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Toward Translating Raw Indoor Positioning Data into Mobility Semantics

Published:25 November 2020Publication History
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Abstract

Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into mobility semantics that describe a moving object’s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the cleaning layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the annotation layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a semantic matching method that makes proper annotations for split snippets. In the complementing layer, we devise an inference method that makes use of the indoor topology and the mobility semantics already obtained to recover the missing mobility semantics. The extensive experiments demonstrate that our solution is efficient and effective on both real and synthetic data. For typical queries, our solution’s resultant mobility semantics lead to more precise answers but incur less execution time than alternatives.

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          cover image ACM/IMS Transactions on Data Science
          ACM/IMS Transactions on Data Science  Volume 1, Issue 4
          Special Issue on Retrieving and Learning from IoT Data and Regular Papers
          November 2020
          148 pages
          ISSN:2691-1922
          DOI:10.1145/3439709
          Issue’s Table of Contents

          Copyright © 2020 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 25 November 2020
          • Online AM: 7 May 2020
          • Accepted: 1 January 2020
          • Revised: 1 November 2019
          • Received: 1 August 2019
          Published in tds Volume 1, Issue 4

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