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Wireless sensor networks are typically deployed to monitor phenomena that vary over the spatial region the sensor network covers. The sensor readings may also be dual-used for additional purposes. In this paper, the authors propose to use the inherent spatial variability in physical phenomena, such as temperature or ambient acoustic energy, to support localization and position verification. They first present the problem of localization using general spatial information fields, and then, propose a theory for exploiting this spatial variability for localization. Their Spatial Correlation Weighting Mechanism (SCWM) uses spatial correlation across different phenomena to isolate an appropriate subset of environmental parameters for better location accuracy.
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