  Future Internet of Things (IoT) applications will require that billions of
wireless devices transmit data to the cloud frequently. However, the wireless
medium access is pointed as a problem for the next generations of wireless
networks; hence, the number of data transmissions in Wireless Sensor Networks
(WSNs) can quickly become a bottleneck, disrupting the exponential growth in
the number of interconnected devices, sensors, and amount of produced data.
Therefore, keeping a low number of data transmissions is critical to
incorporate new sensor nodes and measure a great variety of parameters in
future generations of WSNs. Thanks to the high accuracy and low complexity of
state-of-the-art forecasting algorithms, Dual Prediction Schemes (DPSs) are
potential candidates to optimize the data transmissions in WSNs at the finest
level because they facilitate for sensor nodes to avoid unnecessary
transmissions without affecting the quality of their measurements. In this
work, we present a sensor network model that uses statistical theorems to
describe the expected impact of DPSs and data aggregation in WSNs. We aim to
provide a foundation for future works by characterizing the theoretical gains
of processing data in sensors and conditioning its transmission to the
predictions' accuracy. Our simulation results show that the number of
transmissions can be reduced by almost 98% in the sensor nodes with the highest
workload. We also detail the impact of predicting and aggregating transmissions
according to the parameters that can be observed in common scenarios, such as
sensor nodes' transmission ranges, the correlation between measurements of
different sensors, and the period between two consecutive measurements in a
sensor.
