## 2 Related Works
However, CSI-based approaches need extra hardware modifications in the mobile nodes in order to generate the data, and this makes them expensive compared to RSSI-based methods that do not require additional hardware. [PERSON] et al. [20] presented a WiDeep, a deep learning-based indoor localization system that achieves a fine-grained and robust accuracy in the presence of noise. It associates a stacked denoising auto encoder deep learning model with a probabilistic framework to reduce noise in WiFi fingerprints and capture the complex relationship between WiFi AP signals that run on the Android device and their locations. WiDeep employed regularization to avoid overfitting in deep learning models. WiDeep was evaluated with two separate test beds, a large university building and a small domestic apartment with different WiFi APs. The model achieved mean localization accuracy of 2.64 m and 1.21 m for the larger and the smaller test beds, respectively. [PERSON] et al. [21] created a sensor fusion framework which combines WiFi, Pedestrian Dead Reckoning (PDR), and landmarks. The data obtained by all these sensors were fused using the Kalman filter for position estimation. The performance of the system was evaluated by comparing the proposed fusion approach with individual approaches of WiFi weighted path loss (WPL) and PDR with landmarks. The proposed approach produced an average localization error of 1 m, which is an interesting performance. [PERSON] et al. [22] proposed an indoor localization of vehicles that uses a CNN with deep learning to identify and localize vehicles in a car-park. Based on the study, a vehicle can localize itself with landmark-based positioning and internal car sensors, but is costly and requires precise knowledge of the enclosed area. The approach used off-the-shelf cameras mounted in the car-park and vehicle-to-infrastructure communication to enable all vehicles to obtain an indoor position. In this work, a pre-trained Google Net model was applied for object class classification of the images obtained from a public dataset known as ImageNet and other small images captured by cameras in their car garage. The evaluation of system was done on an eValet underground car garage equipped with network cameras monitoring several lanes. The approach generated an accuracy of 65.69% on the test data. [PERSON] et al. [23] developed DeepFi, a novel deep-learning-based indoor fingerprinting system using CSI. The experiment was conducted using one fixed AP and a mobile terminal. The mobile device receives transmitted wireless signals from the access point and stores the raw CSI values in its firmware. The CSI values from the firmware were read and assigned weights. The study proposed using the weights in a deep network to represent fingerprints and apply a greedy learning algorithm for weight training in order to reduce complexity. DeepFi achieved good accuracy; nevertheless, capturing fingerprints using CSI is not cost effective and requires a powerful machine to perform localization tasks. [PERSON] et al. [24] proposed an indoor localization system using the affinity propagation clustering algorithm and the particle swarm optimization-based artificial neural network (PSO-ANN). Both algorithms were used on WiFi-based RSS data. The clustering technique was implemented with the purpose of reducing the location error. The PCA algorithm was used to reduce the dimensions of RSS fingerprint data. Additionally, the PCA was used with a purpose of reducing the computational complexity during the ANN training process. The affinity propagation clustering was also performed in an unsupervised approach in order to reduce the computational complexity. The proposed algorithm produced an error of 2.971 m.  
[PERSON] et al. [5] proposed a sigma-point Kalman smoother (SPKS)-based location and tracking algorithm as a superior alternative for indoor positioning. In their experimentation, different low cost sensors were used to capture the indoor environment data, including RSSI, infrared motion sensor data, and binary foot switches.