# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 2 Related Works
In the tracking phase, a non-linear function between the RSS inputs and outputs can be approximated using the pretrained ANN model. In [27], the authors proposed a node localization system based on the Voronoi diagram and a support vector machine (SVM). The main idea is to divide the tracking region into several parts using the Voronoi diagram and anchor node in the localization region, and then the initial location of the target node is calculated through locating the region of the target node. Then, the SVM is employed to further optimize the position of the target node. The authors of [28] proposed a novel algorithm that involves a DL model and high-level extracted features using autoencoder to enhance the localization accuracy. The authors raised the number of training data to improve the localization accuracy. On the other hand, the work presented in [29] involves an indoor localization system based on an SVM model. The authors employed a multi-class SVM with RSS measurements to develop a zoning localization approach. The work presented was validated in two different areas (hospital and laboratory buildings). The obtained results were compared with the ANN and showed the effectiveness of the presented SVM model. In [30], the authors developed a WSN-based localization system using a cascaded layered recurrent neural network (L-RNN) for the classification of user localization in indoor environments. After considering several experiments by adopting different neural network models, the authors revealed that the experimental results showed that the implemented L-RNN model is an accurate localization method for indoor environments. The work presented in [31] investigates the employment of ML algorithms for network-wide localization in large WSNs. The authors adopted an SVM model with a radio basis function (RBF) kernel for training the learning algorithm using the training dataset. The authors of [32] proposed a location identification method using the ANN method and RSS signals for sensor networks. The authors revealed that the performance analysis demonstrated the effectiveness of the proposed ANN model to estimate the location of the target nodes. The authors of [33] proposed the employment of an outlier detection method for removing the effect of erroneous distance estimates in position estimating using the RSS method. In this work, the authors proposed three different localization schemes that apply the outlier detection to effectively minimize the localization errors in shadowed environments. In [34], the authors proposed a range-free localization algorithm based on neural network ensembles (LNNEs), where the target's location is estimated using LNNEs solely based on the connectivity information in the WSN. The authors compared the obtained results with the centroid and DV-Hop range-free localization systems, where the experimental results demonstrated the efficiency of the LNNE approach. As discussed above, various localization systems have been developed recently with different costs, accuracy, and experimental results. Table 1 presents a comparison among the existing AI-based positioning approaches for WSN, where the recently developed AI-based localization systems are discussed according to the following parameters:  
1. ML algorithm: localization data can be estimated using different types of ML algorithms. Therefore, it is important to determine the ML algorithm that has been employed in the offline and online phases. 2. Experiment testbed: in general, experiments can be either simulation or real-time experiments. Simulation experiments are efficient in ideal situations; however, they offer unreliable results in complex environments. Real-time experiments, on the other hand, are hard to implement; however, they offer reliable localization results. 3. Localization accuracy: localization systems need to be validated in order to assess the performance of the localization accuracy. Usually, localization accuracy is measured using the localization error in centimeters (cm) or meters (m).