## 2 State of the Art
In [14], the same authors of this document present the results obtained when representing the losses in a digital video transmission by means of ARIMA and SARIMA models, following the Box-Jenkins methodology and making use of the R programming language to estimate the coefficients. [PERSON] and [PERSON] in [15] present the results of their research by performing network traffic analysis and forecasting using SARIMA, NARX (Nonlinear Autoregressive Exogenous), and BPNN (back propagation neural network) models based on a short-term time series data set. The determination of these prediction models offers an alternative for researchers to obtain more accurate prediction results in the analysis of traffic in data networks. In the article titled \"Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas\" [16], its authors show how the use of the Internet of Things (IoT) generates a new dimension in smart agriculture. Likewise, it is shown that it is possible to use fog computing and a long-range WiFi network to connect agricultural bases located in rural areas in an efficient way. [PERSON] and others, in their study \"A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0\" [17], present in detail a hierarchical, layered structure according to which all the functional elements of the system are adapted to the context, while the context awareness operation is achieved in a cloud-based distributed middleware component that is the core of the entire decision support system (DSS). This document shows a dynamic integration of precision farming (PF) systems on the Internet of Things (IoT) applied in agricultural environments through wireless sensor and actuator networks (WSAN). The paper presented by [PERSON] [18] documents a research result where a WSN based on IoT was used to observe the condition of yields and automate the precision of agriculture using various sensors seeking to strengthen agricultural production with improvement of production yields through intelligent decisions and obtaining information on crops, plants, measurement of temperature, humidity, and irrigation systems. The document \"Design and Development of IoT-Based Framework for Indian Aquaculture\" [19] presents the design and development of an Internet of Things (IoT)-based framework that measures water quality in aquaculture farms and provides alerts to farmers so that they can take the necessary precautions and save themselves from great losses in India. Finally, [PERSON], in his paper [20], shows an application of the Internet of Things and machine learning algorithms such as decision tree and time series analysis to forecast the weather more accurately over a long period of time. Other writings that address a problem similar to the issue raised in this article but with less impact are \"Autonomous Crop Irrigation System using Artificial Intelligence\" [20], A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming [21], and \"Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna\" [22]. Some documents were also considered for the construction of the state of the art, which, if true, are not mentioned directly in this writing but were relevant to various phases of the project described here. For this reason, they are included in the references of this article.