Design and Performance Evaluation of Meghadoot - A Hybrid Wireless Network Architecture

Ramana, BV; Agrawal, Devesh; Murthy, C. Ram

doi:10.1109/icon.2006.302627

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Graph theory, which is widely developed in computer science,28, 29 has been used in bioinformatics and several bioinformatics methods have been developed based on graph theory 30–33. Computer science is one of the basic theories of bioinformatics, therefore graph theory is very useful for obtaining a comprehensive understanding of biological processes.…”

Section: Methodsmentioning

confidence: 99%

“…Here, we decomposed the networks into 1‐hop subgraphs, 1‐hop is a kind of k‐hop when k = 1, and it is the simplest form of k‐hop. The 1‐hop neighborhood subgraph rooted by vertex v x ∈ V of the graph G ( V , E ) is simply a group of vertexes from G that are reachable in one step from v x , using the edges in E and all the edges connected these vertexes, from E 28, 29 (see Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

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Protein–protein interaction network‐based detection of functionally similar proteins within species

et al. 2012

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Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co‐evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein–protein interaction networks using graphs, we split the graphs into subgraphs using the 1‐hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein–protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Proteins 2012; © 2012 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Protein–protein interaction network‐based detection of functionally similar proteins within species

et al. 2012

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A Hierarchical Semi-centralized Architecture for Load Balancing of Heterogeneous Wireless Networks

Shi

2010

2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing

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A Network Architecture for Load Balancing of Heterogeneous Wireless Networks

Shi

Li²,

Li³

et al. 2011

JNW

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The traditional centralized load balancing had relatively low reliability, and the distributed load balancing has a huge overhead. To solve these problems, this paper mapped heterogeneous wireless networks to distributed grids by introducing Resource Management Unit, and then presented a hierarchical semi-centralized architecture for load balancing of heterogeneous wireless networks drawing on the idea of grid in computer networks. The analytical models for the integrated reliability and signaling overhead of the architecture were established. Theoretical analysis and simulation results indicate that the architecture can reduce the signaling overhead and improve the system reliability effectively. Privacy Policy and Terms of Service apply.

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