Decentralized Estimation for Bandwidth Constrained Sensor Networks in Clustered Environments
Tuncer C. Aysal, Kenneth E. Barner · 2007
This paper extends the homogeneous wireless sensor network (WSN) model to possess (possibly) varying levels of noise that represent environment clusters in a WSN. The estimation of the source parameter is approached from maximum likelihood (ML) perspective. The Cramer Rao lower bound (CRLB) for any unbiased estimator operating in a clustered WSN environment is derived. Noting that the ML estimate cannot be found in closed-from, we resort to a numerical search. Although numerically determined, the ML estimate is guaranteed to converge to the optimal solution since it is shown here that the log-likelihood function is concave. Also considered is the estimation of a random parameter with a priori information, which is approached from maximum a posteriori (MAP) perspective. Finally, the proposed MAP and ML optimal are validated and compared to theoretical bounds with illustrative numerical examples.