ABSTRACT
The demand for using location-based services (LBSs) is rapidly increased, specifically in the last decade. Most people’s daily activities are related to LBS services, including navigation, billing address, tracking stuff, transportation, and other point-of-interest (POI). In the same manner, many solutions are widely available to process the positioning from outdoors to indoors. One of the most utilized positioning solutions is using fingerprinting-based techniques via different technologies, including WiFi, Bluetooth, 3G/4G, and UWB. Many attempts have been made to enhance fingerprinting-based positioning and then to provide an accurate solution. The recent attempts are referred to use modern machine learning algorithms as fingerprinting matching process. However, there is no single solution to provide an accurate, low-cost, on-the-go, and seamless positioning solution. Therefore, this article aims to address the issues of using fingerprinting-based positioning. A new taxonomy for the recent solutions, which are related to fingerprinting-based techniques, is also designed. Accordingly, machine learning algorithms which have been used in fingerprinting-based technique and their challenges are investigated.
