet economy. Enterprises can’t effectively face the complex and fierce market competition environment which relied solely on internal heterogeneous resources or core competencies, so many firms adopt platform strategy and build a business ecosystem to be out of this dilemma. It could get eco-advantage for platform leaders to leverage the external resources and ability from the business ecosystem. How can platform leaders gain sustainable development depending on the superiority niche in the platform ecosystem? This paper will answer this question by taking JD as an example which is a typical B2C Internet platform. The results of the study are as follows: First, the platform firm is the leader and creator in the early establishment of the platform-based ecosystem, it’s the formational basis of eco-advantages comes from enough quantity and high quality of platform user. User install and demand are not only the important orientations for platform enterprises producing and managing but also the power source for forming eco-advantages and sustainable development. Second, the sustainable development of the platform leader is related to the size and strength of the network effect. The more network effects, the more conducive to get eco-advantages and sustainable development for platform leaders. Third, the platform-based ecosystem is a value network consisted of a large number of network nodes which is a symbiosis relationship by competing and cooperating. The cross-border integration and collaborative development of multi-agent in the ecosystem are beneficial to promoting platform leaders to form ecological advantages and achieve sustainable development. Forth, platform leader, which is the leading species and key species determined the fate of species in the ecosystem, provides a place for value co-creation of multi-species. It is the process of multi-agent value co-creation based on the platform for platform leaders to obtain sustainable development, which is also the result of internal innovation and external open innovation. This study is of great significance to enrich the research of platform enterprise theory and guide the sustainable development of platform enterprise.

Equivalence between dropout and data augmentation: A mathematical check

The great achievements of deep learning can be attributed to its tremendous power of feature representation, where the representation ability comes from the nonlinear activation function and the large number of network nodes. However, deep neural networks suffer from serious issues such as slow convergence, and dropout is an outstanding method to improve the network’s generalization ability and test performance. Many explanations have been given for why dropout works so well, among which the equivalence between dropout and data augmentation is a newly proposed and stimulating explanation. In this article, we discuss the exact conditions for this equivalence to hold. Our main result guarantees that the equivalence relation almost surely holds if the dimension of the input space is equal to or higher than that of the output space. Furthermore, if the commonly used rectified linear unit activation function is replaced by some newly proposed activation function whose value lies in R, then our results can be extended to multilayer neural networks. For comparison, some counterexamples are given for the inequivalent case. Finally, a series of experiments on the MNIST dataset are conducted to illustrate and help understand the theoretical results.

Structural optimisation algorithm of weight correlation analysis for DBN

The deep belief network (DBN) algorithm has slow convergence rate and low learning efficiency, and it is sensitive to model hyper-parameters. In view of the fact that DBN is a learning network model with a large number of network nodes and multiple hidden layers, this paper proposes a weight correlation analysis algorithm for DBN (WCA-DBN) to determine the approximately optimal network structure of DBN. This algorithm establishes a PS orthogonal projection space based on the 'arbitrary node set' using the linear discriminant analysis (LDA) method, projects the 'balanced node set' onto the PS plane, and solves the weights of the network nodes between sets through the correlation principle, to adjust the network structure of the hidden layer adaptively. The real-time traffic flow of six different types of roads in Jinan City, Shandong Province was selected as experimental materials to verify that the WCA-DBN algorithm has more obvious advantages over traditional algorithms in terms of accuracy and time complexity.

Layout optimization of large-scale oil–gas gathering system based on combined optimization strategy

Layout optimization of large-scale oil–gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells.

BASCO: a toolbox for task-related functional connectivity.

BASCO (BetA Series COrrelation) is a user-friendly MATLAB toolbox with a graphical user interface (GUI) which allows investigating functional connectivity in event-related functional magnetic resonance imaging (fMRI) data. Connectivity analyses extend and compliment univariate activation analyses since the actual interaction between brain regions involved in a task can be explored. BASCO supports seed-based functional connectivity as well as brain network analyses. Although there are a multitude of advanced toolboxes for investigating resting-state functional connectivity, BASCO is the first toolbox for evaluating task-related whole-brain functional connectivity employing a large number of network nodes. Thus, BASCO allows investigating task-specific rather than resting-state networks. Here, we summarize the main features of the toolbox and describe the methods and algorithms.

