Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

Content-Centric Vehicular Networks (CCVNs) are considered as an attractive technology to efficiently distribute and share contents among vehicles in vehicular environments. Due to the large size of contents such as multimedia data, it might be difficult for a vehicle to download the whole of a content within the coverage of its current RoadSide Unit (RSU). To address this issue, many studies exploit mobility-based content precaching in the next RSU on the trajectory of the vehicle. To calculate the amount of the content precaching, they use a constant speed such as the current speed of the vehicle requesting the content or the average speed of vehicles in the next RSU. However, since they do not appropriately reflect the practical speed of the vehicle in the next RSU, they could incorrectly calculate the amount of the content precaching. Therefore, the authors propose an adaptive content precaching scheme (ACPS) that correctly estimates the predictive speed of a requester vehicle to reflect its practical speed and calculates the amount of the content precaching using its predictive speed. ACPS adjusts the predictive speed to the average speed starting from the current speed with the optimized adaptive value. To compensate for a subtle error between the predictive and the practical speeds, ACPS appropriately adds a guardband area to the precaching amount. Simulation results verify that ACPS achieves better performance than previous schemes with the current or the average speeds in terms of the content download delay and the backhaul traffic overhead.

• Record URL:
  http://dx.doi.org/10.3390/s21165376
• Record URL:
  https://www.mdpi.com/1424-8220/21/16/5376
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • 2021© Youngju Nam et al.
• Authors:
  • Nam, Youngju
  • Choi, Hyunseok
  • Shin, Yongje
  • Lee, Euisin
  • Lee, Eun-Kyu
• Publication Date: 2021-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 5376
• Serial:
  • Sensors
  • Volume: 21
  • Issue Number: 16
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Adaptive control; Networks; Predictive models; Vehicle trajectories; Vehicles by speed
• Identifier Terms: Roadside (Computer program)
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01780819
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 30 2021 2:50PM