RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems

Abstract

Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy <0.85 m over 3000 m2, with an accumulative density function (CDF) of 88% with a distance error of 2 m.

RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering

“…To reduce the size of WiFi radio maps, some authors have proposed the dimensionality reduction. For instance, Abed et al [10] exploit the feature of some APs to transmit more than one signal (Multiple Service Set Identifiers, MSSID), and they propose a new dimensionality-reduction technique based on it. Their objective is to identify the most relevant APs, improve computational efficiency, and reduce the effects of multipath propagation.…”

Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation

“…The predefined points are known as the anchors, while the target refers to the point whose position needs to be estimated. The common measurements are angle of arrival (AOA) [ 14 , 15 , 16 , 17 ], time of arrival (TOA) [ 18 , 19 , 20 ], time difference of arrival (TDOA) [ 21 , 22 ] and received signal strength (RSS) [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. However, AOA requires antenna arrays.…”

A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

“…A standard probabilistic method was developed in [11] to improve the accuracy and consistency of localization through the received signal strength of the device. In [12], a dimensionality reduction scheme with low computation cost was introduced for an indoor positioning system. But then the designed method failed to consider the path loss to achieve high precision indoor positioning system.…”