In this paper we address the problem of inferring the topology, 
or inter-node navigability, of a sensor network given 
non-discriminating observations of activity in the 
environment.  By exploiting motion present in the environment, 
our approach is able to recover a probabilistic model of the 
sensor network connectivity graph and the underlying traffic 
trends.  We employ a reasoning system made up of a stochastic 
Expectation Maximization algorithm and a higher level search 
strategy employing the principle of Occam's Razor to look 
for the simplest solution explaining the data.  The 
technique is assessed through numerical simulations and 
experiments conducted on a real sensor network.