LAUSR

Smart connected vehicles are becoming standardized with the incorporation of information and communication technology. Connected vehicles are employed for surveillance and management of road traffic, navigation assistance, etc., by inheriting different analytical and communication techniques. With the Social Internet of Things (SIoT), interogrowthperable and shared computing models are adopted by the connected vehicles to perform application-specific decisions. By considering the need for computation models in smart connected vehicle networks, this article introduces a shared adaptive computing model (SACM) for improving the reliability of vehicle control and traffic management. This computing model considers multiple features of the in-range vehicles in detecting traffic and providing guided solutions for reliable routing in a smart city environment. This computing model is aided by the conditional support vector machine (SVM) for differentiating the complexity of multiflow data processing from the neighboring vehicles. The physical and connectivity-based factors from the smart vehicle using SVM classification learning improve the decision reliability and reduce the computing time and complexity.