Cognitive-Radio-Based Resource Management for Smart Transportation: A Sliding Mode Control Approach

Smart transportation is one of the key components of the smart city. A significant amount of data is produced by the numerous vehicles connected via wireless networks. This demands smart spectrum management for efficient data communication. The optimal bandwidth usage can be obtained by opportunistically sharing the spectrum among high-priority class primary and low-priority class secondary users' vehicles. In this study, we develop a mathematical model to ensure effective resource allocation in cognitive-radio-based smart transportation networks (CR-STNs) considering the mobility, bandwidth constraint, and interference models. Robust sliding mode control is synthesized using an exponential reaching law to avoid data packet congestion in CR-STN to ensure smart spectrum management. In addition, a dynamic event-triggered mechanism is incorporated with the sliding mode controller to reduce computational complexity and energy expenses. The effectiveness of the suggested controller has been validated by simulation results.