Distributed estimation with dependent observations in wireless sensor networks
Sung-Hyun Son, Sanjeev R. Kulkarni, S.C. Schwartz · 2006
A wireless sensor network with a fusion center is consid-ered to study the effects of dependent observations on the parameter estimation problem. The sensor observations are corrupted by Gaussian noise with geometric spatial correla-tion. From an energy point of view, sending all the local data to the fusion center is the most costly, but leads to optimum performance results since all the dependencies are taken into account. From an estimation accuracy point of view, send-ing only parameter estimates is the least accurate, but is the most parsimonious in terms of communication costs. Hence, this tradeoff between the energy efficiency and the estima-tion accuracy is explored by comparing the performance of maximum likelihood estimator (MLE) and the sample aver-age estimator (SAE) under various topologies and commu-nication protocols. We start by reviewing the results from the one-dimensional case and continue by extending those results to various two-dimensional topologies. Surprisingly, we discover a class of regular polygon topologies where the MLE under spatial correlation reduces to the SAE. 1.