ПІДХІД ДО ВИБОРУ ПРОТОКОЛУ МАРШРУТИЗАЦІЇ ДЛЯ МЕРЕЖ МАЙБУТНЬОГО

The article presents the studies results of some aspects of networks of the future design. In this paper, most attention is paid to routing protocols. This theme was chosen because of its significant relevance and importance of networks of the future design, as one of the priority directions of further development of information and communication technologies. Internet Engineering Task Force (IETF) published a series of recommendations related to routing protocols, in particular the recommendation RFC 2328 describes the protocol OSPF v2, recommendation RFC 1771 contains the basic provisions of the protocol BGP v4. Based on this information, as well as some other data were analyzed in order to select a routing protocol for a specific task. The paper presents the most famous routing protocols. Relevant protocols are OSPF and BGP v4, supporting a large number of network nodes with high reliability and low service traffic. For non-large networks often is used RIP v2 due to the simplicity of its realization. IS-IS protocol is currently not widely used because it does not satisfy a very important requirement for modern networks, namely not support IP-routing. Protocols IGRP and EIGRP are developed by Cisco and implemented, primarily, on their equipment. It should be noted that this study is directed primarily to show that the task of choosing a routing protocol is a controversial issue and has a large dependence on various factors. These factors include: the purpose of creating a network, the number of nodes in the network, the requirements for scalability and reliability, convergence. To solve global issues appropriate to apply protocols OSPF and BGP, while BGP is a major dynamic routing protocol in the Internet. By considering a particular example of a small corporate network, for which is not necessary to use such protocols like: OSPF and BGP v4, which will be characterized by high complexity for this situation, RIP v2 is best suited. Consequently, the value of this lies in correct, competent and flexible approach to the choice of routing protocol for each case, according to the recommendations of various standards organizations.

A Tracked Robot for Complex Environment Detecting

The complex geographical environment put forward grand challenge to most current detecting robot on the problem of communication control and moving obstacle avoidance.This paper proposed a crawler robot for complex environment detecting.Information acquisition was achieved by Labview programming,the collected sensor information was debugged by fusing and displaying in the Labview interface , in order to realize the stability of the system.The positioning part adopted wireless location technology of ZigBee networks,which can support a large number of network nodes, fast, and reliable security etc.In addition, the upper part of the robot body was equipped with camera and mechanical arm to achieve target capture, carrying something and other tasks. According to the situation of accident, remote control terminal through wireless control function can control the further action of the robot. Through experimental tests, this paper proposes this kind of detection robot had great advantages in detection, search and rescue, it can complete the search under complex and dangerous environment.

Enhancing Sink-Location Privacy in Wireless Sensor Networks through k-Anonymity

Due to the shared nature of wireless communication media, a powerful adversary can eavesdrop on the entire radio communication in the network and obtain the contextual communication statistics, for example, traffic volumes, transmitter locations, and so forth. Such information can reveal the location of the sink around which the data traffic exhibits distinctive patterns. To protect the sink-location privacy from a powerful adversary with a global view, we propose to achieve k-anonymity in the network so that at least k entities in the network are indistinguishable to the nodes around the sink with regard to communication statistics. Arranging the location of k entities is complex as it affects two conflicting goals: the routing energy cost and the achievable privacy level, and both goals are determined by a nonanalytic function. We model such a positioning problem as a nonlinearly constrained nonlinear optimization problem. To tackle it, we design a generic-algorithm-based quasi-optimal (GAQO) method that obtains quasi-optimal solutions at quadratic time. The obtained solutions closely approximate the optima with increasing privacy requirements. Furthermore, to solve k-anonymity sink-location problems more efficiently, we develop an artificial potential-based quasi-optimal (APQO) method that is of linear time complexity. Our extensive simulation results show that both algorithms can effectively find solutions hiding the sink among a large number of network nodes.

