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Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery

1
Institute of Computing, University of Campinas, Campinas 13083-852, Brazil
2
Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
3
Department of Computer Science, Institute of Exact Sciences, University of Brasília (UnB), Campus Darcy Ribeiro, Asa Norte, Federal District, Brasília-DF 70910-900, Brazil
4
Instituto de Telecomunicações, University of Aveiro, 3810-193 Aveiro, Portugal
5
Computer Science Department, Federal University of Bahia, Salvador 40170-115, Brazil
*
Author to whom correspondence should be addressed.
Vehicles 2020, 2(3), 453-467; https://doi.org/10.3390/vehicles2030025
Received: 10 June 2020 / Revised: 28 June 2020 / Accepted: 30 June 2020 / Published: 4 July 2020
The number of connected cars and the massive consumption of digital content on the Internet have increased daily. However, the high mobility of the vehicles, coming from patterns’ variation over time, makes efficient large-scale content distribution quite challenging. In light of this, the emerging Vehicular Named Data Network (VNDN) architecture provides support for content-centric network communications and caching capabilities, which allows reliable and larger-scale content delivery over Vehicular Ad-Hoc Networks (VANETs). This notwithstanding, the high number of interest packets in VNDN tends to introduce broadcast storm occurrences during the cache discovery process. Thus, network performance degradation comes up for the influence of both increased packet loss rates and delays on content recovery during communication between vehicles. This work proposes a new cache discOVEry pRoTocol (OVERT VNDN), which combines the computational geometry and degree centrality concepts to tackle the VNDN performance degradation challenges and issues. The main idea behind OVERT VNDN is to choose the most appropriate relay vehicles to engage interest packets’ delivery within the VNDN, seeking to achieve higher network performance by optimizing broadcast storm incidence. The obtained results suggest that OVERT VNDN outperforms its competitor in the following key performance indicators: (i) improving the cache discovery process by 120.47%; (ii) enhancing the content delivery rate by 43%; and (iii) reducing the number of interest packets by 80.99%. View Full-Text
Keywords: cache discovery; computational geometry; degree centrality; broadcast storm; VNDN cache discovery; computational geometry; degree centrality; broadcast storm; VNDN
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MDPI and ACS Style

Rondon, L.B.; Immich, R.; Filho, G.P.R.; Venâncio Neto, A.; Leone Maciel Peixoto, M.; Villas, L.A. Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery. Vehicles 2020, 2, 453-467. https://doi.org/10.3390/vehicles2030025

AMA Style

Rondon LB, Immich R, Filho GPR, Venâncio Neto A, Leone Maciel Peixoto M, Villas LA. Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery. Vehicles. 2020; 2(3):453-467. https://doi.org/10.3390/vehicles2030025

Chicago/Turabian Style

Rondon, Lucas B., Roger Immich, Geraldo P.R. Filho, Augusto Venâncio Neto, Maycon Leone Maciel Peixoto, and Leandro A. Villas 2020. "Towards Improved Vehicular Information-Centric Networks by Efficient Caching Discovery" Vehicles 2, no. 3: 453-467. https://doi.org/10.3390/vehicles2030025

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