Presenting a scheduling algorithm to intelligently reduce urban traffic to improve quality of service (QoS) in vehicular ad hoc networks (VANETs)

Abstract

Urban traffic management in smart cities requires rapid decision-making and real-time data exchange among vehicles. Vehicular Ad-hoc Networks (VANETs) provide this communication infrastructure, but high topology dynamics, variable vehicle density, and wireless channel limitations make it difficult to guarantee Quality of Service (QoS) for heterogeneous messages. This study proposes an intelligent, content-centric scheduling algorithm to mitigate network congestion and support urban traffic reduction. In the proposed method, messages are categorized based on application type into priority queues (emergency, video, audio, and miscellaneous), and transmission scheduling is performed adaptively to reduce end-to-end delay and increase the packet delivery ratio. To simultaneously improve QoS metrics, the periodic adjustment of MAC layer parameters and transmission power was considered a multi-objective optimization problem. Then, with mobility prediction and content-centric clustering, routing and data transmission were reconfigured in each time slot. Performance evaluation was conducted in an urban simulator based on the IEEE 802.11p standard under scenarios involving varying vehicle counts, speeds, and radio ranges. Results demonstrated that the proposed method, compared to reference models, reduces end-to-end delay in dense conditions and improves the successful delivery ratio. Furthermore, emergency messages are transmitted with lower delay and better stability, which is vital for safety applications. This multi-level queuing mechanism reduces competition among ordinary messages and allocates resources to time-sensitive streams. Overall, combining priority-based scheduling, content-centric topology reconstruction, and adaptive MAC adjustment provides an efficient framework to enhance QoS and mitigate congestion effects.