An Efficient K-Hop Weighted Domination Clustering Algorithm for Mobile Ad Hoc Networks Using Ranking

Prakash, Tapan; Nair, N. Kesavan

doi:10.1109/iccima.2007.39

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…In addition, it does not consider the capability factor of a node to act as cluster head and is mainly adaptable for large and dense network.. In clustering based on ranking procedure [6], the node with a highest weight is selected as the cluster head and the correct position of the nodes are identified. But there is a chance of redundancy in this approach.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Stability of Network through Clustering in Mobile Ad Hoc Networks

Francis

Rajsingh

George

2009

2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies

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Clustering is a vital technique widely used in MANETS. The clustering technique guarantees performance by increasing the stability and throughput of the network. The clustering technique divides the network into group of mobile nodes as clusters. A clustering protocol is proposed that employs a powerful analytical hierarchy process methodology, a mathematical model to compute relative weights for all mobile nodes to choose appropriate cluster heads in the network. The three crucial factors of the mobile nodes are identified that influence the selection of the cluster head. This novel clustering enhances the stability of the network to a great extent. Simulation results show the study of various performance metrics using ns-2.30 simulator and shows that the performance is better than previous clustering algorithms.

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Section: Related Workmentioning

confidence: 99%

“…The local weight is computed for each criterion and alternatives using Eigen vector method [5,6]. For instance, the matrix C is considered to calculate the Eigen value equation and it is written as where N ranges from 1 to 5 as the mobile nodes are evaluated under three secondary goals and t ranges from 1 to x.…”

Section: ) Calculating Weight Vectormentioning

confidence: 99%

Enhancing Stability of Network through Clustering in Mobile Ad Hoc Networks

Francis

Rajsingh

George

2009

2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies

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show abstract

“…1- 14 The minimum connected dominating set (MCDS) of the network topology graph forms a virtual backbone by which the routing overhead can be significantly reduced, where the number of vertices responsible for the route discovery and data transmission can be reduced to the 6,9,12-16 A WCDS of a graph G is a dominating subset of G so that the vertex set of the given subset and all the vertices with at least one endpoint in the subset induce a connected sub-graph of G. Finding the WCDS 1,2,4-6,8,10 is a well-known approach for clustering the wireless ad hoc networks, 17,[18][19][20][21][22][23][24][25] and was first proposed by Chen and Listman in Ref. 4.…”

Section: Introductionmentioning

confidence: 99%

Learning Automata-Based Algorithms for Finding Minimum Weakly Connected Dominating Set in Stochastic Graphs

Torkestani

Meybodi

2010

Int. J. Unc. Fuzz. Knowl. Based Syst.

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A weakly connected dominating set (WCDS) of graph G is a subset of G so that the vertex set of the given subset and all vertices with at least one endpoint in the subset induce a connected sub-graph of G. The minimum WCDS (MWCDS) problem is known to be NP-hard, and several approximation algorithms have been proposed for solving MWCDS in deterministic graphs. However, to the best of our knowledge no work has been done on finding the WCDS in stochastic graphs. In this paper, a definition of the MWCDS problem in a stochastic graph is first presented and then several learning automata-based algorithms are proposed for solving the stochastic MWCDS problem where the probability distribution function of the weight associated with the graph vertices is unknown. The proposed algorithms significantly reduce the number of samples needs to be taken from the vertices of the stochastic graph. It is shown that by a proper choice of the parameters of the proposed algorithms, the probability of finding the MWCDS is as close to unity as possible. Experimental results show the major superiority of the proposed algorithms over the standard sampling method in terms of the sampling rate.

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An Efficient K-Hop Weighted Domination Clustering Algorithm for Mobile Ad Hoc Networks Using Ranking

Cited by 2 publications

References 27 publications

Enhancing Stability of Network through Clustering in Mobile Ad Hoc Networks

Enhancing Stability of Network through Clustering in Mobile Ad Hoc Networks

Learning Automata-Based Algorithms for Finding Minimum Weakly Connected Dominating Set in Stochastic Graphs

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