  We show a tight lower bound of $\Omega(N \log\log N)$ on the number of
transmissions required to compute the parity of $N$ input bits with constant
error in a noisy communication network of $N$ randomly placed sensors, each
having one input bit and communicating with others using local transmissions
with power near the connectivity threshold. This result settles the lower bound
question left open by Ying, Srikant and Dullerud (WiOpt 06), who showed how the
sum of all the $N$ bits can be computed using $O(N \log\log N)$ transmissions.
The same lower bound has been shown to hold for a host of other functions
including majority by Dutta and Radhakrishnan (FOCS 2008).
  Most works on lower bounds for communication networks considered mostly the
full broadcast model without using the fact that the communication in real
networks is local, determined by the power of the transmitters. In fact, in
full broadcast networks computing parity needs $\theta(N)$ transmissions. To
obtain our lower bound we employ techniques developed by Goyal, Kindler and
Saks (FOCS 05), who showed lower bounds in the full broadcast model by reducing
the problem to a model of noisy decision trees. However, in order to capture
the limited range of transmissions in real sensor networks, we adapt their
definition of noisy decision trees and allow each node of the tree access to
only a limited part of the input. Our lower bound is obtained by exploiting
special properties of parity computations in such noisy decision trees.
