Provenance is a rapidly progressing new field with many open research problems. Being related to data and processes, provenance research is at the cross-roads of research from several research communities. With the huge amount of information and processes available in sensor networks, provenance becomes crucial for understanding the creation, manipulation and quality of data and processes in this domain too. Sensors collaboratively carry out sensing tasks and forward their data to the closest data processing center, which may further forward it. Provenance provides the means to record the data flow and manipulate snapshots of the network. Consequently given enough data, provenance can be used in sensor network applications to find out causes of faulty behavior, to figure out the circumstances that will affect the performance of the sensor network, to produce trustworthy data after elimination of the causes, etc. In this position paper, we describe provenance work in the sensor network community to sketch a panoramic view of the recent research and give a provenance model of a binary target localization sensor network as a real life example to show how provenance can be used in sensor network applications for fault-tolerance and troubleshooting.
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