We consider universal decentralized estimation of a noise-corrupted signal by a bandwidth constrained sensor network with a fusion center (FC). We show that in a homogeneous sensing environment and under a bandwidth constraint of 1-bit per sample per node, there exist universal decentralized estimation schemes (DBS) with a mean squared error (MSB) decreasing at the rate I/K, where K is the total number of sensors. We extend such 1-bit decentralized estimators to the case of inhomogeneous sensing environment, and propose quantization and transmission power control strategies for local sensors in order to minimize the total consumed sensor energy while ensuring a given MSB performance. We also design a DBS for the joint estimation of a vector source based on its noisy and linearly distorted observations, and show that to achieve a MSB within a factor of 2 away from the best linear unbiased estimator (BLUE), the local message length has a nice form of being the channel capacity of "a virtual AWGN channel" from "nature" to each local sensor.
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