  We consider the distributed source coding problem in which correlated data
picked up by scattered sensors has to be encoded separately and transmitted to
a common receiver, subject to a rate-distortion constraint. Although
near-tooptimal solutions based on Turbo and LDPC codes exist for this problem,
in most cases the proposed techniques do not scale to networks of hundreds of
sensors. We present a scalable solution based on the following key elements:
(a) distortion-optimized index assignments for low-complexity distributed
quantization, (b) source-optimized hierarchical clustering based on the
Kullback-Leibler distance and (c) sum-product decoding on specific factor
graphs exploiting the correlation of the data.
