In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data

Huan Li, Hua Lu, Lidan Shou, Gang Chen, Ke Chen

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

As people spend significant parts of daily lives indoors, it is useful and important to measure indoor densities and find the dense regions in many indoor scenarios like space management and security control. In this paper, we propose a data-driven approach that finds top-k indoor dense regions by using indoor positioning data. Such data is obtained by indoor positioning systems working at a relatively low frequency, and the reported locations in the data are discrete, from a preselected location set that does not continuously cover the entire indoor space. When a search is triggered, the object positioning information is already out-of-date and thus object locations are uncertain. To this end, we first integrate object location uncertainty into the definitions for counting objects in an indoor region and computing its density. Subsequently, we conduct a thorough analysis of the location uncertainty in the context of complex indoor topology, deriving upper and lower bounds of indoor region densities and introducing distance decaying effect into computing concrete indoor densities. Enabled by the uncertainty analysis outcomes, we design efficient search algorithms for solving the problem. Finally, we conduct extensive experimental studies on our proposals using synthetic and real data. The experimental results verify that the proposed search approach is efficient, scalable, and effective. The top-k indoor dense regions returned by our search are considerably consistent with ground truth, despite that the search uses neither historical data nor extra knowledge about objects.
OriginalsprogEngelsk
Titel35th IEEE International Conference on Data Engineering (ICDE)
Antal sider2
ForlagIEEE
Publikationsdato2019
Sider2127-2128
ISBN (Trykt)978-1-5386-7475-8
ISBN (Elektronisk)978-1-5386-7474-1
DOI
StatusUdgivet - 2019
Udgivet eksterntJa
Begivenhed35th International Conference on Data Engineering (ICDE) - Macau, Kina
Varighed: 8 apr. 201911 apr. 2019
Konferencens nummer: 35
https://www.computer.org/csdl/proceedings/icde/2019/1aDSOMTGCIw

Konference

Konference35th International Conference on Data Engineering (ICDE)
Nummer35
LandKina
ByMacau
Periode08/04/201911/04/2019
Internetadresse

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