City-scale localization with telco big data

Fangzhou Zhu, Chen Luo, Mingxuan Yuan, Yijian Zhu, Zhengqing Zhang, Tao Gu, Ke Deng, Weixiong Rao, Jia Zeng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

22 Citations (Scopus)

Abstract

It is still challenging in telecommunication (telco) industry to accurately locate mobile devices (MDs) at city-scale using the measurement report (MR) data, which measure parameters of radio signal strengths when MDs connect with base stations (BSs) in telco networks for making/receiving calls or mobile broadband (MBB) services. In this paper, we find that the widely-used location based services (LBSs) have accumulated lots of over-the-top (OTT) global positioning system (GPS) data in telco networks, which can be automatically used as training labels for learning accurate MR-based positioning systems. Benefiting from these telco big data, we deploy a context-aware coarse-to-fine regression (CCR) model in Spark/Hadoop-based telco big data platform for city-scale localization of MDs with two novel contributions. First, we design map-matching and interpolation algorithms to encode contextual information of road networks. Second, we build a two-layer regression model to capture coarse-to-fine contextual features in a short time window for improved localization performance. In our experiments, we collect 108 GPS-associated MR records in the centroid of Shanghai city with 12 × 11 square kilometers for 30 days, and measure four important properties of real-world MR data related to localization errors: stability, sensitivity, uncertainty and missing values. The proposed CCR works well under different properties of MR data and achieves a mean error of 110m and a median error of 80m, outperforming the state-of-art range-based and fingerprinting localization methods.

Original languageEnglish
Title of host publicationProceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM 2016)
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages439-448
Number of pages10
ISBN (Print)9781450340731
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume24-28-October-2016

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period24/10/1628/10/16

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