  In this paper, we tackle decision fusion for distributed detection in a
randomly-deployed clustered wireless sensor networks (WSNs) operating over a
non-ideal multiple access channels (MACs), i.e. considering Rayleigh fading,
pathloss and additive noise. To mitigate fading, we propose the distributed
equal gain transmit combining (dEGTC) and distributed maximum ratio transit
combining (dMRTC). The first and second order statistics of the received
signals were analytically computed via stochastic geometry tools. Then the
distribution of the received signal over the MAC are approximated by Gaussian
and log-normal distributions via moment matching. This enabled the derivation
of moment matching optimal fusion rules (MOR)for both distributions. Moreover,
suboptimal simpler fusion rules were also proposed, in which all the CHs data
are equally weighed, which is termed moment matching equal gain fusion rule
(MER). It is shown by simulations that increasing the number of clusters
improve the performance. Moreover, MOR-Gaussian based algorithms are better
under free-space propagation whereas their lognormal counterparts are more
suited in the ground-reflection case. Also, the latter algorithms show better
results in low SNR and SN numbers conditions. We have proved that the received
power at the CH in MAC is proportional O(2R2) and to O(2ln2R) in the free-space
propagation and the ground-reflection cases respectively, whereis SN deployment
intensity and R is the cluster radius. This implies that having more clusters
decreases the required transmission power for a given SNR at the receiver.
