A Betweenness Centrality Based Clustering in VANETs

Jabbar, Mays Kareem; Trabelsi, Hafedh

doi:10.1109/sin56466.2022.9970553

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In weighted hypergraph MoDyC Relative position, moving direction, and link lifetime NS2, SUMO [19] KMFW Average distance NS2 [13] VMaSC Average speed NS3, SUMO [14] VMaSC-LTE Average speed NS3, SUMO [15] Arkian et al [15] Speed, neighbor OMNET++, SUMO [16] PMC Speed, neighbors, link lifetime, and position NS2, vanetMobiSim [22] PPC Travel time, ID, and relative velocity NS2 [23] HCAR Lowest ID NS2, VANET MobiSim [6] CVoEG Link lifetime, position, and relative speed MATLAB, SUMO, MOVE [18] FCMS Relative speed, degree, security, and trustworthiness - [24] JCV Movement at the junction, relative position, time, and degree of a node SUMO, CVANETSIM, JAVA [7] Khan et al [7] Link connectivity MATLAB, SUMO, MOVE [20] KMRP Velocity, free bufer size, and node degree NS2 [10] HGCM Relative speed, neighboring, trust score MATLAB, SUMO [21] BCBC Betweenness centrality method MATLAB, SUMO…”

Section: Cluster Formation As a Hypergraphmentioning

confidence: 99%

“…Journal of Electrical and Computer Engineering Some methods employed a K-mean approach and combined it with other algorithms to build stable clusters, such as K-mean and Floyd-Warshall algorithms (KMFW) in [19], the K-means clustering method, a new routing protocol (KMRP) in [20], and a betweenness centrality based clustering (BCBC) in [21]. Te K-mean method is one of the simplest partitioned clustering methods.…”

mentioning

confidence: 99%

See 1 more Smart Citation

EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET

Jabbar

Trabelsi

2023

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

A smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The “Eigen-trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle’s score, and the CH is selected based on the maximum vehicle’s score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay.

show abstract