## 6 Results and Discussion
### Forecasting and Transmitting  
The sensor and the BS train the model using the same historical data. This way both models are the same and start forecasting from the same point in time in a dual prediction scheme. The sensor compares every prediction with the observation and triggers a transmission when the error between them is higher than a defined threshold. Consider that the points transmitted by the sensor will help further predictions be more accurate; therefore, they are used to update the wrong values and to forecast the next points.  
A small batch of three days was used to inspect the effect of defining different error thresholds for transmission. The models performed a forecast during the three days, measuring the errors between the observations and the forecast using multiple thresholds. The process starts using the historical data and uses the observations only to update the points transmitted. The percent of points transmitted can be seen in Figure 17, with the thresholds in the X-axis. The threshold is defined from the error in the training stage. Figure 17 presents the threshold without scaling for better understanding.  
It can be seen that the Attention model performs much better than the rest, starting in around 5% of transmission. With a threshold of 1.5 degrees, there is a reduction  
Figure 16: Predictions over testing data using the model with Attention.  
Figure 17: Percent of points transmitted per threshold for the models used for comparison.  
in transmission of around 65% and a MAE of approximately 0.45 degrees. For lower thresholds, the number of points transmitted is higher because the final data have to be more precise; therefore, the sensor has to transmit more points to the BS, and both have to overwrite bad predictions with real data to improve the accuracy of the final prediction.  
Figure 18 shows the MAE between the final predictions after transmitting and the real data per threshold. It can be seen that the Attention model presents a higher MAE per threshold than the rest. This happens because the model is more accurate than the rest; therefore, more points will fall inside the interval defined by the threshold, triggering less transmissions. Taking, for example, a threshold of two degrees, we can see the LSTM model has a lower MAE. However, when checking the points transmitted, we can see that it is around 90%; therefore, the final data will be much closer to the real data and thus the lower MAE.  
Figure 19 shows a comparison between the real temperature data and two final predictions from close, one with a small threshold of 1.5 \({}^{\circ}\)C and 37% of transmitted points and another with a threshold of 3 \({}^{\circ}\)C and approximately 20% of transmitted points.  
We can see that, with a maximum error of 3 \({}^{\circ}\)C, the predicted data still capture the patterns and the abrupt changes of the real data, saving up to 80% of transmission. We can see in Figure 20, the performance of the algorithm with the Attention model in the three time series when using the 1.5 degrees threshold, saving around 60% of transmissions. Independent thresholds for each variable could be used to trigger transmissions in the same manner as it was  
Figure 19: Forecast of temperature with different thresholds.  
Figure 18: MAE of the predicted time series and real observations per threshold.