Network Topology Model Research Articles

A Novel Adaptive Optimization of Intragrated Network Topology and Transmission Path for IoT System

The Internet of Things (IoT) is currently the emerging trend in network applications. The core component is the network layer in the IoT system. There are already some existing improvements, most of them are focused either on the transmission path or on the network topology. We have previously designed the Queen Honey Bee Migration (QHBM) algorithm which is a new method for solving routing and transmission path. In this paper, we further improve both the transmission path and the network topology at the same time. The authors designed two novel adaptive models to solve the optimization of networking. We employed the Adaptive Model of Network Topology (AMNT) based on the QHBM algorithm to optimize the repeater; moreover, we also used the probability model to design Adaptive Model of Transmission Path (AMTP) to optimize the end-device. It allows the access point (AP) in the network to perform transmission path transfer or network topology reorganization according to the specified conditions. We evaluated two network performance parameters: transmission time and packet loss rate. The control mode maintains a conventional mode and does not change the existing network topology or transmission path with any factors. The experimental mode performs according to the novel adaptive integrated network topology and the transmission path for the IoT system, thus this system will operate according to the setting of the environment with the signal interference source generated by a random process method. We found that in laboratory testing, compared to conventional mode, the network performance of the novel integrated network topology and the transmission path for the IoT system has been increased by AMNT and AMTP. The performance index of the transmission time increased by 70.6±11%, and the performance index of the packet loss rate increased by 84 ± 38%. We conclude that our novel adaptive optimization of integrated network topology and the transmission path for the IoT system is practically applicable and has achieved encouraging result in terms of IoT performance. We hope for a wide application of this novel design on the networking of the IoT in the future.

Network context matters: graph convolutional network model over social networks improves the detection of unknown HIV infections among young men who have sex with men.

HIV infection risk can be estimated based on not only individual features but also social network information. However, there have been insufficient studies using n machine learning methods that can maximize the utility of such information. Leveraging a state-of-the-art network topology modeling method, graph convolutional networks (GCN), our main objective was to include network information for the task of detecting previously unknown HIV infections. We used multiple social network data (peer referral, social, sex partners, and affiliation with social and health venues) that include 378 young men who had sex with men in Houston, TX, collected between 2014 and 2016. Due to the limited sample size, an ensemble approach was engaged by integrating GCN for modeling information flow and statistical machine learning methods, including random forest and logistic regression, to efficiently model sparse features in individual nodes. Modeling network information using GCN effectively increased the prediction of HIV status in the social network. The ensemble approach achieved 96.6% on accuracy and 94.6% on F1 measure, which outperformed the baseline methods (GCN, logistic regression, and random forest: 79.0%, 90.5%, 94.4% on accuracy, respectively; and 57.7%, 80.2%, 90.4% on F1). In the networks with missing HIV status, the ensemble also produced promising results. Network context is a necessary component in modeling infectious disease transmissions such as HIV. GCN, when combined with traditional machine learning approaches, achieved promising performance in detecting previously unknown HIV infections, which may provide a useful tool for combatting the HIV epidemic.

Network topological model of reconstructive solid-state transformations

Reconstructive solid-state transformations are followed by significant changes in the system of chemical bonds, i.e. in the topology of the substance. Understanding these mechanisms at the atomic level is crucial for proper explanation and prediction of chemical reactions and phase transitions in solids and, ultimately, for the design of new materials. Modeling of solid-state transitions by geometrical, molecular dynamics or quantum-mechanical methods does not account for topological transformations. As a result, the chemical nature of the transformation processes are overlooked, which limits the predictive power of the models. We propose a universal model based on network representation of extended structures, which treats any reorganization in the solid state as a network transformation. We demonstrate this approach rationalizes the configuration space of the solid system and enables prediction of new phases that are closely related to already known phases. Some new phases and unclear transition pathways are discovered in example systems including elementary substances, ionic compounds and molecular crystals.

A Particle Swarm Optimization for Terminal Resistor Matching

The CAN bus is widely used in the industrial field and its terminal resistance has an important influence on the bus signal. In this paper, a particle swarm optimization based on the joint simulation of Saber and MATLAB is proposed to address this problem. First, we transform the terminal resistor matching problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives: bus voltage, rise time and overshoot. Then, we establish a network topology model of CAN bus in Saber, and combine saber and MATLAB. Finally, virtual simulation and physical verification are conducted and the results demonstrate that the optimized terminating resistor has a significant improvement on the signal quality of the bus, and algorithm has engineering applicability.

