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Indoor Localization with Passerby Data in Parasitic Approach

Published: 24 January 2023 Publication History

Abstract

We propose an innovative indoor localization approach to constructing user trajectories for indoor spaces where GPS signals are unavailable. The results of a simulation experiment demonstrate the effectiveness of the proposed approach and shed light on a new value for obtaining accurate positions indoors.

References

[1]
Sungmin Cho and Christine Julien. 2016. CHITCHAT: Navigating Tradeoffs in Device-to-Device Context Sharing. In Proc. of PerCom. 1--10.
[2]
Maha Kadadha, Hamda Al-Ali, Maha Al Mufti, Amira Al-Aamri, and Rabeb Mizouni. 2016. Opportunistic Mobile Social Networks: Challenges Survey and Application in Smart Campus. In Proc. of WiMob. 1--8.
[3]
Kota Tsubouchi, Teruhiko Teraoka, Hidehito Gomi, and Masamichi Shimosaka. 2020. Parasitic Location Logging: Estimating Users' Location from Context of Passersby. In Proc. of PerCom. 1--10.
[4]
Haotian Wang, Niranjini Rajagopal, Anthony Rowe, Bruno Sinopoli, and Jie Gao. 2019. Efficient Beacon Placement Algorithms for Time-of-Flight Indoor Localization. In Proc. of SIGSPATIAL. 119--128.

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  1. Indoor Localization with Passerby Data in Parasitic Approach

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    cover image ACM Conferences
    SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
    November 2022
    1280 pages
    ISBN:9781450398862
    DOI:10.1145/3560905
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 24 January 2023

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    Author Tags

    1. indoor localization
    2. location logging
    3. mobile sensing

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    SenSys '22 Paper Acceptance Rate 52 of 187 submissions, 28%;
    Overall Acceptance Rate 174 of 867 submissions, 20%

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