Wind storm estimation using a heterogeneous sensor network with high and low resolution sensors

We develop a new algorithm for spatial field reconstruction in heterogeneous (mixed analog & digital sensors) wireless sensor networks (WSNs). We consider spatial physical phenomena which are observed by a heterogeneous WSN, meaning that it is partially consists of sparsely deployed high-quality sensors and partially of low-quality sensors. The high-quality sensors transmit their (continuous) noisy observations to the Fusion Centre (FC), while the low-quality sensors first perform a simple thresholding operation and then transmit their binary values over imperfect wireless channels to the FC. We develop a novel algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue.