Using known vectors to improve data dissemination in opportunistic networks

Jyh How Huang*, Ying Yu Chen, Li Ping Tung, Ling Jyh Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

An opportunistic network is a type of challenged network in which contacts are intermittent, an end-to-end path rarely exists between the source and the destination, disconnection and reconnection are common occurrences, and link performance is highly variable or extreme. Conventional methods of data dissemination in opportunistic networks rely on a meta-message exchange scheme, called the summary vector (SV), to prevent redundant transmission of data bundles that already exist on receivers. We consider that the SV scheme is costly in terms of the data transmission overhead, which is unaffordable in opportunistic networks. Hence, we propose an alternative scheme, called the known vector (KV), to improve the efficiency of meta-message exchanges for data transmission in opportunistic networks. Using a comprehensive set of simulations with realistic network scenarios, we demonstrate that the KV scheme can be easily integrated into existing opportunistic network routing protocols (e.g., Epidemic and PRoPHET routing). Moreover, it can reduce the communication overhead significantly, thereby improving energy efficiency for data transmission in opportunistic networks.

Original languageEnglish
Pages (from-to)59-69
Number of pages11
JournalInternational Journal of Ad Hoc and Ubiquitous Computing
Volume17
Issue number1
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Ad-hoc network
  • DTN routing
  • Delay tolerant network
  • Opportunistic network
  • Wireless network routing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Using known vectors to improve data dissemination in opportunistic networks'. Together they form a unique fingerprint.

Cite this