An efficient weighted distributed clustering algorithm for mobile ad hoc networks

Hussein, Abdelrahman H.; Yousef, Sufian; Al-Khayatt, Samir; Arabeyyat, Omar Suleiman

doi:10.1109/icces.2010.5674857

Cited by 28 publications

(11 citation statements)

References 8 publications

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“…This is because the nodes with higher speed quit the current cluster and into another one more rapidly. As we observed from Figure 3-6, the cluster stability of DECW approach is better than that of CBMD and ECUM clustering algorithms [10,11] (less number of inter-cluster transferring and the numbers of CH updating). It is the reason that DECW approach is dynamic entropy combination method, which can track the timely varying network topology more efficiently.…”

Section: Performance Evaluationsmentioning

confidence: 80%

“…However, for small transmission range, most nodes remain out of each other's transmission range, the probability of alone cluster increases, thus the average number of CHs is increased. Also, the results show that the proposed DECW approach produces fewer clusters than CBMD algorithm [10] . Figure 2 depicts the variation of the average number of CHs with respect to the node speed.…”

Section: Performance Evaluationsmentioning

confidence: 97%

“…where E iA ,E iB , and E iC represent the information entropy of clustering index A, B, and C, respectively, whose expression is similar to (10). Similarly, the dynamic entropy weight w iA , w iB , and w iC could be calculated, which satisfy the following constraints 0 ( , , ) 1 1…”

Section: Ch Electionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

Xie¹,

Zhou³

2015

IJFGCN

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Section: Performance Evaluationsmentioning

confidence: 80%

Section: Performance Evaluationsmentioning

confidence: 97%

See 1 more Smart Citation

Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

Xie¹,

Zhou³

2015

IJFGCN

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“…From wireless networks to current MANETs, a number of clustering algorithms [5][6][7][8][9][10] have been proposed in the past years. However, These algorithms are not robust especially in a dynamic environment.…”

Section: Background and Related Workmentioning

confidence: 99%

Group mobility based clustering algorithm for mobile ad hoc networks

Cai

Rui

Liu

et al. 2015

2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS)

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Recent research activities have recognized the essentiality of node mobility for the creation of stable, scalable and adaptive clusters with good performance in mobile ad hoc networks (MANETs). In this paper, we propose a distributed clustering algorithm based on the group mobility and a revised group mobility metric which is derived from the instantaneous speed and direction of nodes. Our dynamic, distributed clustering approach use Gauss Markov group mobility model for mobility prediction that enables each node to anticipate its mobility relative to its neighbors. In particular, it is suitable for reflecting group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. We also take the residual energy of nodes and the number of neighbor nodes into consideration. The proposed clustering scheme aims to form stable clusters by reducing the clustering iterations even in a highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two wellknown clustering approaches, the MOBIC and DGMA, in terms of average number of clusterhead changes.

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“…This section gives an overview of related work on clustering and cluster head selection techniques in MANET. A weighted distributed clustering algorithm, called CBMD was proposed by Hussein et al, [16]. It contemplates the parameters: connectivity (C), residual battery power (B), average mobility (M), and distance (D) of the nodes to select locally best cluster heads.…”

Section: Related Workmentioning

confidence: 99%

A Game Theory Approach for Efficient Node Clustering Based on Unstable Ranging Transmission in MANET

Author¹,

Author²

2018

IJET

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In Mobile Ad Hoc Network (MANET), improving scalability, energy efficiency and network lifetime are the most important and challenging issues. Node Clustering is a well known solution for handling these issues. While existing work focused on stable transmission range, this paper uses unstable ranges transmission for clustering nodes in network. Game theory has been used in fields of science such as economics and biology, but recently it was used in MANET for clustering problem. Grouping mobile nodes into convenient clusters can reduce routing overhead and provide more scalable solution. The main objective of this work is to clustering of nodes in MANET and selects energy efficient cluster head to improve the network lifetime using game theoretical model. This paper proposes an efficient algorithm for Node Clustering based on Unstable Ranging Transmission (NCURT). In addition to this, a game theory based cluster head (CH) selection algorithm is proposed to selects CH based on Evolutionary game theory approach where each node behaves selfish or unselfish based on the strategy selection. Analytical and simulation studies are carried out to assess the performance of the proposed cluster formation and cluster head selection algorithm. The numerical results verify that the proposed approach is effective and efficient to guarantee life time and energy efficiency in MANET.

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An efficient weighted distributed clustering algorithm for mobile ad hoc networks

Cited by 28 publications

References 8 publications

Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

Dynamic Entropy Based Combination Weighted Clustering Approach for High-Speed Ad hoc Network

Group mobility based clustering algorithm for mobile ad hoc networks

A Game Theory Approach for Efficient Node Clustering Based on Unstable Ranging Transmission in MANET

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