Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game.

To study the underlying organizing principles of various complex systems, designing an efficient graph-based model for data representation, is a fundamental aspect. As the topological structure of the network changes over time, it is a challenging task to design a communication system having ability to respond to randomly changing traffic. We are interested to find out the suitable and fair traffic flow rates to each system for getting optimal system utility using dynamic complex network model. In this context, we design and simulate a growth model of the data communication network based on the dynamics of in-flowing links which is motivated by the concept that newly added node will connect to the most influential nodes already present in the system. The connectivity distribution of the evolved communication networks follows power law form, free from network scale. We analyze Kelly’s optimization framework for a rate allocation problem in communication networks at different time instants, and optimal rates are obtained with the consideration of arbitrary communication delays.

In order to improve the security of low-latency cluster dispatching communication network nodes, this paper proposes a security situation assessment method of low-latency cluster dispatching communication network nodes based on grey relational model. The security situation assessment model of low-delay cluster scheduling communication network nodes is constructed, and the comprehensive risk index is introduced as the three-layer assessment index of low-delay cluster scheduling communication network nodes. Based on the analysis of load loss probability and expected response characteristics of insufficient packet loss load in communication transmission, the reliability index calculation system of low-delay cluster scheduling communication network node scheduling is established. By introducing expected load reduction parameters, the frequency characteristics of node scheduling security in low-delay cluster scheduling communication network are monitored. Based on the average sustained risk index evaluation of load reduction, the security permission conditions of node scheduling in low-delay cluster scheduling communication network are established. The network transmission load control is carried out by using grey correlation scheduling model. The node control model of low-delay cluster scheduling communication network is constructed by combining risk assessment and load parameter detection. The learning control of node security situation assessment is realized by using Cyber-net and unsupervised learning methods. The node security situation assessment of low-delay cluster scheduling communication network nodes is realized through access and decentralized scheduling. Tests show that this method has high accuracy in evaluating the security situation of nodes in low-delay cluster dispatching communication networks, and has strong completeness and reliability in grid-connected control of communication networks.

Deep learning is one machine learning method based on the layers used in artificial neural networks. The breakthrough of deep learning in classification led to its rapid application in speech recognition, natural language understanding, and image processing and recognition. In the field of biodiversity informatics, deep learning efforts are being applied in rapid species identification and counts of individuals identified based on image, audio, video, and other data types. However, deep learning methods hold great potential for application in all aspects of biodiversity informatics. We present a case study illustrating how to increase data collection quality and efficiency using well-established technology such as optical character recognition (OCR) and some image classification. Our goal is to image data from the scanned documents of various butterfly atlases, add species, specimens, collections, photographs and other relevant information, and build a database of butterfly images. Information collection involves image annotation and text-based descritpion input. Although the work of image annotation is simple, this process can be accelerated by deep learning-based target segmentation to make the selection process easier, such as changing box select to a double click. The process of information collection is complicated, involving input of species names, specimen collection, specimen description, and other information. Generally, there are many images in atlases, the text layout is rather messy, and overall OCR success is poor. Therefore, the measures we take are as follows: Step A: select the screenshot of the text and then call the OCR interface to generate the text material; Step B: proceed with NLP- (natural language processing) related processing; Step C: perform manual operations on the results, and introduce the NLP function again to this process; Step D: submit the result. The deep learning applications we integrated in our client tool include: target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. target segmentation of the annotated image for automatic positioning and background removal, etc. to improve the quality of the image used for identification; making a preliminary judgment on various attributes of the labeled image and using the results to assist the automatic filling of relevant information in step B, including species information, specimen attributes (specimen image, nature photo, hand drawing pictures, etc.), insect stage (egg, adult, etc.); OCR in step A. Some simple machine learning methods such as k-nearest neighbor can be used to automatically determine gender, pose, and so on. While complex information such as collection place, time, and collector can be analyzed by deep learning-based NLP methods in the future. In our infomation collection process, ten fields are required to submit one single record. Of those, about 4-5 input fields can be dealt with the AI-assistant. It can thus be seen from the above process that deep learning has reduced the workload of manual information annotation by at least 30%. With improvements in accuracy, the era of using automatic information extraction robots to replace manual information annotation and collection is just around the corner.

The paper is proposing a framework of a dynamic social evolution model called SynopCity - Virtual City serving as virtual living laboratory for a research of topics related to smart cities. Concept of SynopCity - Virtual City is an application representing a model of virtual city, connected to a social network (www.synopcity.com) where network's members are acting as a citizens developing new urban developments in a virtual world under specific rules and regulations. It will allow authors of paper to study complex social behavior of all parties responsible for city development and decision making processes based on available historical data collected during this experiment of dynamic social evolution model development. Citizens are actively participating on this process along with model authorities and community representatives following statutes, rules and law. Authors of paper are also proposing an implementation of Industry 4.0 principles. The city is acting as an Industry 4.0 factory where all processes and relation between product and production are mirrored between physical factory networked systems and factory's virtual model.

