  We present novel lower bounds on the mean square error (MSE) of the location
estimation of an emitting source via a network where the sensors are deployed
randomly. The sensor locations are modeled as a homogenous Poisson point
process. In contrast to previous bounds which are a function of the specific
locations of all the sensors, we present CRB-type bounds on the expected mean
square error; that is, we first derive the CRB on the MSE as a function of the
sensors' location, and then take expectation with respect to the distribution
of the sensors' location. Thus, these bounds are not a function of a particular
sensor configuration, but rather of the sensor statistics. Hence, these novel
bounds can be evaluated prior to sensor deployment and provide insights into
design issues such as the necessary sensor density, the effect of the channel
model, the effect of the signal power, and others. The derived bounds are
simple to evaluate and provide a good prediction of the actual network
performance.
