# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 1 Introduction
This paper investigates how to use supervised classifiers for indoor localization, where locations are estimated as categorical data with improved RSSI linearization. We introduced a novel linearization and filtering mechanism with ML framework to improve the localization accuracy in robust indoor localization systems. For this experiment, we used a publicly available testbed and its dataset [17]. In this case, we consider the location estimation as a classification-type ML problem, and where the specific location (zone) of an existing person will be predicted. This testbed consists of 13 BLE beacon nodes implemented inside a library. In addition, high security, a low cost, and minimal power consumption are all benefits of Bluetooth positioning technology; yet, Bluetooth has poor signal reliability and a limited communication range. More details on the dataset and testbed will be discussed later in this paper. We used the RSSI dataset for pre-processing using a moving average filter and weighted least-squares method. Afterward, the dataset was trained by the supervised classifiers FFNN, SVM, and kNN, and we evaluated each algorithm's performance on localization. However, this study was limited to one particular BLE dataset, and other wireless technologies were not considered. This paper is organized as follows: firstly, the background theory of wireless indoor localization will be discussed in the background section. Secondly, related works in ML for indoor localization and the mathematical model will be discussed. Thirdly, our novel system model is proposed. In the fourth section, we elaborate on the steps of data pre-processing and the ML-model development and training. The fifth section discusses the model evaluation in detail, including the hyperparameter tuning for each model. Finally, we conclude the findings. Wireless indoor localization is a technique that estimates the objects' location by using distance measurements. Primarily, indoor localization systems can be categorized as device-free and device-based localization. Typically, these location localization techniques require distance measurements as input for the algorithm. The widely used distance measurement techniques in positioning systems are the Angle of Arrival (AoA), Time of Arrival (ToA), Time of Flight (ToF), Time Difference of Arrival (TDoA), Channel State Information (CSI), and Received Signal Strength Indicator (RSSI). When comparing the above distance measurement techniques, there are pros and cons. For instance, the AoA system uses a set of antennae to determine the signal propagation angle, since more hardware is needed and implementation cost is high. The AoA technology typically requires complex hardware such as antenna arrays calibrated to get the correct position. The RSSI level correlates the distance between the transmitter and the receiver. Additionally, the signal intensity weakens as it goes away from the transmitter; it is frequently used to calculate the separation between a transmitter and the receiver of the nodes. Because the transmitting signal is vulnerable to environmental noise and multipath fading, RSSIs generally lead to inaccurate values and errors in localization systems; therefore, strong signal processing techniques should apply before it is processed through ML algorithms. Once it has a distance measure, it can feed as the input for the algorithm. Before being fed into the algorithms, it requires strong signal filtering. Specific algorithms require a bare minimum number of anchor nodes. For instance, trilateration requires at least three anchor nodes in the system [17, 18, 19].