Radio-frequency-based indoor-localization techniques for enhancing Internet-of-Things applications
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ORIGINAL ARTICLE
Radio-frequency-based indoor-localization techniques for enhancing Internet-of-Things applications Andreas Girgensohn1
· Mitesh Patel1 · Jacob T. Biehl2
Received: 31 October 2019 / Accepted: 20 August 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract An important capability of most smart, Internet-of-Things-enabled spaces (e.g., office, home, hospital, factory) is the ability to leverage context of use. Location is a key context element, particularly indoor location. Recent advances in radio ranging technologies, such as Wi-Fi RTT, promise the availability of low-cost, near-ubiquitous time-of-flight-based ranging estimates. In this paper, we build on prior work to enhance this ranging technology’s ability to provide useful location estimates. For further improvements, we model user motion behavior to estimate the user motion state by taking the temporal measurements available from time-of-flight ranging. We select the velocity parameter of a particle-filter-based on this motion state. We demonstrate meaningful improvements in coordinate-based estimation accuracy and substantial increases in roomlevel estimation accuracy. Furthermore, insights gained in our real-world deployment provide important implications for future Internet-of-Things applications and their supporting technology deployments such as social interaction, workflow management, inventory control, or healthcare information tools. Keywords Indoor location · Bluetooth Low Energy · Wi-Fi RTT
1 Introduction Indoor positioning and tracking are long-standing topics of IoT research. As the realization and utility of broader concepts such as smart buildings gain traction, accurate and reliable indoor positioning and tracking techniques have become essential core technologies. Research and industry have proposed many technology approaches to indoor location, including audio [1, 2], light [3, 4], inertial [5], and magnetic sensing [6] approaches. However, most techniques leverage common radios, such as Wi-Fi and Bluetooth Low Andreas Girgensohn
[email protected] Mitesh Patel [email protected] Jacob T. Biehl [email protected] 1
FX Palo Alto Laboratory, Inc., 3174 Porter Drive, Palo Alto, CA, 94304, USA
2
School of Computing and Information, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA 15260, USA
Energy (BLE) [7–9], due to their ubiquity and low cost. Recently, standards committees and individual companies have published open protocols that can be easily accessed on commodity mobile devices, such as iPhone or Android smartphones. The predominant focus and driving metric in indoor positioning research has been accurate coordinate position estimation. Specifically, the goal is the low latency and high accuracy prediction of a device’s location as a point on a map. The research community has advanced the state-of-the-art in coordinate positioning, contributing technology algorithms [10, 11], evaluations [12], and thought leadership [13]. These efforts have established standards and goals for thi
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