Fingerprint indoor positioning based on user orientations and minimum computation time

Abstract

Indoor Positioning System (IPS) has an important role in the field of Internet of Thing. IPS works based on many existing radio frequency technologies. One of the most popular methods is WLAN Fingerprint because this technology has been installed widely inside buildings and it provides a high level of accuracy. The performance is affected by people who hold mobile devices (user) and also people around the users. This research aimed to minimize the computation time of kNN searching process. The results showed that when the value of k in kNN was greater, the computation time increased, especially when using Cityblock and Minkowski distance function. The smallest average computation time was 2.14 ms, when using Cityblock. Then the computational time for Euclidean and Chebychev were relatively stable, i.e. 2.2 ms and 2.23 ms, respectively.