What-if Analysis in Wireless Sensor Networks Using Workflow Provenance

Over the time sensor network readings become large datasets. A user reading the sensed data will not totally comprehend the readings without learning the path taken and understanding the dataset. As this is an accepted fact, the idea of including the provenance data while publishing sensor readings has been around for many years. First, the readings were annotated with data provenance such as reading time, node id. Since only keeping data provenance was not sufficient, the idea of storing workflow provenance arose. Workflow provenance illustrate the path taken to produce the readings and provenance models capture a complete description of evaluation of a workflow. As provenance is crucial for wireless sensor networks to support reproducibility, debugging and result comprehension, they have been an increasingly important part of wireless sensor networks. In our paper, we argue that sensor network provenance systems should support what-if analysis and debugging in order to allow users do modifications, see the results visually without actually running the workflow steps and be able to debug the workflows to figure out the anomalies in a wireless sensor network. Keywords—Workflow Provenance, Trust, Wireless Sensor Networks, Fault Tolerance, Distributed Intelligence, Self Organization.