# Multiparametric Monitoring in Equatorial Tomato Greenhouses (II): Energy Consumption Dynamics
## 5 Discussion and Conclusions
The power consumption processes of all ZigBee network nodes were seasonal for the medium and high speeds, while for the minimum speed the seasonality was lost from approximately 200 milliseconds of delay. Based on this background, we conclude that the ZigBee network at medium and high transmission rates was the most recommendable for purposes of energy consumption modeling of greenhouse monitoring networks. The relevant contributions of the energy consumption study developed for the sensor and coordinator nodes of three WSNs with different communication technologies, topologies, and transmission rate are described next* The usual patterns and duration times of each transmission were determined by locating the periodic spikes in the time series of the electrical current measurements. * A detailed statistical description of the consumption dynamics and ergodic properties using histograms, the estimated _pdf_, and their CIs was elucidated. * The nodes where consumption prediction is feasible by means of seasonality study (estimated SAF and CIs), as well as the differentiation of the nodes affected by the type of topology or speed change, were identified. To consider additional aspects such as the full charge of the battery during the experimental phase, a particular case of this study was given by the sensor nodes that measure CO\({}_{2}\), as well as the Coordinating Nodes of the ZigBee and DigiMesh networks. * Recommendation of the most suitable network in terms of energy saving for use in greenhouse monitoring systems are given. In summary, this work provides engineers and professionals in the field with a set of guidelines that can be taken into account for the design and deployment of greenhouse monitoring networks, to choose the most appropriate technology and topology, as well as the ideal transmission rate in terms of energy consumption, without affecting the quality of the monitored data. This research can be further expanded through the application of statistical learning and machine learning techniques to predict the trend of battery discharge, depending on wireless technology, network topology, transmission rate, link distances, and types of installed nodes. [PERSON] designed and implemented sensor networks, conducted the experiments, organized and wrote the paper, and performed the analysis of experimental results. [PERSON] contributed to writing and reviewing some sections of the manuscript, and analyzed some experimental charts. [PERSON] supported the data processing in Matlab\({}^{\text{TM}}\). [PERSON] and [PERSON] reviewed some sections of the document. [PERSON] was involved in data processing, and helped write and review the paper. This research received no external funding. This work was partly supported by Research Grants PRINCIPIAS and FINALE (TEC2013-48439-C4-1-R and TEC2016-75161-C2-1-R) from Spanish Government; Research Grant PRICAM (S2013/ICE-2933) from Comunidad de Madrid, supported in part by the Universidad de las Fuerzas Armadas ESPE under Research Grant Energy Efficiency in Wireless Sensor Networks (2016-PIC-043), and 2015-PIT- 004; and the Thematic Network: RiegoNets (CYTED project 514 RT0486), and PLATANO project from Universidad Politecnica Salesiana, Cuenca, Ecuador. The authors are grateful to [PERSON]. The authors declare no conflict of interest.