  Sensing and aggregation of noisy observations should not be considered as
separate issues. The quality of collective estimation involves a difficult
tradeoff between sensing quality which increases by increasing the number of
sensors, and aggregation quality which typically decreases if the number of
sensors is too large. We examine a strategy for optimal aggregation for an
ensemble of independent sensors with constrained system capacity. We show that
in the large capacity limit larger scale aggregation always outperforms smaller
scale aggregation at higher noise levels, while below a critical value of
noise, there exist moderate scale aggregation levels at which optimal
estimation is realized.
