In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data [Extended Abstract]

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

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-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 When a search is triggered, the object positioning information is already out-of-date and thus object locations are uncertain.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.
Original languageEnglish
Title of host publicationProceedings of the 35th IEEE International Conference on Data Engineering (ICDE)
Publication date2019
Pages2127-2128
Publication statusPublished - 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: 8 Apr 201911 Apr 2019

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period08/04/201911/04/2019

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Uncertainty analysis
Topology
Concretes
Uncertainty
Indoor positioning systems

Cite this

Li, H., Lu, H., Shou, L., Chen, G., & Chen, K. (2019). In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data [Extended Abstract]. In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE) (pp. 2127-2128)
Li, Huan ; Lu, Hua ; Shou, Lidan ; Chen, Gang ; Chen, Ke. / In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data [Extended Abstract]. Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE). 2019. pp. 2127-2128
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Li, H, Lu, H, Shou, L, Chen, G & Chen, K 2019, In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data [Extended Abstract]. in Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE). pp. 2127-2128, 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 08/04/2019.

In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data [Extended Abstract]. / Li, Huan; Lu, Hua; Shou, Lidan; Chen, Gang; Chen, Ke.

Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE). 2019. p. 2127-2128.

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

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Li H, Lu H, Shou L, Chen G, Chen K. In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data [Extended Abstract]. In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE). 2019. p. 2127-2128