What Does Model-Driven Data Acquisition Really Achieve in Wireless Sensor Networks?
Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in Wireless Sensor Networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed.