One task in which inaccurate measurements are often used is location discovery, a process where the nodes in a network determine their locations. We have focused on location discovery as the primary target of our study since many sensor network tasks are dependent on location information. We demonstrate the benefits of location error analysis for system software and applications in wireless sensor networks. The technical highlight of our work is a statistically validated parameterized model of location errors that can be used to evaluate the impact of a location discovery algorithm on subsequent tasks. We prove that the distribution of location error can be approximated with a family of Weilbull distribution.s Then, we show that while performing the location discovery task, the nodes in a network can estimate the parameters of the distribution. Finally, we describe how applications can use the estimated statistical parameters to: (i) estimate the confidence intervals for their results, (ii) organize resource consumption to achieve optimal results in presence of estimated magnitude of error.
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