Invulnerability Analysis of Traffic Network in Tourist Attraction Under Unexpected Emergency Events Based on Cascading Failure

This study aims to analyze the cascading failure process of traffic network in tourist attraction under unexpected emergency events, and discuss the measures to improve traffic network invulnerability. For that purpose, taking tourist attraction as the research object, the topology model of traffic network in tourist attraction is established based on Space L method. After different types of emergency events are simulated as different attack strategies, we discuss the spatial and temporal distribution characteristics and evolution process of tourism emergency events. The cascading failure model of traffic network based on load-capacity is constructed, then the main factors affecting the scale of dynamic cascading failures are given and their sensitivity are analyzed. Taking the Summer Palace in Beijing as an example, the invulnerability analysis of the traffic network is carried out and the node protection strategies with different network load are proposed. The study reveals that (a) the traffic network of the Summer Palace has a typical network characteristics of small world; (b) network load, node capacity, node attack strategy, adjacent nodes relationship, load distribution rule significantly affect the scale of cascading failure of the traffic network in tourist attraction, (c) when the network load coefficient $\delta , the cascading failure rate (CFR) can be effectively controlled within 0.2 to avoid large-scale cascading failure; (d) the scale of cascading failure can be effectively reduced by 22.14%, 40.91%, and 63.66%, after increasing the capacity of the nodes which are the top 5%, 10%, and 20% in the ranking of CFR, respectively.

MCADNNet: Recognizing Stages of Cognitive Impairment through Efficient Convolutional fMRI and MRI Neural Network Topology Models.

Mild cognitive impairment (MCI) represents the intermediate stage between normal cerebral aging and dementia associated with Alzheimer's disease (AD). Early diagnosis of MCI and AD through artificial intelligence has captured considerable scholarly interest; researchers hope to develop therapies capable of slowing or halting these processes. We developed a state-of-the-art deep learning algorithm based on an optimized convolutional neural network (CNN) topology called MCADNNet that simultaneously recognizes MCI, AD, and normally aging brains in adults over the age of 75 years, using structural and functional magnetic resonance imaging (fMRI) data. Following highly detailed preprocessing, four-dimensional (4D) fMRI and 3D MRI were decomposed to create 2D images using a lossless transformation, which enables maximum preservation of data details. The samples were shuffled and subject-level training and testing datasets were completely independent. The optimized MCADNNet was trained and extracted invariant and hierarchical features through convolutional layers followed by multi-classification in the last layer using a softmax layer. A decision-making algorithm was also designed to stabilize the outcome of the trained models. To measure the performance of classification, the accuracy rates for various pipelines were calculated before and after applying the decision-making algorithm. Accuracy rates of 99.77% 0.36% and 97.5% 1.16% were achieved for MRI and fMRI pipelines, respectively, after applying the decision-making algorithm. In conclusion, a cutting-edge and optimized topology called MCADNNet was designed and preceded a preprocessing pipeline; this was followed by a decision-making step that yielded the highest performance achieved for simultaneous classification of the three cohorts examined.

An optimization model of signal timing plan and traffic emission at intersection based on Synchro

With the rapid development of urbanization in China, the pressure of urban traffic keeps increasing. The problem of traffic pollution is increasingly serious as the automobile exhaust has been one of the main sources of urban air pollution. The major causes of traffic emission are the traffic jam and delay in the urban road intersections. Many advanced traffic signal control methods have been widely applied in the traffic management to reduce traffic jam. This paper proposes an optimization model of signal timing plan that comprehensively considers operating efficiency and traffic emission, which is based on signal timing plan for building a model of network topology and simulation for optimizing the signal timing. Meanwhile, this plan is to improve the efficiency of intersections, reduce queuing time and harmful emission. Taking some intersections in Harbin as an example, the measured pollutant emissions data at the intersections before and after signal optimization were collected and compared, then the levels of vehicle exhaust pollution and emissions of various emissions under different control strategies were analyzed, thus the emissions under different traffic management and control were evaluated. All these data has a great importance both in theoretical and practical value.

Uses of social network topology and network-integrated multimedia for designing a large-scale open learning system: case studies of unsupervised featured learning platform Design in South Korea

This paper discusses the uses of network-integrated multimedia and social network modeling to design a large-scale open learning system (OLS) for unsupervised learning. Multimedia uses for enriching learning experiences have greatly improved within the last decade due to advances in broadband technology and high-speed transmission. The multimedia integrated learning environment is no longer limited to embedding low-quality video and audio streaming but can provide multivariant information delivery to enhance the self-directed learning experience. This paper discusses two case studies of an open learning system developed in South Korea that used social learning network models and multimedia: the NOOC Project for civic education, and the Genesis Project for professional and continuing education. While the focus of study is on uses of social network topology modeling and network-integrated multimedia to design an open learning system, the underlying design has broad implications for modeling any gradient-based, unsupervised open learning system for continuing education and informal learning.

