  Designing resource allocation strategies for power constrained sensor network
in the presence of correlated data often gives rise to intractable problem
formulations. In such situations, applying well-known strategies derived from
conditional-independence assumption may turn out to be fairly suboptimal. In
this paper, we address this issue by proposing an adjacency-based spatial
whitening scheme, where each sensor exchanges its observation with their
neighbors prior to encoding their own private information and transmitting it
to the fusion center. We comment on the computational limitations for obtaining
the optimal whitening transformation, and propose an iterative optimization
scheme to achieve the same for large networks. We demonstrate the efficacy of
the whitening framework by considering the example of bit-allocation for
distributed estimation.