Epigenetic communication through histone and cytosine modifications is essential for gene regulation and cell identity. Here, we propose a framework that is based on a chromatin communication model to get insight on the function of epigenetic modifications in ESCs. The epigenetic communication network was inferred from genome-wide location data plus extensive manual annotation. Notably, we found that 5-hydroxymethylcytosine (5hmC) is the most-influential hub of this network, connecting DNA demethylation to nucleosome remodeling complexes and to key transcription factors of pluripotency. Moreover, an evolutionary analysis revealed a central role of 5hmC in the co-evolution of chromatin-related proteins. Further analysis of regions where 5hmC co-localizes with specific interactors shows that each interaction points to chromatin remodeling, stemness, differentiation, or metabolism. Our results highlight the importance of cytosine modifications in the epigenetic communication of ESCs.

In the future, we are likely to see a tremendous rise in mobile computing and communications as ubiquitous applications incorporate multimedia information. These mobile multimedia applications will have QoS requirements; resource provisioning algorithms utilize current system resource availability information to ensure that these applications meet their QoS requirements. Information collection algorithms collect and maintain current system resource information and are vital in performing efficient resource provisioning. In this paper, we present a novel information collection technique for mobile environments – the ABIC (aggregation‐based information collection) algorithm. This algorithm derives aggregate mobility from individual user mobility, then utilizes the aggregation to drive the information collection process and adjust the related parameters in the process. A feedback loop allows the information collection process to utilize feedback from the resource provisioning process in further decision making. We compare the ABIC approach to other proposed information collection algorithms under different workloads and mobility patterns. Our experimental results show that the aggregation based information collection algorithm exhibits superior performance under most mobility criteria and request patterns.

Network traffic flow is an aggregated result of a huge number of travelers' route choices, which is influenced by the travelers' choice behaviors. So day-to-day traffic flow is not static, but presents a complex and tortuous day-to-day dynamic evolution process. Studying day-to-day dynamic evolution of network traffic flow, we can not only know whether the traffic network equilibrium can be reached and how the process is achieved, but also can know what phenomenon will occur in the evolution of network traffic flow if the equilibrium is not reached. In a real traffic system, taking day as scale unit, the day-to-day network traffic demand is variable and changes with everyday's traffic network state. The travelers' route choices are also influenced by the previous day's behaviors and network state. Then, will the day-to-day network traffic flow evolution be stable? If it is unstable, when will bifurcation and chaos occur? In this paper we discuss the day-to-day dynamic evolution of network traffic flow with elastic demand in a simple two-route network. The dynamic evolution model of network traffic flow with elastic demand is formulated. Based on a nonlinear dynamic theory, the existence and uniqueness of the fixed point of dynamic evolution model are proved, and an equilibrium stability condition for the dynamic evolution of network traffic flow with elastic demand is derived. Then, the evolution of network traffic flow is investigated through numerical experiments by changing the three parameters associated with travelers, which are the sensitivity of travelers' travel demand to travel cost, the randomness of travelers' route choices, and travelers' reliance on the previous day's actual cost. Our findings are as follows. Firstly, there are three kinds of final states in the evolution of network traffic flow: stability and convergence to equilibrium, periodic motion and chaos. The final state of the network traffic flow evolution is related to the above three parameters. It is found that under certain conditions the bifurcation diagram of the network traffic flow evolution reveals a complicated phenomenon of period doubling bifurcation to chaos, and then period-halving bifurcation. Meanwhile, the chaotic region is interspersed with odd periodic windows. Moreover, the more sensitive to cost the travelers' travel demand the more likely the system evolution is to be stable. The smaller the randomness of travelers' route choices, the less likely the system evolution is to be stable. The lower the degree of travelers' reliance on the previous day's actual cost, the more likely the system evolution is to be stable.

Electrical power communication networks undertake complicated power services. They interact with physical power grids all the time, which will result in a high risk to power grids when their electric power communication network is attacked. Therefore, to improve the reliability of power systems, it is of great significance to identify the key links of communication networks. Existing research studies focus more on the risk of power communication services undertaken by communication links, which ignores the frequent interaction between power grids and communications networks. In this paper, from a multi-layer network perspective, an assessment method of communication equipment is proposed in view of the operation characteristics of nodes in different systems as well as the dynamic fault propagation between homogeneous communication nodes. Firstly, we build a model of cyber-physical power interdependent network. Then, for communication nodes, a dynamic model is adopted for cascading failure among the nodes based on the load-capacity model. Afterward, we define an index to evaluate the node importance considering characteristics of the operation of both communication networks and power grids. Finally, a local power grid is taken as an example for verification by comparing the effects of different indexes. The simulation results demonstrate the validity of the evaluation method proposed.

