  Using large deviations results that characterize the amount of information
per node on a two-dimensional (2-D) lattice, asymptotic behavior of a sensor
network deployed over a correlated random field for statistical inference is
investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric
first order conditional autoregression, the behavior of the total 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.
