System design for predictive road-traffic information delivery using edge-cloud computing

Ryoichi Shinkuma, Shingo Kato, Masahiro Kanbayashi, Yasuhiro Ikeda, Ryoichi Kawahara, Takanori Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper presents a novel system architecture for predictive road-traffic information delivery in which computing resources at the network edge and the central cloud are cooperatively used to analyze sensing data collected by vehicles on the road. In this paper, we also present the mathematical problem formulation of the proposed system architecture for ensuring that the system could successfully deliver road-traffic information at realtime without overflowed computational and network loads. The numerical examination using a real dataset and a realistic network emulator validates our system.1

Original languageEnglish
Title of host publicationCCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538647905
DOIs
Publication statusPublished - 2018 Mar 16
Externally publishedYes
Event15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018 - Las Vegas, United States
Duration: 2018 Jan 122018 Jan 15

Publication series

NameCCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
Volume2018-January

Conference

Conference15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018
Country/TerritoryUnited States
CityLas Vegas
Period18/1/1218/1/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

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