Distributed Linear Convolutional Space-Time Coding for Two-Hop Full-Duplex Relay 2x2x2 Cooperative Communication Networks

In this paper, distributed linear convolutional space-time coding (DLC-STC) for two relay full-duplex (FD) $2\times 2\times 2$ cooperative communication networks is presented. A network topology model which consists of two sources, two destinations and one layer of two relays, is considered. In this communication model, two sources may transmit signals to two different destinations with the help of two relays simultaneously, which will cause inter-user interference. As a result, one has to deal with the inter-user interference in addition to the self-loop interference at the relays. Most part of the self-loop interference can be cancelled largely with the self-loop interference techniques at the relays, but the residual part is still a problem that requires proper processing. We consider that the two relays work in FD mode with an amplify-and-forward (AF) protocol, and the relay self-loop interference may not be cancelled completely from the received signals. Distributed space-time coding for this model is studied, for which the residual part of relay self-loop signal is applied to serve as a self-coding signal. With the self-coding property of the AF FD system, a distributed linear convolutional space time code is formed. Simulation experiments are conducted to evaluate the proposed DLC-STC for the considered FD relay communication network model, and the results illustrate the effectiveness of the presented approach.

Research of Distributed Control System for Oilfield Oil Pump Based on PLC and LAN

Abstract In order to change the current bad situation such as the high cost of manpower, the failure to deal with the fault in time and the low management efficiency, etc. in oilfield oil pump measure and control, this paper puts forward the research and development of oilfield oil pump distributed control system based on PLC and LAN. Firstly this paper describes the operation principle of oil pump equipment and gives its network topology model. Secondly it designs the system macro distributed structure framework with 4 layers: Pump layer, PLC layer, IPC layer and RCC layer in detail. Finally the paper discusses the design of the software system including the PLC on-site field measure and control software subsystem, the IPC local session software subsystem and the RCC remote management software subsystem.

Sensing Nodes Selective Fusion Scheme of Spectrum Sensing in Spectrum-Heterogeneous Cognitive Wireless Sensor Networks

A new spectrum sensing scheme based on spatial-temporal opportunity detection is proposed in this paper. To verify the practicability and validity of the method, we proposed a more real 2-D cognitive wireless sensor network (CWSN) topology model, where the primary signal transmitter is distributed in a given sensing area randomly at each iteration. The proposed 2-D sensing nodes selective fusion scheme consists of three main sensing steps: non-cooperative spectrum sensing for all wireless sensor nodes, location estimation, and the decision fusion of selected sensing nodes. One other thing to note is the limitation of energy in CWSN. To save more energy, a sensing nodes selection procedure for energy sufficiency is used to address the problem of too much energy consumption of direct transmissions to the fusion center. Simulation results show that the proposed scheme not only has a desirable property to process the spectrum-heterogeneous problem in spatial-temporal 2-D sensing environment but shows satisfactory detection performance with less energy consumption as well.

Enhancing Protection Performance by Effectively Using Spectral Slots of Impairment-Aware Elastic Optical Networks [Invited

The challenge of using spectral bandwidth effectively considering optical impairments in elastic optical networks is studied. The protection performance is enhanced by improving the spectral efficiencies of the networks. Novel optical filter configuration, signal equalization, and spectral bandwidth assignment technologies are proposed to minimize the use of the guardband while taking the passband narrowing effect into consideration. It is verified that spectral slots are effectively used with actual network topology models.

Communication dynamics in complex brain networks.

Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.

Model of Computer Network Topology Optimization Based on Pattern Recognition Technology

The topological structure of the computer network has an important influence on the efficiency of the whole network system, the exertion of the technical performance, the reliability and the cost. The concept of computer network topology classification, introduces the characteristics, based on the analysis of the complex network structure, explore the effective construction of computer network topology model, for its practical application in the design of redundancy is studied, to improve the network system reliability and system safety design.

Performance Analysis of Frame Relay Network Using OSPF (Open Shortest Path First) and MPLS (Multi-Protocol Label Switching) based on GNS3

enterprise networks. This research focuses on combining advantages from Frame Relay, MPLS and OSPF into a modeled multi-site network. The combination is compared to th original OSPF network and analyzed for its throughput, packet loss, and delay. To meet the demand for efficient and resilient network, and to emphasize the advantages of MPLS-OSP as well as to avoid traffic shifting, specific network topology models are applied: full mesh with virtual circuit in core network connected to OSPF nodes for the rest. From analysis and comparison of network quality values obtained at, this combination attests to be reliable and robust network architecture without trading off its efficiency. The average throughput value of the combination networks was 18.47 bps, which is better compared to the OSPF. The average delay and packet loss from combination network also show better results, which are 59.90ms and 2.01% respectively. The results shows that the combination of Frame Relay, OSPF andMPLS generates better performance as well as significant improvement in networksecurity.

