# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
## Appendix B
Namely, if the battery voltage decreases, the similarity between the signals reduces, and for this reason it is recommended that the battery is charged to its maximum capacity in each realization. The widest CI occurs in the Coordinator Node, followed by Node 4. Based on this, it was established that the behavior of the consumption of these nodes was not repetitive, and that it strongly depends on external factors. In the case of the Coordinator Node, the consumption was conditioned by the battery voltage and the severity of the mismatch with the load, whereas at Node 4 it is a function of the response time of the CO\({}_{2}\) sensor. The CI of Nodes 1, 2, and 3 were narrower, which suggests that the consumption process in each transmission is similar and independent of the battery charge status. The empirical _pdf_ of elements in the DigiMesh network was multimodal and biased to the left. The Coordinator Node is the network manager that interacts with all nodes. Based on this, and considering that the energy consumption of the transmission sequences is different for each sensor node, we verified that the distribution was quite spaced and with a greater number of modes than the rest of nodes. For sensor nodes, both the number and range of modes depend on the type and number of installed sensors. The seasonality of consumption signals was analyzed using the SAF for 500 delays of one millisecond. By means of the representation of the SAF in time series, we can observe the correlation of all nodes, and for each realization. Figure A5 shows some signals on a linear scale that we selected as representative. In general terms, the SAF of the Coordinator Node falls slowly and exponentially, while, in the sensor nodes, the falls are fast, with persistence for Nodes 1, 3, and 4, and anti-persistence in Node 2. With respect to the average of the correlation coefficient, in the Coordinator Node and in Sensor Nodes 1, and 4, it varies significantly in some realization, because of the load effect already explained. In the rest of the nodes, the average was about the same in all the realizations and its value was nearby zero for larger delays, hence we deduce that the relation between a signal and its out-phase is minimal. The SAFs in Figure A6 show additional graphical representations of the energy consumption dynamics for each node at a linear scale. For each realization, the behavior was studied in terms of its falling smoothness and of his persistence with increasing delay. The M-mode was also used for this case, allowing us to simultaneously represent the SAF of the ten runs at each node. The bootstrap replication of the averaged autocorrelation per node was obtained with a 95% confidence level. This analysis revealed that the Coordinator Node has slow exponential-like and anti-persistent drop, indicating its absence of seasonality. On the other hand, Nodes 1 and 3 were fast-dropping and persistent, i.e., they were processes that follow seasonal behavior, whereas Nodes 2 and 4 were uncorrelated processes. Although their fall was slow and persistent in the initial delays, the repetitiveness was lost for larger delays. Similar to what happened in the _pdf_ analysis, the coordinator and Node 4 presented wider CI of the FAS, for already explained reasons.