Efficient traffic management with adaptive SDN in vehicular networks

Abstract

Improving road safety and easing congestion require effective real-time traffic data analysis and management. A crucial part of intelligent transportation systems, vehicular ad hoc networks (VANETs) deal with issues like inconsistent data from erratic vehicle movements and frequent topology changes. In order to develop a responsive and adaptable network management architecture for VANETs, this study makes use of Software-Defined Networking (SDN). SDN optimizes traffic flow, boosts routing efficiency, and improves Quality of Service (QoS) by separating the control and data planes. Traffic analysis and network performance are greatly improved when SDN is combined with priority algorithms and the Zigbee protocol. The effectiveness of this strategy in a controlled setting is shown by simulations conducted with COOJA software. Web digitization tools are also used to guarantee the accuracy of the data. Improved QoS, better traffic flow management, and scalable solutions for dynamic vehicular networks are some of the main results.