## 2 Related Work  
One of the most used solutions to predict intermediate points is the LI method [20]. This method builds a continuous function from data discs connecting two interpolated points [21]. [PERSON] et al. (2020) [22] states that LI is a method that has several variations and is used to solve problems in many areas such as computer vision [23], digital photography [24], computer graphics [25], and image calibration [26]. In this article, a method is presented that improves the performance of LI; in addition, a method to evaluate the quality of the interpolator is described [22]. In 1960, [PERSON] developed a recursive solution to the linear filtering problem of discrete data [27]. His method became quite famous and was used in several applications [28; 29; 30]. With this popularization, the algorithm came to be known as the KF in honor of its creator. [PERSON] et al. (2018) [31] uses the KF combined with an ML model, Optimized Support Vector Machine (O-SVM), in order to correct coordinates in a sample of collected data. As the acquired data was noisy, before being sent to a server, the KF was applied for pre-processing to smooth it out. In a second step, on the server, the O-SVM model trained on this clean data was used to correct it. Finally, this method was compared with others that were already known: CoreLocation Framework, Open ALTeacon Standard, Linear Regression, and Non-Linear Regression. What was obtained was an average error lower than these other methods. [PERSON] et al. (2018) [32] performs the same initial procedure, uses the KF to smooth the data coming from BLE trackers and then the Back Propagation Neural Network optimized by Particle Swarm Optimization is applied, thus repositioning the coordinate that will be sent to the server. [PERSON] et al. (2018) [33] developed a method based on reinforcement learning to fill in missing coordinates in animal paths in nature. In theory, the GPS should register them every minute, but for various reasons, this did not happen. So a reward space was built based on the environmental preferences of the studied species, a seabird. In this method, in order to make the prediction smoother, the paths made between preference points are straight, because these birds can take indirect paths. [PERSON] et al. (2020) [34] developed a method by using Convolutional Neural Network in seismic data to reconstruct missing parts, whether regular or irregular. The presented result surpassed the method based on rank reduction [35]. Similarly, [PERSON] et al. (2019) [36] also did work related to data reconstruction, but in time series. River water flow data from 1970 to 2016 were used and 1586 data reconstructions were performed by using MissForest, an ML algorithm. As a result, the algorithm presented satisfactory results (\(R^{2}>0.6\)), being much better than the linear regression model in all analyses performed. [PERSON] et al. (2018) [37] developed a model to classify types of indoor environments by using IoT devices, such as a laboratory, narrow corridor, lobby and a more open area. In this work, Decision Trees, Support Vector Machine and k-NN algorithms were used, together with Channel Transfer Function (CTF) and Frequency Coherence Function (FCF). They concluded that the combination that presented the best result was with k-NN, using CTF and FCF, resulting in an accuracy of 99.3% and prediction time below 10 us. Table 1 presents the contributions of the main articles cited in this section. The main contribution of our article is to solve the problem of network overload caused by numerous IoT devices trying to access it simultaneously. By predicting server-side paths using machine learning, it could be possible to increase the latency of sending data on the client side, reducing the load of data traveling over the network. We also compare the accuracies of a LI, some ensemble models and an Artificial Neural Network (ANN) model [38; 39]. This article represents an evolution of a recent research published in Portuguese [40].