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[VTC2020-Spring] Design of BLE 2-Step Separate Channel Fingerprinting

[VTC2020-Spring] Design of BLE 2-Step Separate Channel Fingerprinting

Presented in VTC2020-Spring, Online

T. Yamamoto, S. Ishida, R. Kimoto, S. Tagashira, and A. Fukuda
Design of BLE 2-Step Separate Channel Fingerprinting
IEEE Vehicular Technology Conference (VTC2020-Spring), Online, pp.1-6, May 2020

paper: https://doi.org/10.1109/VTC2020-Spring48590.2020.9128872
pdf: https://pman0214.netlify.app/static/98573658a071aa31656ffc6ccd1ab7cd/yamamoto20-vtc-spring.pdf

Shigemi ISHIDA

May 25, 2020
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  1. Takahiro Yamamoto*1, Shigemi Ishida*1, Ryota Kimoto *1,
    Shigeaki Tagashira*2, Akira Fukuda *1
    *1 Kyushu University, *2Kansai University
    Design of BLE 2-Step
    Separate Channel Fingerprinting

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  2. Background
    2
    ■ IoT systems and location-based services
    • Location is important information
    ■ Localization
    • Outdoor: GPS
    • Indoor: Manual measurement
    or indoor localization system
    ■ Localization using wireless signals
    • BLE is popularly used in many IoT
    systems

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  3. BLE Localization
    3
    ■ BLE (Bluetooth Low Energy)
    • Low power, low cost, narrow-band wireless communication technology
    • Frequency hopping spread spectrum (FHSS)
    ■ BLE localization
    • Uses advertising packets sent on 3 channels separated by up to 78MHz

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  4. Unstable BLE Signal Strength
    [Ishizuka+14] “A fundamental study on a indoor localization method using BLE signals and PDR for a smart phone – sharing results of experiments in Open
    Beacon Field Trial (in Japanese)”, IEICE Tech. Rep. MoNA
    4
    ■ Received signal strength (RSS) drastically changes time to time
    • Because of frequency separation of 3 advertising channels [Ishizuka+14]
    ε

    ore)
    ਤ 2 BLE γάφϧͷ RSSI ͱڑ཭ଌҐ݁Ռ
    զʑ͸ɼਤ 3 ͷΑ͏ʹ BLE σόΠεΛਖ਼ࡾ֯ܗʹ഑ஔ͠ɼத
    ৺ʹ RSSI ड৴༻ͷ୺຤Λ഑ஔ͢Δ͜ͱͰɼෳ਺ BLE σόΠ
    45dB
    10dB
    Each measurement suffered from RSS
    changes by up to 45dB
    → RSS cannot directly be mapped to
    distance
    Average also suffered from 10dB
    changes
    → Long-term measurement cannot
    overcome the unstable RSS

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  5. Related Work
    [Zhu+14] “RSSI based Bluetooth Low Energy indoor positioning”, IPIN
    [Paterna+17] “A Bluetooth Low Energy indoor positioning system with channel diversity, weighted trilateration and kalman filtering”, Sensors
    [Li+18] “Indoor positioning algorithm based on the improved RSSI distance model”, Sensors
    [Faragher+15] “Location fingerprinting with Bluetooth low energy beacons”, IEEE J. Sel. Areas Commun.
    [Ishida+16] “Proposal of separate channel fingerprinting using Bluetooth Low Energy”, IIAI-AAI
    5
    ■ Accuracy improvement in BLE-based localization
    • With filtering outliers [Zhu+14][Paterna+17]
    • Compensation using multiple BLE devices [Li+18]
    • Fingerprinting [Faragher+15]
    ■ We also proposed separate channel fingerprinting (SCF) [Ishida+16]
    • Fingerprinting employing channel diversity of 3 adv. channels to improve
    localization accuracy

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  6. Separate Channel Fingerprinting (SCF) [Ishida+16]
    [Ishida+16] “Proposal of separate channel fingerprinting using Bluetooth Low Energy”, IIAI-AAI
    6
    ■ Separately measure RSS on 3 advertising channels
    ■ Channel diversity is employed as location-specific feature

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  7. High Max Error Problem
    7
    ■ SCF suffers from high max error
    • Compared to unified channel
    fingerprinting (UCF), which is a
    conventional BLE fingerprinting,
    SCF improves mean localization
    error
    • Max error is more than 6 meters
    for both UCF and SCF
    ⇒Want to reduce max error
    while improving localization
    accuracy
    0 2 4 6
    0
    200
    400
    Frequency
    UCF
    0 2 4 6
    Localization error [meters]
    0
    200
    400
    Frequency
    SCF
    mean = 0.71
    mean = 0.59

