Wideband communications through drone-assisted cognitive radio VANETs using SURF channel selection
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Le résumé fourni par la source
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 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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Wideband communications through drone-assisted cognitive radio VANETs using SURF channel selection
- Date Crossref
- 01/07/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Engineering and Technology Taxila pays non établi dans la noticeUniversité ou école supérieure
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Sivas State Hospital pays non établi dans la noticeÉtablissement de santé
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Sivas Bilim ve Teknoloji Üniversitesi pays non établi dans la noticeUniversité ou école supérieure
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COMSATS University Islamabad pays non établi dans la noticeUniversité ou école supérieure
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HITEC University pays non établi dans la noticeUniversité ou école supérieure
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Noroff University College pays non établi dans la noticeUniversité ou école supérieure
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Middle East University pays non établi dans la noticeUniversité ou école supérieure
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Umm al-Qura University pays non établi dans la noticeUniversité ou école supérieure
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Sivas University of Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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College of Computer and Information Systems Computer and Network Engineering Department pays non établi dans la noticeUniversité ou école supérieure
University of Engineering and Technology Taxila, Sivas State Hospital et Sivas Bilim ve Teknoloji Üniversitesi, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.