Efficient Techniques and Algorithms for Improving Indoor Localization Precision on WLAN Networks Applications

Abstract

This paper proposes efficient techniques that allow the deploying of high precision location applications for indoor scenarios over Wireless Local Area Networks (WLAN). Firstly, we compare the use of radio frequency (RF) power levels and relative time delays based on ray-tracing as detection methods to estimate the localization of a set of mobile station using the fingerprint technique. Detection method play an important role in applications of high frequencies techniques for locations systems based on current and emerging standards such as Wi-Fi (802.11x) and Wi-Max (802.16x). The localization algorithm computes the Eucli- dean distance between the samples of signals received from each unknown position and each fingerprint stored in the database or radio-map obtained using the FASPRI simulation tool. Experimental results show that more precision can be obtained in the localization process by means of relative delay instead of RF power detection method. Secondly, the Euclidean distance has been compared with others similarity distance measures. Finally, an interpolation algorithm between the fingerprinting weighing based on the distances has been implemented in order to eliminate those fingerprints that do not contribute to the improvement in the accuracy. These techniques allow obtaining more precision in the localization of indoor mobile devices over WLAN networks

Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

“…There is a large number of technologies to develop Indoor Positioning Systems (IPSs): Radio-Frequency Identification (RFID) (Jin et al, 2006;Montaser & Moselhi, 2014;Calderoni et al, 2015), Bluetooth (Feldmann et al, 2003;Li, 2014), Wireless Local Area Network (WLAN or Wi-Fi) (Bahl & Padmanabhan, 2000;Lau & Chung, 2007;del Corte-Valiente et al, 2009;Gansemer et al, 2010a;Segou et al, 2010;Machaj et al, 2011;Marques et al, 2012;Chen et al, 2013;Lan & Shih, 2013;Le et al, 2014), ZigBee (Martí et al, 2012), Ultrasound (Ijaz et al, 2013), Magnetic field variations (Chung et al, 2011;Guo et al, 2014), and even LED light (Kuo et al, 2014), among others. A combination of technologies has also been used (Martí & Marín, 2011;Baniukevic et al, 2013;Li et al, 2015).…”

Quantum Computing for Location Determination

“…Classical fingerprinting-based RF localization systems use different matching functions to compare the online RSS vector to those stored in the fingerprint. These matching functions include Euclidean, Manhattan, Chi-Squared, Bray-Curtis, Mahalanobis, and cosine similarity [4,5,13,21,54]. The last one is usually used to combat the device heterogeneity effects [21,54].…”

Capturing Privacy-Preserving User Contexts with IndoorHash

“…Furthermore, by knowing the location of WiFi APs, the location of users can be inferred from their fingerprint. In particular, some research works are focusing on optimizing the similarity distance between two WiFi fingerprints [2,9,20,37]. Zhang et al [40] introduce Polaris, a location system using cluster-based solution for WiFi fingerprints.…”