Time series: theory and methods
Time series: theory and methods
Elements of information theory
Elements of information theory
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Error Exponents for Neyman–Pearson Detection of Markov Chains in Noise
IEEE Transactions on Signal Processing
Impact of Data Retrieval Pattern on Homogeneous Signal Field Reconstruction in Dense Sensor Networks
IEEE Transactions on Signal Processing
Sensor Configuration and Activation for Field Detection in Large Sensor Arrays
IEEE Transactions on Signal Processing
Neyman-pearson detection of gauss-Markov signals in noise: closed-form error exponentand properties
IEEE Transactions on Information Theory
On Divergences and Informations in Statistics and Information Theory
IEEE Transactions on Information Theory
How Dense Should a Sensor Network Be for Detection With Correlated Observations?
IEEE Transactions on Information Theory
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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(area-1/2) 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 dn 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(Et2/3) for the fixed per-node sensing energy and fixed density and increasing coverage case.