Network-compressive coding for wireless sensors with correlated data

Ketan Rajawat, Alfonso Cano, Georgios B Giannakis

Research output: Contribution to journalArticlepeer-review

Abstract

A network-compressive transmission protocol is developed in which correlated sensor observations belonging to a finite alphabet are linearly combined as they traverse the network on their way to a sink node. Statistical dependencies are modeled using factor graphs. The sum-product algorithm is run under different modeling assumptions to estimate the maximum a posteriori set of observations given the compressed measurements at the sink node. Error exponents are derived for cyclic and acyclic factor graphs using the method of types, showing that observations can be recovered with arbitrarily low probability of error as the network size grows. Simulated tests corroborate the theoretical claims.

Original languageEnglish (US)
Article number6353398
Pages (from-to)4264-4274
Number of pages11
JournalIEEE Transactions on Wireless Communications
Volume11
Issue number12
DOIs
StatePublished - 2012

Bibliographical note

Funding Information:
Manuscript submitted June 28, 2011; revised February 14, June 11, and August 31, 2012; accepted September 16, 2012. The associate editor coordinating the review of this paper and approving it for publication was S. Valaee. This work was supported by the MURI (AFOSR FA9550-10-1-0567) grant. K. Rajawat and G. B. Giannakis are with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA (e-mail: {ketan, georgios}@umn.edu). A. Cano is with Broadcom Corp., CA, USA (e-mail: alfon.cano@gmail.com). Digital Object Identifier 10.1109/TWC.2012.102612.111230

Keywords

  • compression
  • graphical models
  • Network coding
  • wireless sensor networks

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