Probabilistic models for access strategies to dynamic information elements

In various network services remote access to dynamically changing information elements is a required functionality. Three fundamentally different strategies for such access are investigated in this paper: (1) a reactive approach initiated by the requesting entity, and two versions of proactive approaches in which the entity that contains the information element actively propagates its changes to potential requesters, either (2) periodically or (3) triggered by changes of the information element. This paper develops probabilistic models for these scenarios, which allow to compute a number of performance metrics, with a special focus on the mismatch probability. In particular, we use matrix-analytic methods to obtain explicit expressions for the mismatch probability that avoid numerical integration. Furthermore, limit results for information elements spread over a large number of network nodes are provided, which allow one to draw conclusions on scalability properties. The impact on mismatch probability of different distribution types for the network delays as well as for the time between changes of the information element are obtained and discussed through the application of the model in a set of example scenarios. The results of the model application allow for design decisions on which strategy to implement for specific input parameters and specific requirements on the performance metrics.

에이전트 플랫폼에서의 효율적인 결함-허용을 제공하는 이주 기법

인터넷의 급속한 발전과 더불어 예견되는 미래의 응용 서비스들은 수많은 네트워크의 노드들을 연결하는 네트워크 기반 응용 서비스들이 주류를 이를 것으로 기대된다. 하지만 이러한 응용 서비스들이 운영되는 미래의 환경은 네트워크의 크기 및 트래픽의 양면에서 현재의 네트워크와는 비교할 수 없을 정도로 성장할 것이며, 이러한 구조에 맞는 응용 서비스들을 개발하기 위해서는 단순히 네트워크 인프라 처리속도를 향상시키는 것만으로는 효율적인 해결방안이 될 수 없다. 본 논문에서는 네트워크 컴퓨팅 기반 기술향상에 이바지하기 위해, 네트워크와 플랫폼 상에서 독자적이고 비동기적으로 작업을 수행할 수 있는 에이전트 기술을 이용한 에이전트 플랫폼 소프트웨어를 제안한다. 제안하는 에이전트 플랫폼 소프트웨어는 빠르게 증가하는 네트워크 호스트 수를 감당할 수 있는 확장성, 다양한 환경 변화에 대한 적응성 그리고 결함에 대처할 수 있는 가용성을 지원한다. With the development of the internet technology, network application services based on a large number of network nodes have been focused. However, such application services require much larger network size and traffic than current network. In order to develop them, efficient solutions as well as a simple improvement of network infra processing time are required. In this paper, to contribute a improvement of network computing technology, we design and implement the agent platform software based on the agent technology that performs works independently and asynchronously on a network and platform. The proposed agent platform software supports the scalability to accommodate the number of network hosts with rapid growth, the adaptability on a variable environments, and the availability for a fault-tolerance.

Cooperative distributed MIMO channels in wireless sensor networks

The large number of network nodes and the energy constraints make Wireless Sensor Networks (WSN) one of the most important application fields for Cooperative Diversity. Node cooperation increases the spatial diversity of wireless channels and, thus, reduces the transmitted power. In this paper, we propose a multi-hop WSN with nodes grouped in cooperative clusters that exploits transmit and receive cooperation among cluster nodes. Multi-hop transmission is carried out by concatenating single cluster-to-cluster hops, where every cluster-to-cluster link is defined as a cooperative distributed multiple-input-multiple-output (MIMO) channel. Transmit diversity is exploited through a time-division, decoder-and-forward, relaying scheme based upon two time slots: the Intracluster Slot, used for data sharing within the cluster, and the Intercluster Slot, used for transmission between clusters. At the receiver side, a distributed reception protocol is devised based upon a Selection Diversity algorithm. The proposed multi-hop cooperative WSN is optimally designed for minimum end-to-end outage probability by deriving the optimum time and power allocated on the intracluster and intercluster slots of every single hop, given a per-link energy constraint. A simplified suboptimum resource allocation is also proposed, which performs close to the optimal policy. Results show that the proposed scheme achieves diversity equal to the equivalent MIMO system and significantly reduces energy consumption with respect to. the non-cooperative channel

