<![CDATA[Vehicular Ad Hoc Networks (VANETs) play an important role in ensuring reliable communication in Intelligent Transportation Systems (ITS). This helps to improve efficient transportation services for vehicles. However, several existing clustering methods such as mobility based and weighted clustering algorithms, which face challenges in maintaining stability in clusters. This issue is further pronounced in environments where there is high vehicle mobility and periodic changes in network structure. Therefore, to overcome these drawbacks, this study proposes Vehicular Reliable Adaptive Clustering Environment (VRACE), an adaptive clustering method based on a fuzzy approach. This incorporates queuing theory to improve the cluster stability and communication efficiency of the network. This method selects the cluster heads based on several factors such as relative mobility, direction of vehicles, link quality, travel direction and vehicle speed. Estimating these factors allows the structure to make adaptive decisions suitable for dynamic vehicular environments. This system was evaluated through simulation under different vehicle density scenarios using SUMO and NS2. The proposed method improves overall network performance by showing approximately 14% increase in cluster lifetime, 2.5% higher throughput, 4.3% improvement in packet delivery ratio (PDR) and 22.5% reduction in end-to-end delay. These findings indicate that VRACE can support reliable communication in dense and rapidly changing vehicular networks.]]>
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