Modified WiFi-RSS Fingerprint Technique to locate Indoors-Smartphones: FENG building at Koya University as a case study

Abstract

Positioning system used for different purposes and different services, many researches are going on to find a more accurate position with low error within high performance. There are many localization solutions with different architectures, configurations, accuracies and reliabilities for both outdoors and indoors. For example, Global Navigation Satellite System (GNSS) technology has been used for outdoors. Global Positioning System (GPS) is one of the most common outdoors tracking solutions in the world, for outdoors, however, when indoors; it could not be accurately tracked users by using a GPS system. This is because, when users enters into indoors the GPS signals will no longer available due to blocked by the roof of buildings and it is no longer considered as a viable option. WiFi Positioning System (WPS) can be used as an alternative solution to define users' position, especially when GPS signal is not available. Further, WPS is a low cost solution, because there is no need to deploying WiFi Access Points (WAPs) in the vicinity, as they are installed to access the Internet. In this paper, specifically, WiFi-RSS Fingerprinting technique is used to locate smartphones using WAPs signals with a modified calculation. The new modified calculation is to dynamic weighting of the WAPs RSS values based on the real-live indoors structure. The achieved positioning accuracy, based on several trial experiments, is up to 6 meters via the implemented algorithm in the MALTAB.

Developing A Conceptual Passive Contact Tracing System for Commercial Buildings Using WiFi Indoor Positioning

“…Subsequently, in the online phase, the smart device gathers the RSS from the APs and sends it to the server to compare the predefined fingerprint of the offline phase with the RSS data in the online phase in order to estimate the location on the grid map [27]. Different machine learning algorithms are suggested and used in order to compare offline and online data, such as k-nearest neighbors (KNNs) [28], weighted KNN [29], neural network [30], recurrent neural network (RNN) [31], and Naïve Bayes [32], among others. The positioning algorithm and the quality of observations can impact the performance of these positioning techniques.…”