## 2 Related Work
The results with real RSS values measurement show that the modified FOS performance is better than the traditional FOS implementation. [PERSON] et al. [25] presented a novel algorithm, which is called a clustering and Decision Tree-based method (CaDet) for intelligent selection of APs in indoor positioning. CaDet is a union of information theory, clustering analysis, and a decision tree algorithm. By using machine-learning techniques, the results showed that CaDet is capable of selecting an informative small combination of APs to detect the user's location with high accuracy, as compared with traditional techniques. [PERSON] et al. [26] proposed a novel technique for indoor localization based on the cooperation between IEEE802.11 infrastructure and Bluetooth Low Energy (BLE). BLE is used to confine the location of the user within a specific subarea that means coarse localization. A new K-Nearest Neighbors (K-NN) algorithm is utilized to estimate the final user's location. Due to the use of a fragment of the initial fingerprint dataset, the results exhibit that the proposed system achieves fast positioning estimation and improves the accuracy of the proposed system as well. [PERSON] et al. [27] implemented a novel approach based on compressive sensing (CS) theory and selection APs to obtain high accuracy. The theory of CS is applicable for accurate IPS using RSS-based fingerprinting due to the location sparse nature on the map. There are two stages for finding the location of a user. The first stage is achieved by an Affinity propagation clustering method to reduce the computational cost and increase accuracy as well. In the second stage, fine positioning is applied by using the compressive sensing theory. Random and strongest APs selection were used in this article. The results show that the proposed system leads to robust enhancement in positioning accuracy and complexity over traditional fingerprinting techniques. [PERSON] et al. [28] proposed an indoor localization methodology based on data fusion and feature transformation. The methodology is formed by four phases: dataset building, feature fusion, feature transformation, and classification. Dataset consists of RSS from Wi-Fi AP, orientation from a compass, and simulated RSS from simulated Light Emitting Diode (LED) lamps. Principal Component is used to reduce dataset dimensionality and improve computational performance of system. The results exhibit that the proposed system considerably reduces overall computational cost and provides an acceptable location accuracy. [PERSON] et al. [29] proposed a refinement cycle for an indoor positioning system based on dimensionality reduction techniques. Several dimensionality reduction techniques have been utilized to different datasets such as Principal Component Analysis (PCA), Linear Discrimination Analysis (LDA), and t-Stochastic Neighbor Embedding (t-SNE). Two different visualization methods are defined to obtain graphical information about the quality of the radio map in terms of overlapping areas and outliers. The proposed system is evaluated by two kinds of data: dataset, which is measured by the research group, and standard data. In general, the results show that there are some useful configurations which can be used to perform the refinement process.