An Empowered Intuitionistic Fuzzification Trust Oriented Approach to Handle Broadcast Storm Problem for Emergency Message Transfer in VANET

One of the rapidly expanding research areas with significant benefits for vehicle communication and vehicular adhoc networks is the intelligent transportation system (V ANET). It is the hardest task to provide quality of service during emergency data transfer because of the dynamic and mobility character of vehicle communication, which causes broadcast storm problems due to the large volume of rebroadcast. In this study, emergency data aggregation is used to efficiently manage the broadcast storm problem. To accomplish it, clustering and cluster head selection are suggested to provide secure communication between mobile nodes in the VANET. The cluster head is crucial in rebroadcasting alert messages created by its cluster members and transmitting them to other cluster heads that are heading towards its zone. The lack of consistency and ambiguity in resolving broadcast storm issues in the presence of outliers and border lying vehicles closer to the transmission zone is a problem for conventional clustering techniques. Inadequate cluster head selection also causes end-to-end delays, limited throughput, and high energy consumption. In order to represent each vehicular node in terms of membership grade and non-membership grade and to determine the degree of hesitancy of all nodes belonging to all clusters to transfer limited rebroadcasting of alert messages without compromising reachability, the proposed work adopted intuitionistic fuzzification. While transferring emergency messages in VANET, the quality of service is improved by selecting prospective cluster heads with reduced rebroadcast. The level of hesitation gives more details on how to obtain the maximum reliability in rapid transmission. The potential candidate cluster heads are found using the intuitionistic fuzzy inference model, and the potential cluster heads are then selected for each cluster to avid broadcast storming. While choosing candidate level cluster heads, the trust factor, or level of security, is also taken into consideration. The simulation results demonstrated that, in comparison to other existing clustering algorithms, the Empowered Intuitionistic fuzzification based Trust Oriented Clustering (EIFMTC) proposed for Emergency Data Transfer in VANET offers rapid transfer with least rebroadcast of alert messages.