Time-Varying Network Modeling and Its Optimal Routing Strategy

Kumari, Suchi; Singh, Anurag

doi:10.1142/s0219525918500066

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…At each time instant, few nodes appear and some links are also getting added into the network. A TVCN model is proposed by us 21 where, three operations are performed at each time instants; addition of nodes and links, removal and rewiring of links. Addition and rewiring of links are based on the preferential attachment where, probability Π that a node i will be selected through preferential attachment is proportional to its degree and is given by, Π = ki j∈N kj .…”

Section: Time Varying Communication Network Modelmentioning

confidence: 99%

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

Kumari

Singh

2019

Mod. Phys. Lett. B

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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...

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Section: Time Varying Communication Network Modelmentioning

confidence: 99%

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

Kumari

Singh

2019

Mod. Phys. Lett. B

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Intelligent deception techniques against adversarial attack on the industrial system

Kumari¹,

Yadav²,

Namasudra

et al. 2021

Int J Intell Syst

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Community detection algorithms (CDAs) are aiming to group nodes based on their connections and play an essential role in the complex system analysis. However, for privacy reasons, we may want to prevent communities or a group of nodes in the complex industrial network from being discovered in some instances, leading to the topics on community deception. In this paper, we introduce and formalize two intelligent community deception methods to conceal the nodes from various CDAs. We used node‐based matrices, persistence and safeness scores, to formalize the optimization problems to confound the CDAs. The persistence score is used to destabilize the constant communities in the network while the safeness score is used to assess the level of hiding of a node from CDAs. The objective functions aim to minimize the persistence score and maximize the safeness score of the nodes in the network. From the simulation results, it can be analyzed that the proposed strategies are intelligently concealing the community information in the complex industrial system.

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“…Network structure may be optimized by redistributing the traffic of heavily loaded [6], removing some congested nodes or links [7], adding some network resources [8] and so on. In real networks, the resources such as capacity, queue size are finite.…”

Section: Introductionmentioning

confidence: 99%

Efficient edge rewiring strategies for enhancement in network capacity

Kumari

Abhishek

Singh

2020

Physica A: Statistical Mechanics and its Applications

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The structure of the network has great impact on its traffic dynamics. Most of the real world networks follow heterogeneous structure and exhibit scale-free feature. In scale-free network, a new node prefer to connect with hub nodes and the network capacity is curtailed by smaller degree nodes. Therefore, we propose rewiring a fraction of links in the network, to improve the network transport efficiency. In this paper, we discuss some efficient link rewiring strategies and perform simulations on scale-free networks, confirming the effectiveness of these strategies. The rewiring strategies actually reduce the centrality of the nodes having higher betweenness centrality. After the link rewiring process, the degree distribution of the network remains the same. This work will be beneficial for the enhancement of network performance.

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Time-Varying Network Modeling and Its Optimal Routing Strategy

Cited by 9 publications

References 21 publications

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

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

Intelligent deception techniques against adversarial attack on the industrial system

Efficient edge rewiring strategies for enhancement in network capacity

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