  The first generation of wireless sensor nodes have constrained energy
resources and computational power, which discourages applications to process
any task other than measuring and transmitting towards a central server.
However, nowadays, sensor networks tend to be incorporated into the Internet of
Things and the hardware evolution may change the old strategy of avoiding data
computation in the sensor nodes. In this paper, we show the importance of
reducing the number of transmissions in sensor networks and present the use of
forecasting methods as a way of doing it. Experiments using real sensor data
show that state-of-the-art forecasting methods can be successfully implemented
in the sensor nodes to keep the quality of their measurements and reduce up to
30% of their transmissions, lowering the channel utilization. We conclude that
there is an old paradigm that is no longer the most beneficial, which is the
strategy of always transmitting a measurement when it differs by more than a
threshold from the last one transmitted. Adopting more complex forecasting
methods in the sensor nodes is the alternative to significantly reduce the
number of transmissions without compromising the quality of their measurements,
and therefore support the exponential growth of the Internet of Things.
