Abstract
We develop new algorithms for spatial field reconstruction, exceedance level estimation and classification 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 consists partially 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. The resulting observations are mixed continuous and discrete (1-bit decisions) observations, and are combined in the FC to solve the inference problems. We first formulate the problem of spatial field reconstruction, exceedance level estimation and classification in such heterogeneous networks. We show that the resulting posterior predictive distribution, which is key in fusing such disparate observations, involves intractable integrals. To overcome this problem, we develop an 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.
Highlights
W IRELESS SENSOR NETWORKS (WSN) have attracted considerable attention due to the large number of applications, such as environmental monitoring [1], weather forecasts [2]–[5], surveillance [6], health care [7], structuralManuscript received September 25, 2014; revised January 07, 2015; accepted February 20, 2015
The main goal of this paper is to develop low complexity algorithms to solve the problems of spatial field reconstruction, exceedance level and spatial classification of spatial Gaussian random fields in WSN under practical scenarios of mixed analog and digital sensors
We consider a generic WSN where the sensors deployed in the field are composed of two types of sensors: 1) High-quality sensors : analog sensors which transmit their noisy observations over Additive White Gaussian Noise (AWGN) channels
Summary
W IRELESS SENSOR NETWORKS (WSN) have attracted considerable attention due to the large number of applications, such as environmental monitoring [1], weather forecasts [2]–[5], surveillance [6], health care [7], structural. We move beyond the estimation of a single location parameter by developing models to reconstruct the entire spatial random field which exhibits spatial dependency structure that we capture via either a homogeneous or non-homogeneous spatial covariance function, depending on the statistical properties of the observed spatial field In many cases these WSN use a small set of high-quality and expensive sensors (such as weather stations) [29]. The main goal of this paper is to develop low complexity algorithms to solve the problems of spatial field reconstruction, exceedance level and spatial classification of spatial Gaussian random fields in WSN under practical scenarios of mixed analog and digital sensors. In addition we study the impact that different deployments configurations have on the performance and the trade-offs between analog and digital deployments
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