An Optimal Power Allocation Algorithm in Distributed Sensing

We propose a optimal power allocation algorithm for Distributed Sensing networks under the assumption of a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. Each node computes a local statistic and communicates it to the fusion center over rayleigh fading wireless channels. At the fusion, the linear minimum mean square error (LMMSE) is used. Sensor networks in which energy is a limited resource, our goal is to minimize the distortion under certain power constraints. In this paper, for a given power constraints, equal power transmission strategy and the optimal power allocation strategy is considered. In the later, we first discuss the problem with only a sum power constraint, and then discuss the general case with both sum and individual power constraints. Finally, we demonstrate the applicability of our results through numerical examples. Result shows that the optimal distributed estimation algorithm for homogeneous sensor networks achieves the power gain by turning off sensors that with bad channels and bad observation quality.