Web-based Simulations of Multi-agent Systems

This paper describes the architecture of AgentSimJs, a Javascript-based multi-agent simulator intended to execute and visualize simulations through a Web browser. It includes the needed capabilities to render a 3D scene with objects and agents. AgentSimJs has a modular architecture, the several different components provide a set of flexible primitives to program the agent behaviour. With AgentSimJs, a user can perform a fine tuning of all the aspects related to communication, motion, and group formation. Users can also define a custom network topology and communication model.

Simulating MPI Applications: The SMPI Approach

This article summarizes our recent work and developments on SMPI, a flexible simulator of MPI applications. In this tool, we took a particular care to ensure our simulator could be used to produce fast and accurate predictions in a wide variety of situations. Although we did build SMPI on SimGrid whose speed and accuracy had already been assessed in other contexts, moving such techniques to a HPC workload required significant additional effort. Obviously, an accurate modeling of communications and network topology was one of the key to such achievements. Another less obvious key was the choice to combine in a single tool the possibility to do both offline and online simulation.

R&D planning of manufacturing industry: a systematic method based on SITRM

Industrial technology roadmapping (ITRM) offers an important tool for technology R&D planning in manufacturing industry. However, previous studies cannot support the R&D planning and management in practical applications since they remain conceptual and lack of robustness in a volatile environment. In response, this research uses scenario planning and Bayesian network to support R&D planning for developing an ITRM. The first step is to construct the scenarios which are used as inputs for the scenario-based technology roadmapping (SITRM). Second, a Bayesian network topology model of SITRM is developed, in order to display various states of random variables and the uncertainty in relationships between nodes uncertainty of SITRM. Finally, based on probability deduction methods of Bayesian network, a quantitative analysis process and calculation model in supporting technology R&D planning was performed. As an illustrative example, light emitting diode industry in Guangdong is presented to show the feasibility of this method.

R&D planning of manufacturing industry: a systematic method based on SITRM

Industrial technology roadmapping (ITRM) offers an important tool for technology R&D planning in manufacturing industry. However, previous studies cannot support the R&D planning and management in practical applications since they remain conceptual and lack of robustness in a volatile environment. In response, this research uses scenario planning and Bayesian network to support R&D planning for developing an ITRM. The first step is to construct the scenarios which are used as inputs for the scenario-based technology roadmapping (SITRM). Second, a Bayesian network topology model of SITRM is developed, in order to display various states of random variables and the uncertainty in relationships between nodes uncertainty of SITRM. Finally, based on probability deduction methods of Bayesian network, a quantitative analysis process and calculation model in supporting technology R&D planning was performed. As an illustrative example, light emitting diode industry in Guangdong is presented to show the feasibility of this method.

Reconstruction algorithm of chaotic signal based on generalized likelihood ratio threshold-decision

Blind signal reconstruction in sensor arrays is usually a highly nonlinear and non-Gaussian problem, and nonlinear filtering is an effective way to realize state estimation from available observations. Developing the processing problem of blind signal in wireless sensor networks (WSNs) will greatly extend the application scope. Meanwhile, it also meets great challenges such as energy and bandwidth constrained. For solving the constrained problem in WSNs, the observed signals must be quantified before sending to the fusion center, which makes the overall noise unable to be modeled accurately by simple probabilistic model. To study the reconstruction issue of chaotic signal with unknown statistics in WSNs, a reconstructed method of chaotic signal based on a cost reference particle filter (CRPF) is proposed in this paper. The cost recerence cubature particle filter (CRCPF) algorithm adopts cubature-point transformation to enhance the accuracy of prediction particles, and cost-risk functions are defined to complete particle propagation. The effectiveness of proposed CRCPF algorithm is verified in the sensor network with a fusion center. Moreover, a generalized likelihood ratio functionis obtained by the cost increment of local reconstructed signals in the cluster-based sensor network topology model, which is used to reduce the network energy consumption by selecting working nodes. Simulation results show that compared with CPF and CRPF, the proposed algorithm CRCPF attains good performance in a WSN with unknown noise statistics. Meanwhile, the CRCPF algorithm realizes the compromise between energy consumption and reconstruction accuracy simultaneously, which indicates that the proposed CRCPF algorithm has the potential to extend other application scope.