arrow
Return

Cooperative mobile edge computing system for VANET-based software-defined content delivery

delete2018-10-01
delete29
PRE
AI
J
Jafar Albadarneh
Y
Yaser Jararweh *
M
Mahmoud Al‐Ayyoub
R
Ramon dos Reis Fontes
M
Mohammad AL-Smadi
C
Christian Esteve Rothenberg
DOI:10.1016/j.compeleceng.2018.07.021delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Next-generation smart cities and Internet of Things (IoT) are getting more mature in terms of services and infrastructure requirements. Multiple smart vehicle applications are being conceived these days, including road traffic, road safety and infotainment, all of which are suffering from the WAN-latency problem. In this paper, we propose a Vehicular Adhoc Network (VANET)-based Software-Defined Edge Computing infrastructure supporting content delivery services among connected vehicles. The proposed approach leverages network base stations to embed mobile edge computing (MEC) services closer to the vehicles. Our approach can enable the delivery of more competitive services with reduced-latency by utilizing cooperative MEC search strategy for vehicle to infrastructure (V2I) communications as well as utilizing vehicle-level caching for vehicle to vehicle (V2V) communications between peers. The framework prototype has been implemented as a clean extension of the Mininet-WiFi emulator. Preliminary results serve as validation of the proposed framework and point out the potential benefits of the approach in mitigating WAN-latency in VANET.
Keywords:
Vehicular Ad-hoc networks
Content delivery service
Mobile edge computing
Software defined systems
Internet of things
Intelligent transportation systems
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

C
Computers and Electrical Engineering
IF:
4.9
Papers:
6.7K
Citations:
1.3W

Organization

U
universidade estadual de campinas
Scholars:
3.3W
Papers: 2.3W
Citations: 19