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Experimental Evaluation of Radio Tomographic Imaging Algorithms for Indoor Localization with Wi-Fi
Rhein Westfal TH Aachen, Inst Networked Syst, Aachen, Germany..
Rhein Westfal TH Aachen, Inst Networked Syst, Aachen, Germany..
Rhein Westfal TH Aachen, Inst Networked Syst, Aachen, Germany..
Rhein Westfal TH Aachen, Inst Networked Syst, Aachen, Germany..
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2017 (English)In: 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2017Conference paper, Published paper (Refereed)
Abstract [en]

Object localization is at the core of several context-aware applications envisioned for the Internet of Things. However, the present localization approaches are often too expensive, or are limited by indoor layouts and noise. In recent years, radio tomographic imaging (RTI) has generated great interest as a device-free localization approach. While several RTI algorithms have been proposed in the literature, their robustness and comparative performance in indoor environments, with real-world impairments, has not yet been experimentally studied. In this paper, we compare the performance of three state-of-the-art RTI algorithms and analyze the impact of different environmental conditions and algorithm parameters on the localization accuracy. Our experimental results show that multipath propagation is the main limiting factor for indoor localization using RTI: our measurements over a diverse set of indoor locations exhibit a 90th percentile localization error of between 0.8 m and 2.85 m. Additionally, our experiments reveal that co-channel interference and external human mobility further degrade the accuracy by 5%-20%. Furthermore, we show that while some improvements are achieved through modifications in network configuration and the fundamental RTI algorithm, these changes (such as increased node density and multi-channel RTI) are not feasible for cost-effective deployments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017.
Series
IEEE Global Communications Conference, ISSN 2334-0983
Keywords [en]
Networks, Systems, Model
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-226852DOI: 10.1109/GLOCOM.2017.8253938ISI: 000428054300018Scopus ID: 2-s2.0-85046346978ISBN: 978-1-5090-5019-2 OAI: oai:DiVA.org:kth-226852DiVA, id: diva2:1203685
Conference
IEEE Global Communications Conference (GLOBECOM), Dec 04-08, 2017, YourSingapore, Singapore, Singapore
Note

QC 20180504

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-06-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
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  • Other locale
More languages
Output format
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