Empirical Study of Clustering Algorithms for Wireless Ad Hoc Networks

Tselikis, C.; Mitropoulos, Sarandis; Douligeris, Christos; Ladis, E.; Georgouleas, Kyriakos; Vangelatos, C.; Komninos, Nikos

doi:10.1109/iwssip.2009.5367771

Cited by 3 publications

(2 citation statements)

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“…The highest node degree clustering algorithm [18] is based on the degree of connectivity needed for clustering, and selects the node with the appropriate number of neighboring nodes as cluster head by calculating the numbers of neighbors of nodes. Shi and Luo [19] proposed a mechanism of cluster based location aided dynamic source routing, where CH is elected based on the energy level, relative velocity and degree of connectivity.…”

Section: Related Workmentioning

confidence: 99%

Security Clustering Algorithm Based on Integrated Trust Value for Unmanned Aerial Vehicles Network

2020

KSII TIIS

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Unmanned aerial vehicles (UAVs) network are a very vibrant research area nowadays. They have many military and civil applications. Limited bandwidth, the high mobility and secure communication of micro UAVs represent their three main problems. In this paper, we try to address these problems by means of secure clustering, and a security clustering algorithm based on integrated trust value for UAVs network is proposed. First, an improved the k-means++ algorithm is presented to determine the optimal number of clusters by the network bandwidth parameter, which ensures the optimal use of network bandwidth. Second, we considered variables representing the link expiration time to improve node clustering, and used the integrated trust value to rapidly detect malicious nodes and establish a head list. Node clustering reduce impact of high mobility and head list enhance the security of clustering algorithm. Finally, combined the remaining energy ratio, relative mobility, and the relative degrees of the nodes to select the best cluster head. The results of a simulation showed that the proposed clustering algorithm incurred a smaller computational load and higher network security.

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Section: Related Workmentioning

confidence: 99%

Security Clustering Algorithm Based on Integrated Trust Value for Unmanned Aerial Vehicles Network

2020

KSII TIIS

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“…single-factor clustering algorithms represented by the minimum ID clustering algorithm [12] , the minimum mobility clustering algorithm [13] and the highest node degree method [14] are difficult to meet the clustering requirements of the swarm UAV network. Scholars have paid more attention to the research of on-demand clustering and weighted clustering algorithms.…”

mentioning

confidence: 99%

A clustering algorithm for UV-cooperative UAV network in dynamic scenarios

Zhao,

刘,

Zhang

et al. 2023

Preprint

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To address the electromagnetic interference problems faced by swarm UAVs in complex environments and the clustering needs of swarm UAV networks in dynamic scenarios, this paper proposes a weighted clustering algorithm for wireless UV cooperative swarm UAVs, which combines the advantages of UV anti-interference and high secrecy to provide a reliable communication method for swarm UAVs. By introducing adaptive node degree, UAV energy efficiency, and distance stability parameters to improve the cluster head election formula, the algorithm is made more applicable to dynamic scenarios. Simulation results show that this algorithm can improve the network survival period by 16%, while reducing the average number of cluster heads by 37% and nearly doubling the data transmission volume.

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