# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
## 4 Experiments and Results
### Energy Consumption Dynamics of ZigBee Networks
The _pdf_ of the Nodes 1, 2, and 3 at speeds of 19,200 and 57,600 bauds were similar, however, at 9600 bauds there were very low consumption values with very low probability that were apparently generated by electrical noise. In addition, we observed that the sensor nodes at low speed showed a greater number of modes, while the Coordinator Node at higher speeds had a greater number of modes than at low speeds. The range with the highest concentration of data consumption acquires an extension when increasing the transmission speed, which occurs only in the Coordinator Nodes. The estimated histograms of the ZigBee network for the three speeds are shown in Figure 13. Figure 13 (center, left) shows that the current ranges with higher probability of occurrence for the sensor and coordinator nodes were the same for the speeds of 9600 and 19,200 bauds. However, Figure 13 (right) exhibits a decrease in the current range with lower probability of occurrence for the sensor nodes, and this range increased for the Coordinator Node. Therefore, the speed increase to 57,600 implied a characteristic increase in the consumption peaks at the Coordinator Node. From these results, we conclude that the increase in the transmission speed to its maximum value increases, although not in a greater proportion, the average consumption of the node that manages the network. From the review of the SAFs plotted in time series and with the M-mode for the three speeds, we conclude that for each node the resulting signals in each realizations were statistically similar. Figure 11: M-mode of _pdf_ with distinctive characteristics for Coordinator Node of the ZigBee network: (**Left**) at 9600 bauds; and (**Right**) at 57,600 bauds. Figure 10: Time signals for ZigBee network at different NPLC. From left to right: 0.2, 2, and 10 NPLC. Figure 14 shows some representative SAFs. In Figure 14a-c, we observe that, for all members of the network, the fall was rapid and persistent. In the Coordinator Node, the persistence peaks were lower, and the correlation was nearby zero, which means that the relationship among observations was very low even in very short delays, and the results were quite similar for all speeds. Hence, the behavior of the SAFs in this node was independent of the transmission rate. In the sensor nodes, we note that for 9600 baud the correlation decreases with an approximately linear trend, and for delays larger than 180 ms, its values were negative. This is to say that the relation between a signal and its displacement is inverse. At 19,200 and 57,600 bauds, the SAF was positive and remains stable around the average. Figure 14d-f represents the M-mode plots of the SAF for Sensor Node 1 to corroborate that this different behavior between speeds was generated in the ten embodiments. The rest of the nodes were not shown because the SAF variations are very similar. The characterization of energy consumption dynamics in terms of seasonality can be complemented by using the average SAF and its CI shown in Figure 15. For the Coordinator Node, the process can be described as uncorrelated for 9600 bauds, since the persistence of the function is lost with increasing delays. At high speeds it was stationary, since the SAF fall was fast and persistence was visible throughout the range of delays. The processes of Nodes 1-3 for 9600 bauds can be described as stationary for the first 200 delays, since the SAF falls are fast and the persistence is pronounced. After that, they were uncorrelated, and they invert due to the persistence, so that the correlation turns negative. In contrast, for 19,200 and 57,600 bauds, the processes were defined as stationary, regardless of the number of delays. Based on the small width of the CIs in all cases, we conclude that the average SAF of each node shows the presence or absence of seasonality in the signals with low accuracy.