Positioning Algorithm for Smartphone Based Staff Tracking

Brovko, Tatiana A.; Chugunov, Alexander; Malyshev, Alexander P.

doi:10.1109/rusautocon52004.2021.9537405

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Most of such devices are already capable of localization and sensing, either through Global Navigation Satellite Systems (GNSS) chipsets in outdoor scenarios or through IEEE802.11* (e.g., WiFi), Ultra-Wide Band (UWB), or Bluetooth Low Energy (BLE) chipsets in indoor scenarios. Many future wireless standards will also make localization and sensing as a part of the system design, such as emerging Sixth generation of cellular communications (6G) cellular communications [1], IEEE802.11bf WiFi upcoming standard [2], and UWB chipsets incorporated in modern smartphones [3].…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications

Lohan

Shubina

Niculescu

2022

Sensors

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Future social networks will rely heavily on sensing data collected from users’ mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user’s location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated.

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Section: Introduction and Problem Statementmentioning

confidence: 99%

Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications

Lohan

Shubina

Niculescu

2022

Sensors

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Deep Learning-Based Multifloor Indoor Tracking Scheme Using Smartphone Sensors

Lin

Shin

2022

IEEE Access

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Having recently become an important research topic, indoor tracking in a multi-floor building delivers comprehensive and efficient location-based services. In this paper, we present a deep learning (DL)based indoor multi-floor tracking scheme, that is independent of infrastructure and only uses the smartphone as a terminal device to measure and analyze the user's mobility information. Our method detects the floor transition according to changes in barometer readings. We compiled the time-series barometer data to train the DL model, and applied the data augmentation method to avoid overfitting and data imbalances during model training. Furthermore, we developed a floor decision algorithm to process the DL model's output and generate the floor detection result. In the proposed scheme, the smartphone's inertial measurement unit sensors are used to measure the user's mobility information, and pedestrian dead reckoning (PDR) is exploited to update the user's 2D location. We implemented the multi-floor tracking by combining the floor detection algorithm with PDR. To avoid the accumulated error problem that commonly arises in the infrastructure-free approach, the calibration nodes (CN) were configured in the floor plan to correct the estimated location by matching the possible CNs during the floor transition. We conducted several experiments in multi-floor buildings to evaluate our scheme's performance, and found that our floor detection method achieves a 99.6% average floor number accuracy, with all floor transition types (i.e., stairs, elevator) being successfully recognized. Furthermore, we compared localization performance with the conventional methods to validate the effectiveness of our approach.

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