Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2024

Abstract

Radio Frequency fingerprinting, based on WiFi or cellular signals, has been a popular approach for localization. However, adoptions in real-world applications have confronted with challenges due to low accuracy, especially in crowded environments. The received signal strength (RSS) could be easily interfered by a large number of other devices or strictly depends on physical surrounding environments, which may cause localization errors of a few meters. On the other hand, the fine time measurement (FTM) round-trip time (RTT) has shown compelling improvement in indoor localization with ~1-2 meter accuracy in both 2D and 3D environments [13]. This method relies on the WiFi standard 802.11mc implemented in APs (two-sided RTT). However, one obstacle is that the number of APs satisfying this 802.11mc requirement is limited because the frequency of an AP upgrade to a newer version is not as frequent as other electrical equipment. The publication of Google's Android 12, supporting one-sided RTT, enables the RTT applicability in almost all AP models. This article synthesizes multiple experiments to evaluate the feasibility of one-sided RTT in indoor localization and describes in detail the effects of various factors such as different AP models, phone models, and burst sizes on the performance of localization accuracy. Despite existing challenges of applying one-sided RTT, this approach is lightweight, scalable, and could easily be utilized by wearable devices to provide reasonably accurate indoor localization.

Keywords

Indoor localization, One-sided RTT

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

BodySYS '24: Proceedings of the 10th Workshop on Body-Centric Computing Systems, Tokyo, June 3-7

First Page

1

Last Page

6

ISBN

9798400706660

Identifier

10.1145/3662009.3662017

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1145/3662009.3662017

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