Information, energy and density for ad hoc sensor networks over correlated random fields: Large deviations analysis
2008 IEEE International Symposium on Information Theory, 2008•ieeexplore.ieee.org
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 …
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 …
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.
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