Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting

We address the two fundamental problems of spatial
field reconstruction and sensor selection in heterogeneous 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
(e.g., 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 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. 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.