Experience : Practical indoor localization for malls

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

7 Scopus Citations
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Author(s)

  • Yuming Hu
  • Feng Qian
  • Zhenhua Li
  • Zhe Ji
  • Yeqiang Han
  • Qiang Xu
  • Wei Jiang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationACM MobiCom '22 - Proceedings of the 2022 The 28th Annual International Conference on Mobile Computing and Networking
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages82-93
ISBN (Print)9781450391818
Publication statusPublished - 2022

Publication series

NameProceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

Conference

Title28th ACM Annual International Conference on Mobile Computing and Networking (MobiCom 2022)
PlaceAustralia
CitySydney
Period17 - 21 October 2202

Abstract

We report our experiences of developing, deploying, and evaluating MLoc, a smartphone-based indoor localization system for malls. MLoc uses Bluetooth Low Energy RSSI and geomagnetic field strength as fingerprints. We develop efficient approaches for large-scale, outsourced training data collection. We also design robust online algorithms for localizing and tracking users' positions in complex malls. Since 2018, MLoc has been deployed in 7 cities in China, and used by more than 1 million customers. We conduct extensive evaluations at 35 malls in 7 cities, covering 152K m2 mall areas with a total walking distance of 215 km (1,100 km training data). MLoc yields a median location tracking error of 2.4m. We further characterize the behaviors of MLoc's customers (472K users visiting 12 malls), and demonstrate that MLoc is a promising marketing platform through a promotion event. The e-coupons delivered through MLoc yield an overall conversion rate of 22%. To facilitate future research on mobile sensing and indoor localization, we have released a large dataset (43 GB at the time when this paper was published) that contains IMU, BLE, GMF readings, and the localization ground truth collected by trained testers from 37 shopping malls.

Research Area(s)

  • bluetooth low energy, geomagnetic field, indoor localization

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Experience: Practical indoor localization for malls. / Hu, Yuming; Qian, Feng; Yin, Zhimeng et al.
ACM MobiCom '22 - Proceedings of the 2022 The 28th Annual International Conference on Mobile Computing and Networking. New York: Association for Computing Machinery, 2022. p. 82-93 (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review