## 1 Introduction
In RSS-based indoor positioning, objects are localized by the strength of the signal at the receiving target node [18]. In our work, we choose RSS for position estimation as it is the simplest method and does not require additional hardware for synchronization. The reason for the wide popularity of RSS is that almost all radio-enabled devices can process and display RSS. When it comes to indoor positioning, range-based techniques have a drawback: they are badly affected by multipath fading and shadowing in cluttered indoor environments. Due to these factors, variability in the values can cause large estimation errors. In comparison, the range-free techniques do not consider these mathematical models and are not prone to error compared to range-based techniques. For these reasons, range-free techniques are preferred indoors, some examples of range-free positioning techniques are cell-identity-based, proximity-based, fingerprinting, etc. Fingerprinting is a range-free technique for localization, and it can be applied in all wireless communication systems. A fingerprint is a unique set of location-dependent signal parameters, so each location has a unique fingerprint associated with that location. It is the most reliable method for localization in RSS-based indoor positioning systems. Fingerprinting is comprised of two stage: offline training and online testing. In the offline training stage, the RSS values of the anchor nodes are recorded at each reference point (RP), a geographic coordinate in the coverage area. During the online testing stage, the RSS values at unknown coordinates are subjected to a robust classifier that estimates the position's coordinate based on the training data [19]. Although fingerprinting-based IPSs are considered reliable, they face some challenges because of variations in permittivity and permeability of materials in the signal propagation path, creating nonuniform propagation loss. Moreover, the multipath effect in indoor environments also threatens the signal strength at target node positions. Various positioning algorithms using a number of classification models have been adopted to estimate the precise 2D coordinates. These models categorise the observed training data and assign the test data to the best set. The most reliable classification models include the multi-class support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and the ensemble classifier. The classification accuracy of these classifiers depends on the RSS collected during the offline training stage. Moreover, the robustness and distinctiveness of the fingerprint data provide more accuracy in the IPS framework. The data collected during offline training, consisting of RSS from each anchor node, is deficient due to cost constraints on the APs in the finite geography. The limited fingerprint training data at each coordinate affects the performance of the overall IPS framework. To handle this issue, we adopt a pre-processing Bag-of-Features (BoF) strategy that improves the overall strength and robustness of the training data. In this paper, the proposed approach transforms the raw data into a high-dimensional form and makes it more compatible with the pre-existing classification models. The proposed approach formulates positioning as a pattern-recognition problem, where for each location a featured vector is obtained using a simplified BoF-based technique. It consists of characterizing several WLAN RSS measurements observed at each RP. The BoF model is applied to the raw RSS fingerprint data by accumulating k-mean clustering and collecting the differential vectorization of vocabulary set occurrences. This is the first time that such a technique has been used in WLAN RSS fingerprinting-based indoor localization. Previously, researchers employed pre-processing methods such as spectrogram transformation [20] and interpolation [21]. However, such methods extend the complexity of the model by increasing the data size.