Analysis of Communication Network Performance From a Complex Network Perspective

Wu, Jiajing; Tse, Chi K.; Lau, Francis C. M.; Ho, Ivan Wang‐Hei

doi:10.1109/tcsi.2013.2264697

Cited by 81 publications

(36 citation statements)

References 18 publications

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“…A larger λ c implies that the network can transmit more traffic without congestion and λ c can be a measure of network traffic capacity. In our previous work [4], we have given a theoretical estimation of the critical generation rate λ c as follows:…”

Section: B Critical Generation Ratementioning

confidence: 99%

Complex-Network-Inspired Design of Traffic Generation Patterns in Communication Networks

Xia

2017

IEEE Trans. Circuits Syst. II

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In this brief, we consider a generic type of communication network consisting of two kinds of nodes: hosts and routers, and only the hosts can generate and receive packets. In this kind of network, the traffic performance is closely related to the traffic generation pattern which strongly depends on the hosts' locations. We employ a simulated annealing algorithm to find a near-optimal configuration of the hosts, which effectively balances the traffic loads and improves the overall traffic performance. Our study suggests that the traffic generation pattern is an important factor for communication network design.

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Section: B Critical Generation Ratementioning

confidence: 99%

Complex-Network-Inspired Design of Traffic Generation Patterns in Communication Networks

Xia

2017

IEEE Trans. Circuits Syst. II

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“…Degree distribution is defined by a probability ( ), which is the proportion of the sensors with the same amount of connections. A graph of Erdös-Rényi random WSN has a vertex degree following the Poisson distribution as [33] …”

Section: Network Assumptions and Statistic Based Feature Modelmentioning

confidence: 99%

Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

Suzhe

Cheng

2016

Journal of Sensors

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Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario.

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“…A similar concept named node usage probability can be used here to represent the ratio between the algorithmic betweenness of a node and the total algorithmic betweenness in each network [5]. Therefore,…”

Section: Modelmentioning

confidence: 99%

“…Traffic on such networked systems is a significant issue. A wealth of studies on traffic congestion from the perspective of an isolated complex network framework have been conducted over the past decade [2][3][4][5]. It has been widely revealed that traffic congestion is closely related to the network structure [2,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Traffic congestion in interconnected complex networks

Tan

Xia

et al. 2014

Phys. Rev. E

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Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected BA scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet AS-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly. DOI:PACS number(s): 89.75. Hc, 89.75.Fb, 89.20.Hh,

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Analysis of Communication Network Performance From a Complex Network Perspective

Cited by 81 publications

References 18 publications

Complex-Network-Inspired Design of Traffic Generation Patterns in Communication Networks

Complex-Network-Inspired Design of Traffic Generation Patterns in Communication Networks

Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

Traffic congestion in interconnected complex networks

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