Wideband communications through drone-assisted cognitive radio VANETs using SURF channel selection

In the past decade, Vehicular Ad-hoc Network (VANET) research has flourished, particularly in the context of smart cities, where it plays a pivotal role in supporting intelligent transportation systems and entertainment services. However, intermittent connectivity, unscalable networks, and high packet collision rates are the key challenges that put hindrances on the wide applications of VANETs. The severity of these challenges becomes even more intensified when deployed in urban areas. Therefore, VANETs should have a communication model that must satisfy the delay and bandwidth needs of VANET applications. To meet the low latency demands of both safety-critical applications and bandwidth-intensive infotainment services while optimizing resource utilization, we introduced cognitive radio technology into drone-assisted VANETs. Our model leverages licensed spectrum opportunistically without interfering with the primary user and relies on line-of-sight communications between drones and vehicles. We employ the SURF channel selection strategy to identify the most suitable channel from available options. SURF selects the channel based on the primary user activity and number of cognitive users. The primary user activity model is based on the alternating on/off Markov Renewal Process (MRP), and the Cognitive Radio (CR) occupancy is based on the number of cognitive neighbors using a specific channel. Extensive experiments are conducted to evaluate the performance of the proposed models for safety-critical and entertainment applications. The results indicate that SURF outperforms the others in terms of the delivery ratio and interference to the primary user, guaranteeing the timely delivery of emergency messages in less than 100 ms to nearby vehicles to avoid further damage. The availability of free spectrum results in a higher throughput of 18 Mbps for entertainment applications.