Skip to main navigation Skip to search Skip to main content

Age of Information in Multicast Networks with Multiple Update Streams

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

Abstract

We consider the age of information in a multicast network where there is a single source node that sends time-sensitive updates to n receiver nodes. Each status update is one of two kinds: type I or type II. To study the age of information experienced by the receiver nodes for both types of updates, we consider two cases: update streams are generated by the source node at-will and update streams arrive exogenously to the source node. We show that using an earliest k1 and k2 transmission scheme for type I and type II updates, respectively, the age of information of both update streams at the receiver nodes can be made a constant independent of n. In particular, the source node transmits each type I update packet to the earliest k1 and each type II update packet to the earliest k2 of n receiver nodes. We determine the optimum k1 and k2 stopping thresholds for arbitrary shifted exponential link delays to individually and jointly minimize the average age of both update streams and characterize the pareto optimal curve for the two ages.
Original languageEnglish
Title of host publication2019 53rd Asilomar Conference on Signals, Systems, and Computers
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1977-1981
Number of pages5
ISBN (Print)978-1-7281-4301-9
DOIs
Publication statusPublished - 6 Nov 2019
Externally publishedYes
Event2019 53rd Asilomar Conference on Signals, Systems, and Computers - Pacific Grove, CA, USA
Duration: 3 Nov 20196 Nov 2019

Conference

Conference2019 53rd Asilomar Conference on Signals, Systems, and Computers
Period3/11/196/11/19

Keywords

  • Receivers
  • Information age
  • Delays
  • Random variables
  • Pareto optimization
  • Automobiles

Fingerprint

Dive into the research topics of 'Age of Information in Multicast Networks with Multiple Update Streams'. Together they form a unique fingerprint.

Cite this