An Improved Stochastic Modeling of Opportunistic Routing in Vehicular CPS

Deze Zeng, Song Guo, Ahmed Barnawi, Shui Yu, Ivan Stojmenovic

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)

Abstract

Vehicular Cyber-Physical System (VCPS) provides CPS services via exploring the sensing, computing and communication capabilities on vehicles. VCPS is deeply influenced by the performance of the underlying vehicular network with intermittent connections, which make existing routing solutions hardly to be applied directly. Epidemic routing, especially the one using random linear network coding, has been studied and proved as an efficient way in the consideration of delivery performance. Much pioneering work has tried to figure out how epidemic routing using network coding (ERNC) performs in VCPS, either by simulation or by analysis. However, none of them has been able to expose the potential of ERNC accurately. In this paper, we present a stochastic analytical framework to study the performance of ERNC in VCPS with intermittent connections. By novelly modeling ERNC in VCPS using a token-bucket model, our framework can provide a much more accurate results than any existing work on the unicast delivery performance analysis of ERNC in VCPS. The correctness of our analytical results has also been confirmed by our extensive simulations.
Original languageEnglish
Article number6880331
Pages (from-to)1819-1829
Number of pages11
JournalIEEE Transactions on Computers
Volume64
Issue number7
DOIs
Publication statusPublished - 1 Jul 2015
Externally publishedYes

Keywords

  • epidemic routing
  • random linear network coding
  • stochastic analysis
  • Vehicular cyber-physical system

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Hardware and Architecture
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'An Improved Stochastic Modeling of Opportunistic Routing in Vehicular CPS'. Together they form a unique fingerprint.

Cite this