  One of the main characteristics of Wireless Sensor Networks (WSNs) is the
constrained energy resources of their wireless sensor nodes. Although this
issue has been addressed in several works and got a lot of attention within the
years, the most recent advances pointed out that the energy harvesting and
wireless charging techniques may offer means to overcome such a limitation.
Consequently, an issue that had been put in second place, now emerges: the low
availability of spectrum resources. Because of it, the incorporation of the
WSNs into the Internet of Things and the exponential growth of the latter may
be hindered if no control over the data generation is taken. Alternatively,
part of the sensed data can be predicted without triggering transmissions and
congesting the wireless medium. In this work, we analyze and categorize
existing prediction-based data reduction mechanisms that have been designed for
WSNs. Our main contribution is a systematic procedure for selecting a scheme to
make predictions in WSNs, based on WSNs' constraints, characteristics of
prediction methods and monitored data. Finally, we conclude the paper with a
discussion about future challenges and open research directions in the use of
prediction methods to support the WSNs' growth.
