Fady Samann^1,2,*, Shavan Askar³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2421-2444, 2024, DOI:10.32604/cmes.2023.031265 - 15 December 2023

Abstract Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms. K-clustering algorithms are simplistic, with fast performance and relative accuracy. However, their implementation depends on the initial selection of clusters number (K), the initial clusters’ centers, and the clustering metric. This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time (LET) as a clustering metric. Realistic traffic flows were considered for three maps, namely Highway, Traffic Light junction, and Roundabout junction, to study the… More >

Fady Samann^1,*, Shavan Askar²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2715-2734, 2023, DOI:10.32604/cmc.2023.043580 - 26 December 2023

Abstract Clustering schemes in vehicular networks organize vehicles into logical groups. They are vital for improving network performance, accessing the medium, and enabling efficient data dissemination. Most schemes rely on periodically broadcast hello messages to provide up-to-date information about the vehicles. However, the periodic exchange of messages overwhelms the system and reduces efficiency. This paper proposes the Flag-based Vehicular Clustering (FVC) scheme. The scheme leverages a combination of Fitness Score (FS), Link Expiration Time (LET), and clustering status flags to enable efficient cluster formation in a hybrid manner. The FVC relies on the periodic broadcast of… More >