# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
###### Abstract  
Over the past decade, the demand and research for indoor localization have burgeoned and Wi-Fi fingerprinting approach has been widely considered because it is cheap and accessible. However, most existing methods lack in terms of positioning accuracy and high computational complexity. To cope with these issues, we formulate a two-stage, coarse and accurate positioning narrow-down approach (NDA). Furthermore, a three-step source domain refinement (SDR) scheme that involves outlier removal, stable AP's weight enhancement, and a data averaging technique by applying the K-means clustering algorithm is also proposed. The collaboration of SDR scheme with the training data selection, area division, and overlapping schemes reduces the computational complexity and improves coarse positioning accuracy. The effect of the proposed SDR scheme on the performance of the support vector machine (SVM) and random forest algorithms is also presented. In the final/accurate positioning phase, a set of lightweight neural networks (DNNs), trained on different sub-areas, predict the user's location. This approach significantly increases positioning accuracy while reducing the online computational complexity at the same time. The experimental results show that the proposed approach outperforms the best solutions presented in the literature. narrow-down approach (NDA); area division and overlapping (ADO); source domain refinement (SDR); support vector machine (SVM); random forest (RF); distributed neural networks for indoor localization (DNLoc); Wi-Fi fingerprint; Internet of Things (IoT) +
Footnote †: journal: _Algorithms_  
accurately. However, the dynamics of routers, random signal interference, and moving obstacles cause uncertainty in measuring the wireless signal strength at reference points (RPs), which in turn degrades the accuracy of indoor positioning. Mostly machine learning (ML)-based algorithms, like K-nearest neighbor (KNN) [6], naive Bayesian (NB) [7], support vector machine (SVM) [8], random forest (RF) [9], and deep neural network (NN) [10], have been used to find the user's indoor location from the fingerprints. Indoor localization methods can be divided into two categories [11]. (1) Classification methods: The whole localization area is divided into sub-areas and then a classification algorithm finds the sub-area where the target resides. (2) Regression methods: A regression-based algorithm finds the user's exact location by utilizing Wi-Fi-based RSS (received signal strength) vectors. In this article, we aim to reduce the training and response time while increasing positioning accuracy. We propose a narrow-down approach that consists of coarse and accurate positioning phases. To deal with the first problem, in the coarse positioning phase, we propose a source domain refinement (SDR) scheme that reduces 80% of training data for classification. Furthermore, we also divide the whole localization area into sub-areas, as proposed by [PERSON] in [12]. To reduce the response time, we select a support vector machine whose response time is far better than the random forest algorithm for classification. We reduce the propagation delay of the regression-based algorithm by making it lightweight and by training it on each sub-area. The combination of SVM, SDR, and the group of distributed neural networks, namely DNLoc, alongside the concept of area division and localization, dramatically increases indoor localization accuracy. The main contributions of this paper are:  
* This study proposes a narrow-down approach (NDA), which comprises the coarse and accurate positioning phases. * The contribution is to select specific reference points (RPs) to train the classification algorithm, while the key considerations are to reduce the offline storage as we do not use all the RPs for training, and the chosen training points for the classifier are distant enough to share minimum RSSI characteristics. This strategy increases classification accuracy.