Recent years have witnessed the deployments of wireless sensor networks (WSNs) for many critical applications such as security surveillance, environmental monitoring, and target detection/tracking. Many of these applications involve a large number of sensors distributed in a vast geographical area.
As a result, the cost of deploying these networks into the physical environment is high. A key challenge is thus to predict and understand the expected sensing performance of these WSNs. A fundamental performance measure of WSNs is sensing coverage that characterizes how well a sensing field is monitored by a network.
Many recent studies are focused on analyzing the coverage performance of large-scale WSNs. Despite the significant progress, a key challenge faced is the obvious discrepancy between the advanced information processing schemes adopted by existing sensor networks and the overly simplistic sensing models widely assumed in the previous analytical studies.
On the one hand, many WSN applications are designed based on collaborative signal processing algorithms that improve the sensing performance of a network by jointly processing the noisy measurements of multiple sensors.
In practice, various stochastic data fusion schemes have been employed by sensor network systems for event monitoring, detection, localization, and classification. On the other hand, collaborative signal processing algorithms such as data fusion often have complex complications to the network-level sensing performance such as coverage.
In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR).
We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.
*** Other authors of this report are: Benyuan Liu, University of Massachusetts, Lowell; Jianping Wang and Xiaohua Jia, City University of Hong Kong; and Chih-Wei Yi, Tung University, Taiwan.
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