A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning and Extended Mobility Features for VANETs

VANETs clustering is an emerging research topic that serves in the intelligent transportation systems of today's technology. It aims at segmenting the moving vehicles in the road environment into sub-groups named clusters, with cluster heads for enabling effective and stable routing. Most of th...

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Vydáno v:IEEE access Ročník 8; s. 169908 - 169921
Hlavní autoři: Talib, Mohammed Saad, Hassan, Aslinda, Alamery, Thamer, Abas, Zuraida Abal, Mohammed, Ali Abdul-Jabbar, Ibrahim, Ali Jalil, Abdullah, Nihad Ibrahim
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
center-based clustering
Clustering
Clustering algorithms
Evolution
evolving clustering
grid based-clustering
Intelligent transportation systems
Measurement
Partitioning
Roads
Stability criteria
VANETs clustering
Vehicular ad hoc networks
ISSN:2169-3536, 2169-3536
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Shrnutí:VANETs clustering is an emerging research topic that serves in the intelligent transportation systems of today's technology. It aims at segmenting the moving vehicles in the road environment into sub-groups named clusters, with cluster heads for enabling effective and stable routing. Most of the VANETs clustering approaches are based on distributed models which make the decision of clusters creation lacking the global view of the vehicle's distribution and mobility in the environment. However, the availability of the LTE and long ranges of base station motivated researchers recently to provide center-based approaches. Unlike existing center-based clustering approaches of VANETs, this article uses the road segmenting phase named grid partitioning before providing summarized information to the clustering center. Furthermore, it presents an integrated approach as a combination of all the clustering tasks including assigning, cluster head selection, removing, and merging. Evaluation of the proposed approach named center-based evolving clustering based on grid partitioning (CEC-GP) is proven superior from the perspective of efficiency, stability, and consistency. An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3020510