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10 May 2012Leveraging provenance to improve data fusion in sensor networks
Provenance is the information about the origin of the data inputs and the data manipulations to a obtain a
final result. With the huge amount of information input and potential processing available in sensor networks,
provenance is crucial for understanding the creation, manipulation and quality of data and processes. Thus
maintaining provenance in a sensor network has substantial advantages. In our paper, we will concentrate on
showing how provenance improves the outcome of a multi-modal sensor network with fusion. To make the ideas
more concrete and to show what maintaining provenance provides, we will use a sensor network composed of
binary proximity sensors and cameras to monitor intrusions as an example. Provenance provides improvements
in many aspects such as sensing energy consumption, network lifetime, result accuracy, node failure rate. We
will illustrate the improvements in accuracy of the position of the intruder in a target localization network by
simulations.
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Gulustan Dogan, Eunsoo Seo, Theodore Brown, Tarek F. Abdelzaher, "Leveraging provenance to improve data fusion in sensor networks," Proc. SPIE 8407, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012, 840709 (10 May 2012); https://doi.org/10.1117/12.918101