Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Kanaris, Loizos; Kokkinis, Akis; Liotta, Antonio; Stavrou, Stavros

doi:10.3390/s17040812

Cited by 62 publications

(47 citation statements)

References 44 publications

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“…In future work, we want to utilize PDR techniques and sensor fusion to automate the data collection process and to enhance the positioning quality during localization. In addition, our approach is not limited to WLAN RSS fingerprinting, but can be extended to support magnetic and light sensors [54,55] or bluetooth [56], which we want to demonstrate in future work.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Laska

Blankenbach

Klamma

2020

Sensors

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The accuracy of fingerprinting-based indoor localization correlates with the quality and up-to-dateness of collected training data. Perpetual crowdsourced data collection reduces manual labeling effort and provides a fresh data base. However, the decentralized collection comes with the cost of heterogeneous data that causes performance degradation. In settings with imperfect data, area localization can provide higher positioning guarantees than exact position estimation. Existing area localization solutions employ a static segmentation into areas that is independent of the available training data. This approach is not applicable for crowdsoucred data collection, which features an unbalanced spatial training data distribution that evolves over time. A segmentation is required that utilizes the existing training data distribution and adapts once new data is accumulated. We propose an algorithm for data-aware floor plan segmentation and a selection metric that balances expressiveness (information gain) and performance (correctly classified examples) of area classifiers. We utilize supervised machine learning, in particular, deep learning, to train the area classifiers. We demonstrate how to regularly provide an area localization model that adapts its prediction space to the accumulating training data. The resulting models are shown to provide higher reliability compared to models that pinpoint the exact position.

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Section: Discussionmentioning

confidence: 99%

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Laska

Blankenbach

Klamma

2020

Sensors

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“…Other approaches use sensor installations in the environment, such as Wi-Fi [11,30], BLE beacons [40,57], or both [36]. BLE technology is now a popular approach to support blind navigation and has been implemented in universities [47], shopping malls [57], airports [7,33,37], among others.…”

Section: Indoor Navigation Assistancementioning

confidence: 99%

Airport Accessibility and Navigation Assistance for People with Visual Impairments

Guerreiro

Ahmetovic

Sato

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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People with visual impairments often have to rely on the assistance of sighted guides in airports, which prevents them from having an independent travel experience. In order to learn about their perspectives on current airport accessibility, we conducted two focus groups that discussed their needs and experiences in-depth, as well as the potential role of assistive technologies. We found that independent navigation is a main challenge and severely impacts their overall experience. As a result, we equipped an airport with a Bluetooth Low Energy (BLE) beacon-based navigation system and performed a real-world study where users navigated routes relevant for their travel experience. We found that despite the challenging environment participants were able to complete their itinerary independently, presenting none to few navigation errors and reasonable timings. This study presents the frst systematic evaluation posing BLE technology as a strong approach to increase the independence of visually impaired people in airports. CCS CONCEPTS • Human-centered computing → Empirical studies in accessibility; Accessibility technologies.

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“…Kanaris et al [26] proposed a novel technique for indoor localization based on the cooperation between IEEE802.11 infrastructure and Bluetooth Low Energy (BLE). BLE is used to confine the location of the user within a specific subarea that means coarse localization.…”

Section: Related Workmentioning

confidence: 99%

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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Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Cited by 62 publications

References 44 publications

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection

Airport Accessibility and Navigation Assistance for People with Visual Impairments

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

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