Wi-Fi Based Accurate Indoor Localization System using SVM and LSTM Algorithms
Haidar Abdulrahman Abbas, Najmadin Boskany, Kayhan Zrar Ghafoor, Danda B. Rawat
Abstract
Indoor localization of mobile nodes is emerging as a promising field in the past decades due to the advances in mobile devices and the increasing number of wireless services and applications. Wi-Fi fingerprints which is a vector of Received Signal Strength (RSS) values in a specific location are considered a good feature for indoor localization due to their simplicity. However, the existing methods such as decision tree (DT) and Naïve Bayes (NB) techniques have some drawbacks. DT has low efficiency because it uses the Boolean function, and NB provides a low accuracy of prediction for the sparse dataset. In this research, fingerprint referencing with two important methods, Support Vector Machine (SVM) and Long-Short Term Memory (LSTM) machine learning algorithms, are used to enable accurate indoor localization. Furthermore, data normalization is implemented to reduce positioning error by increasing the inconsistency of the RSS values. Further, several experiments have been conducted by using three mobile devices in the area of 280 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to create our dataset by taking 480 RSS records for both training and testing phases. The results show that the accuracy LSTM algorithm is 0.9 m, whereas the SVM algorithm accuracy is 1.1 m. Both experimental results show better accuracy for indoor environments can be achieved by the proposed approach than results recorded by several prior studies.