###### Abstract  
Currently, there are billions of connected devices, and the Internet of Things (IoT) has boosted these numbers. In the case of private networks, a few hundred devices connected can cause instability and even data loss in communication. In this article, we propose a machine learning-based modeling to solve network overload caused by continuous monitoring of the trajectories of several devices tracked indoors. The proposed modeling was evaluated with over a hundred thousand of coordinate locations of objects tracked in three synthetic environments and one real environment. It has been shown that it is possible to solve the network overload problem by increasing the latency in sending data and predicting intermediate coordinates of the trajectories on the server-side with ensemble models, such as Random Forest, and using Artificial Neural Networks without relevant data loss. It has also been shown that it is possible to predict at least thirty intermediate coordinates of the trajectories of objects tracked with \(R^{2}\) greater than 0.8.  
network overload; Internet of Things; machine learning +
Footnote †: journal: Sensor and Actuator Networks  
In the literature, it is possible to find numerous articles that aim to interpolate/predict coordinates to discover points on a path [13; 14; 15; 16]. These articles focus on predicting trajectories in open environments, with typically tracked objects being vehicles, which can lose a global positioning system (GPS) signal in tunnels or other areas that restrict satellite communication. Among the most used techniques, we can mention the use of interpolation [17], assuming midpoints or also applications of the Kalman Filter (KF) in conjunction with Constant Turn Rate and Acceleration models [18] as trajectory estimators. The effectiveness of these techniques is especially worth considering because, in the context of predicting trajectories in open environments, there is only the need to predict trajectories at a macro level. For example, it is not a problem if the interpolated path passes over a corner; it is more important to know which streets the vehicle passed through.  
Despite the effectiveness of these techniques, in closed environments the demand for understanding micro movement patterns is greater. In the specific context of a supermarket, for example, using linear interpolation (LI) would imply predicted paths crossing aisles, which could confuse the analysis of subsequent trajectories of the tracked objects/people. In this context, the answer to the following questions would not be accurate: Which sections or products did a particular employee, who wears a traceable wristband, pass through? How many square meters were cleaned with a tracked vacuum cleaner?  
In this article, we propose a machine learning (ML) model for the problem of interpolating coordinates of tracked objects indoors. We use real data collected by researchers at the University of Guelph [19]. To allow for a more complete evaluation of the proposed modeling, synthetic data were generated for three different environments. We aim to answer the following research questions:  
* RQ1--Would the modeling used be able to predict routes that avoid obstacles in closed environments?
* RQ2--How much can latency be increased without loss of performance from ML algorithms?
* RQ3--What is the impact of the amount of data on the performance of ML algorithms?  
This article is organized as follows: Section 2 will present some related works and how this article intends to contribute to the theme. Section 3 will show how the synthetic environments were built and how the data generation process took place. In Section 4 the modeling used for the ML models will be presented in detail. In Section 5, the results of this research will be presented and the research questions will be answered. In Section 6, final considerations will be made, considering the results achieved.