Indoor positioning system using multiple services set identifiers-based fingerprints

Abed, Ahmed K.; Al-Moukhles, Hussein; Abdel‐Qader, Ikhlas

doi:10.1109/eit.2017.8053373

Cited by 4 publications

(4 citation statements)

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“…Furthermore, the performance of the maximum spatial variance with 17 APs (50% reduction) is similar to the performance of all APs (34 APs). This confirms previous work [11]. We found the redundancy of MSSID occurred when SSIDs were exploited in the same fingerprinting vectors.…”

Section: Selection Of Aps With K-meanssupporting

confidence: 92%

“…The channel is utilized by another SSID when the channel is clear. Therefore, the probability that all SSID signals are affected by the fading issue will be low [11]. In this article, we use this benefit to mitigate the variation of RSS signals by using a fusion approach for MSSIDs' signals.…”

Section: Multiple Services Set Identifier (Mssids) Techniquementioning

confidence: 99%

“…Fuzzy partition is achieved through an iterative optimization shown in Equation (10). In order to find and Cj, the Equation (11) and Equation (12) should be applied:…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

“…MSSID is used in most buildings to advertise different WLANs where each WLAN has a different class of clients, and each class requires different keys, firewalls, privacy, and speeds [10]. This technique leads to increasing redundancy in the calculation process and a decrease in the accuracy of systems when each SSID is considered as a single AP [11].…”

Section: Introductionmentioning

confidence: 99%

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

Abed

Abdel‐Qader

2019

Applied Sciences

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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.

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Section: Selection Of Aps With K-meanssupporting

confidence: 92%

Section: Multiple Services Set Identifier (Mssids) Techniquementioning

confidence: 99%

“…Fuzzy partition is achieved through an iterative optimization shown in Equation (10). In order to find and Cj, the Equation (11) and Equation (12) should be applied:…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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