  Censoring has been proposed to be utilized in wireless distributed detection
networks with a fusion center to enhance network performance in terms of error
probability in addition to the well-established energy saving gains. In this
paper, we further examine the employment of censoring in infrastructure-less
cognitive radio networks, where nodes employ binary consensus algorithms to
take global decisions regarding a binary hypothesis test without a fusion
center to coordinate such a process. We show analytically - and verify by
simulations - that censoring enhances the performance of such networks in terms
of error probability and convergence times. Our protocol shows performance
gains up to 46.6% in terms of average error probability over its conventional
counterpart, in addition to performance gains of about 48.7% in terms of
average energy expenditure and savings up to 50% in incurred transmission
overhead.
