A Markovian Analysis for Explicit Probabilistic Stopping-Based Information Propagation in Postdisaster Ad Hoc Mobile Networks

Jiajia Liu, Nei Kato

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

There has been surging research interest in utilizing mobile phones for information relaying in postdisaster areas lacking infrastructure support. A common complication for such postdisaster ad hoc communication is how to efficiently control the forwarding behaviors of relay nodes so as to save their energy consumption and buffer usage while simultaneously guaranteeing the desired delivery performance. Different from previous studies, we consider in this paper an explicit probabilistic stopping mechanism, where a relay node that is actively disseminating a message will stop spreading the message with a certain probability, after meeting another node having already received the message. Besides developing a two-dimensional Markov chain framework to characterize the message propagation process, we also derive the average time required for completion of message propagation, the probability distribution, the expectation, the variance of the fraction of nodes finally receiving the message, etc. Our results reveal that the explicit probabilistic stopping mechanism is very desirable for postdisaster communication, even being able to guarantee a majority of nodes in final message reception. What is more, the developed framework provides us a deeper understanding on how network parameters may affect these important performance metrics, which further enables network designers to accordingly tune controllable parameters.

Original languageEnglish
Article number7185474
Pages (from-to)81-90
Number of pages10
JournalIEEE Transactions on Wireless Communications
Volume15
Issue number1
DOIs
Publication statusPublished - 2016 Jan

Keywords

  • Ad hoc mobile networks
  • Markov chain
  • delivery performance
  • information propagation
  • post-disaster networks
  • probabilistic stopping

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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