Development, Analysis and Evaluation of Performance Models for Mobile Multi-Agent Networks

Mobile agents have a number of interesting features such as creation, disposal of agents, execution of services at various network nodes, migration to other network nodes and communication with other agents. Such features are related to the performance area. Based on some examples, scenarios, or case studies, Petri net models (ordinary Petri net, colored Petri net or predicate/transition net), mathematical models, simulation models and queuing models have been developed in the literature. Such research works concentrate on studying the performance of mobile agent networks with much attention on deadlock problems, dynamic behavior problems and the calculation of response time problems. In order to better understand how to design distributed systems using the mobile agent paradigm and how to build a model with the capability of studying all such performance problems in an easy and a realistic way, we propose a new mobile agent performance model using the capability of the generalized stochastic Petri net (GSPN) modeling technique. We amended this model with new mobile agent behaviors to fully describe the dynamic behavior of the mobile agent network when it manipulates parallel/multiple agents, and uses some of the most important agent communications (e.g. remote, local, direct, indirect and parallel). Furthermore, the proposed model describes the creation process of new agents during the migration process, the execution of tasks among the network nodes, the way of handling the agent and its service task at each node and the interaction of a group of agents (as a collective result of the behaviors of each individual agent). To the best of our knowledge, such aspects are not included into one model because it is difficult to describe all these aspects into one model. Therefore, the developed GSPN mobile model gives the facility to study the performance of mobile agent networks with more details. In the performance analysis, we develop four studies to investigate the effect of different parameters such as agent communication time, size of mobile agent, number of mobile agents and number of hops on the performance of mobile agent networks. By performance of mobile agent networks, we mean the response time of the user's request. To the best of our knowledge, such four performance studies are not investigated previously by the GSPN modeling technique. Finally, we propose a reduction-modeling methodology to facilitate the modeling process of the practical mobile agent network that incorporates a large number of network nodes, a large number of users, and subsequently a large number of parallel/multiple agents. Such methodology reduces the original model to a simpler one while preserving the basic features and properties of the original model.

Architectures and connection probabilities for wireless ad hoc and hybrid communication networks

Ad hoc wireless networks involving large populations of scattered communication nodes will play a key role in the development of low power, high capacity, interactive, multimedia communication networks. Such networks must support arbitrary network connections and provide coverage anywhere and anytime. This paper partitions such arbitrarily connected network architectures into three distinct groups, identifies the associated dual network architectures and counts the number of network architectures assuming there exist N network nodes. Connectivity between network nodes is characterized as a random event. Defining the link availability p as the probability that two arbitrary network nodes in an ad hoc network are directly connected, the network connection probability f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> (p) that any two network nodes will be directly or indirectly connected is derived. The network connection probability f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> (p) is evaluated and graphically demonstrated as a function of p and N. It is shown that ad hoc wireless networks containing a large number of network nodes possesses the same network connectivity performance as does a fixed network, i.e., for p > 0, lim <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> →∞ f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> (p) = 1. Furthermore, by cooperating with fixed networks, the ad hoc network connection probability is used to derive the global network connection probability for hybrid networks. These probabilities serve to characterize network connectivity performance for users of wireless ad hoc and hybrid networks, e.g., IEEE 802.11, IEEE 802.15, IEEE 1394–95, ETSI BRAN HIPERLAN, Bluetooth, wireless ATM and the world wide web (WWW).

A Fault-Tolerant Architecture for Large-Scale Distributed Control Systems

Abstract This article introduces a DCCS architecture and protocol mechanisms designed for application areas for which the major attributes are high reliability and hard real-time operation. The application domains addressed are characterized by a large number of network nodes and a considerable amount of sporadically occurring data. Design rules are derived from the fundamentals of distributed real-time computing and the requirements of the target application domains leading to an advantageous system concept. This concept provides an active error detection and notification mechanism, as opposed to the fail-silent paradigm. The system architecture makes use of existing standards and components where possible.