# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 2 Related Works  
In general, WSN-based localization systems are categorized into two main categories: range-based and range-free techniques, as presented in Figure 1. The former requires an additional positioning device to be employed with each reference node (sensor nodes with known positions) or the target node (a sensor node with an unknown position), for instance, adding infrared, ultrasonic, and GPS-based methods [16; 17], whereas the latter is based on the content of the transmitted message between the reference and the target nodes [18; 19]. Range-free systems can be further divided into hop-count and multidimensional scaling (MDS) localization approaches [20; 21; 22]. Hop-count systems are based on the average hop distance between the reference node and target node, whereas the MDS systems estimate the target node's position based on the basic information of the reference nodes in the communication range. Both the hop-count and MDS systems reduce the hardware requirements for inexpensive sensor nodes; however, range-free approaches are inefficient in terms of localization accuracy. On the other hand, range-based localization systems have received considerable attention recently due to their efficiency in indoor environments and can be categorized into triangulation and fingerprinting localization systems. The former estimates the target node's location through triangulating the distance estimated from at least three reference nodes, whereas the latter is based on the behavior of signal propagation and information around the geometry of the tracking area. The triangulation approaches can be categorized into received signal strength (RSS) and range-finder methods (such as infrared, ultrasonic, UWB, and RFID). In general, triangulation methods offer high localization accuracy in outdoor environments but fail in indoor environments, whereas fingerprinting achieves reasonable localization accuracy in indoor environments. Therefore, in this paper, we focus on employing a fingerprinting localization approach to track target nodes in the area of interest. Fingerprinting approaches are categorized further into traditional approaches and artificial intelligence (AI)-based approaches. Both the traditional and AI-based fingerprinting localization approaches consist of two phases: the offline phase and the online phase. The main difference between the two approaches is the process of dealing with the fingerprints (RSS values with the corresponding 2D coordinates). The traditional approaches estimate the target node's position based on the nearest reference points collected in the offline phase, whereas the latter employed ML and DL approaches to train and estimate the target node's position. Several WSN-based fingerprinting localization systems have been developed with various localization accuracy and efficiency [23; 24]. However, these approaches offer unreliable localization information in complicated environments. Therefore, the AI-based approaches are considered in this paper. This section discusses the recently developed AI-based localization approaches that employed artificial neural networks and ML algorithms in the localization phase. The authors of [25] proposed a hybrid target tracking system using the ML and Kalman filter to estimate the continuous location of the mobile target object. A fingerprint system involves the collection of RSS values from the tracking area, and then the authors employed an ML model for training purposes using the collected RSS dataset. Afterwards, the Kalman filter was employed to combine the predictions of the target's location based on the acceleration information with the first estimates. The work presented in [26] involves developing a device-free wireless localization system using an artificial neural network. The developed system consists of two phases: the collection of the RSS values from the distributed reference nodes and the employment  
Figure 1: The categorization of WSN-based localization systems. of an ANN model to be trained using the collected RSS values.