Yuming Hu,
Feng Qian,
Zhimeng Yin,
Zhenhua Li,
Zhe Ji,
Qiang Xu,
Yeqiang Han,
Wei Jiang
Department of Computer Science & Engineering
University of Minnesota
The official website is here.
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 m^2 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.
MLoc consists of two phases: offline training,
where (fingerprint, location) pairs are collected to build a
localization model, and online inference, where a user’ smartphone
collects fingerprints, uploads them to the edge, and
obtains the location and/or navigation guidance in real time.
Beacon deployment
Fingerprint collecting
Corner cases of localization (narrow corridor, and dead end)
@inproceedings{hu2022mloc, author = {Yuming Hu, Feng Qian, Zhimeng Yin, Zhenhua Li, Zhe Ji, Yeqiang Han, Qiang Xu, Wei Jiang}, title = {Experience: Practical Indoor Localization for Malls}, year = {2022}, isbn = {781450391818}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3495243.3517021}, doi = {10.1145/3495243.3517021}, booktitle = {The 28th Annual International Conference On Mobile Computing And Networking (ACM MobiCom '22)}, pages = {82–93}, numpages = {12}, location = {Sydney, NSW, Australia}, series = {Mobicom '22} }
MLoc has been deployed since
9/2018 with improvements being made over the past three
years. We have conducted principled, large-scale evaluations
by hiring trained testers.
We release the data used in the paper, most of which is collected in the past two years.
MLoc adopts an outsourcing approach (i.e., hiring paid human workers) for
collecting BLE/GMF fingerprints and the ground truth location
data. The details of the data format
can be found here, and an example of the trace file
is shown here.
This project was supported in part by NSF Award 1915122 and 2128489. Zhimeng Yin’s research was supported by City University of Hong Kong 9610491 and NSF China 62102332.