Effect of correlation on the traffic capacity of time-varying communication network

Abstract

Received (Day Month Year) Revised (Day Month Year)The network topology and the routing strategy are major factors to affect the traffic dynamics of the network. In this work, we aim to design an optimal time-varying network structure and an efficient route is allocated to each user in the network. The network topology is designed by considering addition, removal, and rewiring of links. At each time instants, a new node connects with an existing node based on the degree and correlation with its neighbor. Traffic congestion is handled by rewiring of some congested links along with the removal of the anti-preferential and correlated links. Centrality plays an important role to find the most important node in the network. The more a node is central, the more it can be used for the shortest route of the user pairs and it can be congested due to a large number of data coming from its neighborhood. Therefore, routes of the users are selected such that the sum of the centrality of the nodes appearing in the user's route is minimum. Thereafter, we analyze the network structure by using various network properties such as the clustering coefficient, centrality, average shortest path, rich club coefficient, average packet travel time and order parameter.November 21, 2018 1:51 WSPC/INSTRUCTION FILE ws-mplb˙arxiv 2 Authors' Names which is determined by the structure of the underlying network. In this context, an optimal time-varying communication network model is designed to avoid congestion in the network and user's route is selected based on centrality information especially, betweenness centrality.It is found that the communication networks are scale-free (SF) 6 and are more susceptible to traffic congestion than some homogeneous networks. 7 In SF networks large degree nodes posses a large volume of data hence, congestion usually starts at these nodes and then spreads to the whole network. Therefore, researchers proposed various strategies, 8 which can be classified into hard and soft strategies in order to handle traffic congestion and enhance network capacity. The restructuring of network topology comes under hard strategies. Zhao et al. redistributed a load of heavily loaded nodes to others, 5 some connections are removed between large degree nodes, 9 high betweenness centrality nodes are removed first 10 and links are added between the nodes with long distance. 11 Jiang et al., 12 assigned capacity dynamically to each link proportional to the queue length of the link. Some fraction of links is rewired based on node's degree information and betweenness centrality. 4 Chen et al. 13 rewired the link against traffic congestion and proved that the network should have a core-periphery structure.Sometimes it is impossible to modify the network topological structure and it also incurs a high cost to change the structure of the network. Hence, a soft strategy based on finding a better routing strategy is preferable to enhance the network capacity. Yin et al. 14 chose an efficient path (EP) for routing. Zhao et al. 15 assigned di...

Construction and 3D Simulation of Virtual Animation Instant Network Communication System Based on Convolution Neural Networks

“…Khan et al used 3D simulation software to establish a fast reconstruction model of 3D virtual animation, and then through simulation analysis and modeling of different animation data and with reference to the relevant standard evaluation and animation modeling strategy, they provided a new research idea for the research of fast modeling of 3D virtual animation [ 12 ]. Kumari and Singh analyzed and verified the factors influencing the speed of the instant network communication system through the finite element analysis of 3D animation structure and symmetry calculation [ 13 ]. Lee et al have conducted various research studies on the common instant network communication system environment and introduced the instant network communication system model and particle swarm optimization algorithm with higher accuracy and better reconstruction effect [ 14 ].…”

Intelligent deception techniques against adversarial attack on the industrial system

“…The community detection algorithms (CDAs) have been designed to unveil some essential insights, such as human interactions in social networks, 9,10 subject descriptions in information networks, 11,12 interaction between neurons, 13 the association among terrorists in the criminal network, 14,15 and so forth. Community identification tools raise multiple considerable privacy concerns.…”

“…Like the terrorist network, the deception method can be used to hide the status of the participants in the social network and so on. 11,16,17 Similarly, we may apply deception technique to hide internet of things (IoT) devices in cloud computing environment [18][19][20][21][22][23][24] from the CDAs. By doing that we can preserve the identity of such devices and it will be helpful to perform some analysis on such environments.…”

“…The security agents deploy themselves in the network associated with terrorist activities, who pretend themselves as a part of the terrorist community to get the information about the terrorist activities; however, they do not want to be revealed by the outside world. Like the terrorist network, the deception method can be used to hide the status of the participants in the social network and so on 11,16,17 . Similarly, we may apply deception technique to hide internet of things (IoT) devices in cloud computing environment 18–24 from the CDAs.…”