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.
This paper describes the M3-Competition, the latest of the M-Competitions. It explains the reasons for conducting the competition and summarizes its results and conclusions. In addition, the paper compares such results conclusions with those of the previous two M-Competitions as well as with those of other major empirical studies. Finally, the implications of these results and conclusions are considered, their consequences for both the theory and practice of forecasting are explored and directions for future research are contemplated.