Abstract
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.
Highlights
Wireless Sensor Networks (WSN) have attracted considerable attention due to the large number of applications, such as environmental monitoring [1], weather forecasts [2]–[4], surveillance [5], health care [6], structural safety and building monitoring [7] and home automation [4], [8]
This coupling enables the concept of Collaborative Wireless Sensor Network (CWSN), in which networks with different capabilities are deployed in the same physical region and collaborate in order to optimize various design criteria and processes [18]
Related work on spatial field reconstruction in sensor networks: It is common in the literature to model the physical phenomenon being monitored by the WSN according to a Gaussian random field (GRF) with a spatial correlation structure [29]–[36]
Summary
Wireless Sensor Networks (WSN) have attracted considerable attention due to the large number of applications, such as environmental monitoring [1], weather forecasts [2]–[4], surveillance [5], health care [6], structural safety and building monitoring [7] and home automation [4], [8]. In many cases these WSN use a small set of high-quality and expensive sensors (such as weather stations) [12]–[14] While these sensors are capable of reliably measuring the environmental physical phenomenon, the low spatial deployment resolution prohibits their use in spatial field reconstruction tasks. To overcome this problem, sparse high-quality sensor deployment can be augmented by the use of complementary cheap low-quality sensors that can be deployed more densely due to their low costs [2], [15]. 2) Query based sensor set selection with performance guarantee: the task is to perform on-line sensor set selection which meets the QoS criterion imposed by the user, as well as minimises the costs of activating the sensors of these networks [26]–[28]
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