# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
## Appendix B  
Here, we explain in detail the _methodology of energy consumption analysis_ using as an example the DigiMesh network at a transmission rate of 9600 baud. This explanation extends to the analysis of the other case studies. Table 1 summarizes the applicability of the different statistical methods used in this study for the acquired electrical current signals. The revision of the time series of the ten repetitions in the DigiMesh network at 9600-baud rate showed that, for all cases, consumption remains oscillating around the mean. Hence, there is no actual trend, and the variability remains stable, which allows us to say that the behavior of these signals was mostly homoscedastic. Figure 11 depicts two example observations, in which the value of the mean (\(\mu\)) and the standard deviation (\(\sigma\)) of each node are also included. In addition, since the CO\({}_{2}\) sensor of Node 4 requires greater current for its operation compared with the sensors installed in the other nodes, the average energy consumption of this node was notably higher. The coordinator incorporated only the communication module XBEE ZB S1 PRO, which is manufactured with ultra-low power technology, hence its consumption was lower. The patterns of the spikes in each node were analyzed in detail in each series. In the absence of interaction among the elements of the network, their consumption remains almost constant around the average. However, when the communication is established, the consumption peaks are generated with different amplitudes and at time spacings, although they were very similar in terms of sequence of events. Figure 12 shows in an increased scale the consumption peaks of two realizations taken as an example at different time instants. Note that the Coordinator Node opens the communication channel and generates a data request to the four Sensor Nodes, then each node sends the information of the variables, and finally the Coordinator generates a closing communication message. The average transmission duration \(T\) obtained after visual analysis of all realizations varied approximately between 0.6 and 1.7 s.  
The dynamics of consumption signals in the time domain was studied by using three of the methods described in Section 3. The M-mode proposed in this work provided us with simultaneous graphical representation of the empirical _pdf_ for the energy consumption of the ten realizations performed for each node. This way, the similarity of the signals obtained between one and another realization was analyzed to validate the acquired data, discriminate the erroneous signals, and repeat the data collection process if necessary. The regions with the highest concentration of consumption measures for each node, as well as their variability and dispersion, were scrutinized with the histograms, so that an approximate view of the probability distributions was obtained. Finally, the graphs of the CI allowed analyzing the variability of the probability distributions, and, for this purpose, bootstrap techniques for each node yielded the CI for its estimated average, with 95% of confidence level. Each graph was studied in the linear and logarithmic scales, as shown in Figure A3, where the histograms represented with the second option can be visualized with better detail because transitions and tails are imperceptible in the linear coordinate axis. Then, it is more appropriate to analyze the histograms represented in logarithmic scale, since they expand the visual scope of the analysis. Figure A4 depicts the M-mode signals for the empirical _pdf_ of the ten realizations, and their CI obtained in each node. It can be seen that the similarity of the signals obtained in ten realizations for the Sensor Nodes was larger than for the Coordinator Node, since this was connected to the computer and an impedance mismatch was generated. This load effect was evidenced also in the time signals of the ten realizations, where the average of consumption decreased as the number of runs increased.