How much information can one get from a wireless ad hoc sensor network over a correlated random field?

Youngchul Sung, H. Vincent Poor, Heejung Yu

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


New large-deviations results that characterize the asymptotic information rates for general d-dimensional (d-D) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a two-dimensional (2-D) lattice, the asymptotic behavior of ad hoc sensor networks deployed over correlated random fields for statistical inference is investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric first-order conditional autoregression and the assumption of no in-network data fusion, the behavior of the total obtainable information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density, and energy vary. When the sensor node density is fixed, the energy efficiency decreases to zero with rate Θ (area1/2) and the per-node information under fixed per-node energy also diminishes to zero with rate O(Nt-1/3 as the number Nt of network nodes increases by increasing the coverage area. As the sensor spacing dt increases, the per-node information converges to its limit D with rate D - √̄ dne-αdn for a given diffusion rate α. When the coverage area is fixed and the node density increases, the per-node information is inversely proportional to the node density. As the total energy Et consumed in the network increases, the total information obtainable from the network is given by O (log Et) for the fixed node density and fixed coverage case and by Θ(Et2/) for the fixed per-node sensing energy and fixed density and increasing coverage case.

Original languageEnglish (US)
Pages (from-to)2827-2847
Number of pages21
JournalIEEE Transactions on Information Theory
Issue number6
StatePublished - 2009

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Ad hoc sensor networks
  • Asymptotic Kullback-Leibler information rate
  • Asymptotic mutual information rate
  • Conditional autoregressive model
  • Gauss-Markov random fields
  • Large deviations principle
  • Stationary Gaussian fields


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