Smart grid is a cyber-physical system with interdependent power grid and communication network. Such interdependence make the smart grid fragile against cascading failures, where an initial failure in communication network may lead to further failures in power grid, and vice versa. We prevent such inter-network cascading failure by stopping an initial failure from propagating. This is achieved by providing a sufficient number of power-disjoint communication routes between power nodes and control center. The number of power-disjoint routes is a robustness metric. We aim to maximize robustness by configuring the interdependence relationship between power grid and communication networks. Such relationship indicates which power node supplies energy to which communication node, and which communication route is used to connect which power node to the control center. Following Menger's Theorem, we transform such configuration problem into a Maxflow problem and solve it using the simplex algorithm. We have evaluated the proposed method through extensive simulations. The results confirm the optimality and computational efficiency of the proposed method. The number of power-disjoint routes increases with an increase in the network size, but can never exceed the number of node-disjoint routes for a same network.

This paper extends previous research efforts related to the simulation performance modelling and analysis of satellite communication networks. Specifically, the use of low earth orbit (LEO) satellite networks for personal communications is examined. Six different satellite constellation configurations are investigated in a packet-switched operating environment. Performance metrics examined are the end-to-end packet delay and the utilization of satellite channels in the dynamic environment. Realistic and accurate models of the physical satellite network and its terrestrial transmitters require that numerous operating characteristics and assumptions be specified. These are based on proposed design requirements of commercial systems, such as Iridium. Via the use of simulation, we show the relative delay and utilization performance of differing satellite network architectures. From these simulation models, mathematical metamodels are derived for the system delays. These innovative models are used to predict the delay performance of different network architectures not previously simulated. Comparison of these metamodels with simulation results show that metamodels provide an accurate means for performance prediction. Copyright © 1999 John Wiley & Sons, Ltd.

Localization of information on communication networks of an open-source online community

Collaborative optimization of distribution network and 5G base stations considering its communication load migration and energy storage dynamic backup flexibility

Aiming at the interaction between the distribution network and electric vehicles(EVs) during the charge-discharge process, based on the complex network theory, the dynamic evolution model of distribution network under Vehicle-to-Grid(V2G) mode is proposed, which can analyze the relationship between the distribution network topological statistical and electrical characteristics. Considering both the distance and access number of charge stations, EV chooses the optimum charge station, then the access mode of EV in the distribution network is obtained. The model is used to study the impact of the spatial-temporal distribution of EV load on the two distribution network characteristics under three charge-discharge modes. Simulation results show that this method can reflect the interactive evolution process between the distribution network and EVs. The consistency between the distribution network topological statistical and electrical characteristics during peak-valley period is verified by comparative analysis. The method provides a theoretical basis for studying the charge-discharge strategy of EVs based on network topological statistical characteristics.

Communication networks are complex information systems influenced by a vast amount of factors. It is critically important to forecast the paths that data take to verify the network, check its security and plan its updates. Model allows exploring processes that take place in the network without affecting performance and availability of a real network itself. With modelling it becomes possible to investigate the results of infrastructural changes introduced to the network before actually implementing them. It is important to be able to formally convert real network description into the model definition which preserves all data that is significant for network operation and skip data which is not. Outlining the rules for such conversion and using a limited set of basic functional components provide the ground for automatic model creation for the network of different levels of complexity.Proposed approach to modelling of communication networks is based on decomposition of the overall function of every particular real network component into a set of functions that belong to some predefined basic set. Functions of the basic set include L3 routing, L2 switching, packet filtering, NAT, etc. Model of a real network component is defined as a group of functional nodes each of which implements some function from the basic set.Configuration and current state of network components that influence its operation are also decomposed into elements each of which relates to some particular functional node. Configuration of network components is modelled as a set of configuration storage elements and current state is modelled as a set of current state storage elements.Links that connect real network components and links that connect functional nodes in the model are presented as singledirection channels that implement propagation of L2 frames thus simplifying the model due to excluding physical layer (L1) from the scope.Using the proposed approach to modelling may allow to formalize conversion of a real network descrip- tion to a model thus making automated modelling possible. By using a sufficient basic set of functional nodes it is possible to model the network containing components of any complexity level.

Magentix2: A privacy-enhancing Agent Platform