## 2 State of the Art  
In the current scientific literature, several studies were found that showed the results of the use of WSN and IoT devices applied to agriculture and environmental management. However, the analysis of the information was carried out with techniques different from the case study presented in this document. The study and prediction of the behavior of data traffic variables using ARIMA- and SARIMA-type time series were carried out by the same authors and scientists from other universities, oriented toward other technological applications. Among the most relevant writings that address similar topics that allow a construction of the state of the art, the following can be cited:  
In [9], a solution based on an intelligent irrigation system is proposed that takes into account water requirements, soil moisture levels, environmental conditions, and air humidity level and allows the optimization of water use with minimal human intervention as a smart city app. In [10], it is shown how the use of sensors and the Internet of Things (IoT) is key to taking global agriculture to a more productive and sustainable path. It also presents the opportunities that the agriculture and environment sectors have with the use of recent advances in IoT, WSN sensor networks, information, and communication technologies. As the number of interconnected devices continues to grow, this generates more high-volume data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to develop a higher level of knowledge base and insights that result in better decision making, forecasting, and reliable sensor management [11]. This document shows a comprehensive review of the application of different machine learning algorithms in the analysis of sensor data within the agricultural ecosystem. In addition, it analyzes a case study on an IoT-based, data-driven smart farm prototype as an integrated food, energy, and water (FEW) system. [PERSON], [PERSON], and others in [12] write about the challenges to guarantee food for all the inhabitants of the planet. To this end, they propose an alternative that consists of increasing agricultural production using the IoT to improve the capacity of the soil and the protection of environmental resources. This article presents a model called AgriPrediction, which combines a short- and medium-range wireless network with a technological system that anticipates possible crop failures and notifies the farmer to make decisions and corrective measures. To achieve this, AgriPrediction presents a framework whose components are based on both LoRa (Long Range) IoT technology and the ARIMA-type time series prediction model. Their results demonstrated the feasibility of using LoRa in rural locations, as well as providing the advantages of having a prediction system to observe problems related to soil moisture and temperature. All the tests in this study were carried out on an arguula crop where an improvement in productivity was obtained close to 18% compared to the crop that used traditional methods. In the article by [PERSON] and [PERSON] [13], the authors propose the implementation of a NGIF (next-generation integrated farming) using LoRa-IoT to improve productivity, produce better crops, and minimize labor through adequate monitoring of livestock health, soil health, air temperature, humidity, proper watering at the right time, and protecting crops from birds and animals. The simulation result shows that a hybrid Wi-Fi and Lora WAN network can support different IoT connectivity technologies, reduce complexity, and minimize delays in decision making to improve productivity with lower costs in a rural area. All this is thanks to the application of integrated agriculture or smart agriculture.