Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2016

Abstract

We explore the use of multi-dimensional mobile sensing data as a means of identifying errors in one or more of those data streams. More specifically, we look at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi fingerprints, by using concurrent readings from Wi-Fi and barometer sensors from a collection of mobile devices. Our key contribution is a novel two-step process: (i) using longitudinal, crowd-sourced readings of (possibly incorrect) Wi-Fi location estimates to statistically estimate the barometer calibration offset of individual mobile devices, and (ii) then, using such offset-corrected barometer readings from devices (that are supposedly collocated) to identify likely errors in indoor localization. We evaluate this approach using data collected from 104 devices collected on the SMU campus over a period of 61 days: our results show that (i) 49% of the devices had barometer offsets that result in errors in floor-level estimation, and (iii) 46% of the Wi-Fi location estimates were potentially incorrect. By identifying specific locations with unusually high fraction of incorrect location estimates, we attempt to more accurately pinpoint the areas that need re-fingerprinting.

Keywords

barometer offset errors, barometer-offset, calibration, collection-offset, crowdsourced corroboration, device-offset, re-fingerprinting, error detection, indoor location

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

MobiData '16: Proceedings of the First Workshop on Mobile Data, Singapore, June 30

First Page

19

Last Page

24

ISBN

9781450343275

Identifier

10.1145/2935755.2935762

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/2935755.2935762

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