Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Ashraf, Imran; Din, Sadia; Hur, Soojung; Park, Yongwan

doi:10.32604/cmc.2022.018707

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…The reliability threshold set ν t = 24 ms. VR services ensure the immersion in a virtual world by guaranteeing the a QoPE below the 20 ms as λ 3 = 0.8 for VR. The network was simulated with data generated from XR users moving according to a random walk scheme as well as a WiFi positioning dataset [35] so as to have a general scheme for user mobility.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Joint Sensing and Communication for Situational Awareness in Wireless THz Systems

Chaccour

Saad

Semiari

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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Section: Simulation Results and Analysismentioning

confidence: 99%

Joint Sensing and Communication for Situational Awareness in Wireless THz Systems

Chaccour

Saad

Semiari

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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“…Wearable-based pedestrian navigation [24], [25] German Aerospace Center FTP server Wi-MEST Dataset [13], [14] Yeungnam University (Korea) GitHub Urban European driving dataset [65] Institute of Mathematics of the Romanian Academy (Romania) Google Sites RISEdb [89] European Commission Joint Research Center (JRC)…”

Section: Uci Machine Learning Repositorymentioning

confidence: 99%

Survey of Machine Learning Methods Applied to Urban Mobility

Ahmed

Diaz

2022

IEEE Access

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To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger's trip. In this work, we present a survey on urban mobility based on passengers' data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning is used to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger's trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design.

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“…As pointed out in [14], the real-world indoor environments experience both transient changes, which are dynamic alterations that occur briefly such as human mobility, and permanent changes like infrastructural changes. The fingerprinting approach is directly affected by the radio map constructed during the offline stage [15].…”

Section: Introductionmentioning

confidence: 99%

Robust Access Points Selection Strategies for Dynamic Indoor Localization

Mazlan,

2024

IEEE Access

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The proliferation of mobile devices has fueled the demand for indoor location-based services. Consequently, a plethora of techniques have emerged to facilitate object and device localization in indoor environments. Among these, fingerprint-based indoor localization systems, which leverage machine learning, stand out as a promising solution for providing accurate localization. Nonetheless, their performance is inherently reliant on the accuracy of the underlying database, and any changes in the indoor layout can significantly impact the wireless signals, subsequently affecting the localization accuracy. To circumvent this issue, this work proposes a novel access point (AP) selection framework to enhance the robustness of fingerprint-based indoor positioning systems in dynamic indoor environments. More specifically, a hybrid Wi-Fi and BLE fingerprint database is constructed by collecting received signal strength (RSS) from the predefined reference points (RPs). To ensure that the fingerprint database remains relevant over time, some RPs are designated as known points so that the system can periodically collect new RSS at the known points. Subsequently, the proposed scheme computes the differences between the RSS of the database and the updated RSS from the new layout to account for the changes occurred. The building-based, floor-based, and zone-based implementation modes determine which APs are reliable to be utilized during localization based on the RSS discrepancies observed in building, floor, and zone, respectively. Results demonstrate that the proposed building-based, floor-based, and zone-based AP selection schemes could achieve reduction in positioning error up to 28.86%, 33.53%, and 39.66%, respectively, compared to the baseline technique without AP selection schemes.

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Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Cited by 4 publications

References 38 publications

Joint Sensing and Communication for Situational Awareness in Wireless THz Systems

Joint Sensing and Communication for Situational Awareness in Wireless THz Systems

Survey of Machine Learning Methods Applied to Urban Mobility

Robust Access Points Selection Strategies for Dynamic Indoor Localization

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