## 2 Related Works  
This section mainly focuses on related works on deep learning approaches used for localization and feature extraction techniques particularly in indoor environments. The aim of this section is to point out the performances of different studies to aid analysis and validation of the proposed algorithm in this study. In [15], a localization model was proposed by combining CNN with Gaussian process regression (GPR) using Wi-Fi RSSI data. RSSI fingerprints were captured from several access points in order to generate a CNN training dataset which was used for feature extraction after the reprocessing stage. In order to improve the performance of the model, the GPR algorithm was applied for coordinate adjustment of the target node. However, this approach exhibits a high computational complexity in order to achieve better prediction accuracy. Although in this study we also used CNNs in the experiments, our computational complexity in terms of training time was low, which is attributed to the proposed CWT feature sets. Our real-time positioning time complexity was low when using the trained deep learning model. [PERSON] et al. [10] developed a novel hybrid location image based on sensor data series from both WiFi and magnetic field data. CNN was applied to classify the location of images for a defined reference point. Two steps for learning were adopted in order to acquire patterns of different networks. Additionally, these steps are: first, an early-stop method for preventing overfitting caused by small datasets for WiFi fingerprint features; secondly, long-term training of stable magnetic field for learning the different image features. This proposed method achieved a localization error of 1 m considering different smartphone orientations, users, and even use patterns. [PERSON] et al. [16] proposed a top-down searching method using a deep reinforcement learning agent to handle dynamics in indoor environments using WiFi fingerprints. The model uses a hierarchical search strategy that begins from the whole area towards the target node. This method does not require prior knowledge of the floor plan. For a floor size of 2500 square meters, 75% accuracy was reported. [PERSON] et al. [17] developed a localization system that transfers the online prediction complexity to an offline preprocessing step using a convolutional neural network. The fingerprints were generated based on 3D radio images formulated using RSSI data, and CNNs were used for target training and prediction. Using finger print data, Kurtosis values were calculated and used to provide statistical parameters that distribute global information to the local filters. However, [PERSON] et al. [17], considered localization accuracy and computational complexity as a trade-off, yet scalability and energy-efficiency are sometimes key metrics towards high accuracy of the target. [PERSON] et al. [18] presented a CNN-based approach for indoor localization using RSSI time-series from WiFi access points. They demonstrated the approach on the UJIndoorLoc dataset considering multi-building and multi-floor data and obtained 100% accuracy on both building and floor prediction using a mobile node. Four feature set formulations were implemented, namely, single RSS vector, averaged RSS time-series, concatenatedRSSI time-series, and RSSI time-series images. Feedforward DNNs and CNN were used in the prediction of the building floor and specific node location coordinates (longitude and latitude). The study reported that the RSSI time-series image dataset formulation yields an better result. However, it requires huge datasets, which is expensive in terms of manpower and time for data collection. [PERSON] et al. [19] generated a deep learning-based approach for indoor localization through combining received signal strength (RSS) and channel state information (CSI).