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.
Highlights
A precise real-time indoor positioning system has recently attracted considerable attention from researchers due to location-aware services, which include patient location monitoring, mobile advertising, tag tracking, and robot guiding [1,2,3,4,5]
Within indoor environments, tracking people and localizing objects have become a necessity and motivates many researchers to tackle the challenges of Indoor positioning systems (IPS)
Reducing the computational cost of IPS is very important for real-time system applications because large data require a long time to be processed
Summary
A precise real-time indoor positioning system has recently attracted considerable attention from researchers due to location-aware services, which include patient location monitoring, mobile advertising, tag tracking, and robot guiding [1,2,3,4,5]. Unlike Global Positioning Systems (GPS), that require line of sight (LOS) transmission paths, indoor positioning faces many challenges due to changeable radio propagation environments inside the buildings [6]. Due to walls, celling, movement of people or furniture, and obstacles inside the buildings, indoor radio propagation is affected by. Sci. 2019, 9, 3137 multipath fading, shadowing, and delay distortion [7]. In addition to high-accuracy requirements, indoor position systems should estimate the position of an object quickly with a light algorithm and low computational cost
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