ABSTRACT The vehicular ad hoc network (VANET) is one of the promising and encouraging technologies, and it is going to attract great attention in the near future. VANET has turned into a main module of the intelligent transport system. It is a self-controlled, wheeled network (also called network on wheels), and a wider and stimulating class of mobile ad hoc network (MANET). VANETs raise many innovative challenges because of their high-class and unique features, such as high-node mobility, dynamic topology changes, wireless links breakage, network constancy, and network scalability. A well-organized routing protocol is one of the most challenging matters of such networks. In this paper, we propose an intelligent naïve Bayesian probabilistic estimation practice for traffic flow to form a stable clustering in VANET, briefly named ANTSC. The proposed scheme aims to improve routing by employing awareness of the current traffic flow as well as considering the blend of several factors, such as speed difference, direction, connectivity level, and node distance from its neighbors by using the intelligent technique. The proposed technique has proven to be more strong, stable, robust, and scalable than existing ones. INDEX TERMS Vehicular ad hoc network (VANET), mobile ad hoc network (MANET), intelligent transportation systems, V2V, V2I, routing, clustering, traffic flow, naïve Bayesian, scalability, reliable.
In REF a naïve Bayesian probabilistic estimation method was presented and applied to the traffic flow for creating a stable cluster in VANET.
ANTSC: An Intelligent Naïve Bayesian Probabilistic Estimation Practice for Traffic Flow to Form Stable Clustering in VANET
IEEE Access
IEEE Access
Computer Science