  We address the two fundamental problems of spatial field reconstruction and
sensor selection in het- erogeneous sensor networks. We consider the case where
two types of sensors are deployed: the first consists of expensive, high
quality sensors; and the second, of cheap low quality sensors, which are
activated only if the intensity of the spatial field exceeds a pre-defined
activation threshold (eg. wind sensors). In addition, these sensors are powered
by means of energy harvesting and their time varying energy status impacts on
the accuracy of the measurement that may be obtained. We account for this
phenomenon by encoding the energy harvesting process into the second moment
properties of the additive noise, resulting in a spatial heteroscedastic
process. We then address the following two important problems: (i) how to
efficiently perform spatial field reconstruction based on measurements obtained
simultaneously from both networks; and (ii) how to perform query based sensor
set selection with predictive MSE performance guarantee. We first show that the
resulting predictive posterior distribution, which is key in fusing such
disparate observations, involves solving intractable integrals. To overcome
this problem, we solve the first problem by developing a low complexity
algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next,
building on the S-BLUE, we address the second problem, and develop an efficient
algorithm for query based sensor set selection with performance guarantee. Our
algorithm is based on the Cross Entropy method which solves the combinatorial
optimization problem in an efficient manner. We present a comprehensive study
of the performance gain that can be obtained by augmenting the high-quality
sensors with low-quality sensors using both synthetic and real insurance storm
surge database known as the Extreme Wind Storms Catalogue.
