Mobility Prediction-Assisted Over-The-Top Edge Prefetching for Hierarchical VANETs

Zhao, Zhongliang; Guardalben, Lucas; Karimzadeh Motallebiazar, Mostafa; Silva, Jose; Braun, Torsten; Sargento, Susana (2018). Mobility Prediction-Assisted Over-The-Top Edge Prefetching for Hierarchical VANETs. IEEE journal on selected areas in communications, 36(8), p. 1. IEEE 10.1109/JSAC.2018.2844681

[img] Text
01_manuscript.pdf - Accepted Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (12MB) | Request a copy

Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios such as vehicular ad-hoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based Over-The-Top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Zhao, Zhongliang; Karimzadeh Motallebiazar, Mostafa and Braun, Torsten


000 Computer science, knowledge & systems
500 Science > 510 Mathematics








Dimitrios Xenakis

Date Deposited:

09 Mar 2018 14:17

Last Modified:

01 May 2020 10:47

Publisher DOI:





Actions (login required)

Edit item Edit item
Provide Feedback