Optimal Node Density for Two-Dimensional Sensor Arrays
arXiv:0805.1262 · doi:10.1109/SAM.2008.4606870
Abstract
The problem of optimal node density for ad hoc sensor networks deployed for making inferences about two dimensional correlated random fields is considered. Using a symmetric first order conditional autoregressive Gauss-Markov random field model, large deviations results are used to characterize the asymptotic per-node information gained from the array. This result then allows an analysis of the node density that maximizes the information under an energy constraint, yielding insights into the trade-offs among the information, density and energy.
Proceedings of the Fifth IEEE Sensor Array and Multichannel Signal Processing Workshop, Darmstadt, Germany, July 21 - 23, 2008