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  8. Key Idea: Localization in 2-Steps
    8
    1. Coarse localization
    • Ignore channel information
    and averaged over RSS
    measured on 3 adv. channels
    2. Fine-grained localization
    • Utilize channel-diversity
    in fingerprinting

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  9. 9
    1. Coarse localization
    • UCF over whole localization area
    2. Fine-grained localization
    • SCF over an area based on the
    result of coarse localization
    2-Step SCF: System Overview
    DB
    1 - -52
    BLE channel RSS
    1 - -58
    2 - -55
    2 - -60
    location
    (0,0)
    (0,0)
    (0,1)
    (0,1)
    2 - -58
    (0,1)
    : : :
    :
    Unified-Channel Fingerprints
    DB
    R
    1 37 -52
    BLE channel RSS
    1 38 -58
    2 37 -55
    2 38 -60
    location
    (0,0)
    (0,0)
    (0,1)
    (0,1)
    2 39 -58
    (0,1)
    : : :
    :
    Separate-Channel Fingerprints
    Estimated location in
    coarse localization
    Training location used in location estimation
    Training location ignored in location estimation
    Nearest locations selected in location estimation
    Estimated location

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  10. 2-Step SCF: Training Phase
    10
    ■ Constructs separate- and unified-channel fingerprint databases
    • Collect a set A!,#
    of RSS samples of BLE beacon measured on channel
    ∈ {37,38,39} at location ∈ L
    • Separate channel fingerprint
    • ! = !,# 37 , !,# 38 , !,# 39 , !,$ 37 , … , !,% 39
    • !,&
    = median A!,&

    • Unified channel fingerprint
    • 2
    ! = !,#, !,$, !,', … , !,%
    • !,&
    = median ⋃(∈ '*,'+,',
    A!,&

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  11. 2-Step SCF: Estimation Phase
    11
    ■ Coarse localization
    • Collect a set B#
    of RSS samples of BLE beacon measured on channel
    ∈ {37,38,39} at a target location
    • Unified channel target fingerprint /

    • 4
    = #, $, ', … , %

    & = median ⋃(∈ '*,'+,',
    B&
    • Estimate location using unified channel
    fingerprint 1
    !
    and unified channel target
    fingerprint /

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  12. 2-Step SCF: Estimation Phase
    12
    ■ Fine-grained localization
    • Separate channel target fingerprint
    • = #
    37 , #
    38 , #
    39 , $
    37 , … , %
    (39)

    & = B&
    • Estimate location using separate channel target fingerprint and
    separate channel fingerprint !
    in a limited area
    • The limited area is a circular area of radius R centered on the location
    estimated in coarse localization
    • Insufficient number of separate channel fingerprints in a limited area
    means localization failure

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  13. 13
    BLE beacon
    Reference location
    Target location
    10 meters
    ■ H-shpaed corridor in our Univ
    • 24 Silicon Labs BLED112 beacons
    • Measure RSS on MacBook Pro
    • Collect fingerprints at 46
    reference locations
    • Collect target
    fingerprints
    at 7 target
    locations
    Evaluation: Setup
    BLE Beacons

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  14. 14
    ■ Localization accuracy
    • 95th percentile of localization
    errors for all localization trials
    0 2 4 6 8 10
    Localization error [meters]
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    Cumulative probability
    UCF
    SCF
    2S-SCF
    Localization Error
    Method Localization accuracy
    [meters]
    Max error [meters]
    UCF 6.76 6.83
    SCF 2.60 6.95
    2-Step SCF 1.00 2.05
    61.5%

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  15. 15
    ■ Localization success rate
    • Increases as R increases
    • Saturated at 60% when R > 2.0
    ■ Localizaton accuracy
    • Increases as R increases
    ■ Trade-off between:
    • Small localization accuracy
    • High localization success rate
    ⇒Need to determine the best R
    2 4 6 8 10
    Area-limit radius R [meters]
    0.0
    0.5
    1.0
    1.5
    2.0
    2.5
    Localization accuracy [meters]
    Localization accuracy
    Localization success rate
    0
    10
    20
    30
    40
    50
    60
    Localization success rate [%]
    Area-Limit Radius

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  16. Summary
    16
    ■ BLE-based localization
    • Suffers from low accuracy due to freqnecy separation
    • Previously proposed SCF utilizing channel diversity to improve localization
    accuracy, which still suffers from high max error
    ■ 2-step SCF
    • Coarse location estimation w/o channel diversity
    • Fine-grained location estimation w/ channel diversity in a limited area
    • Improved localization accuracy by 61.5% while reducing max localization
    error

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  17. © 2020 Shigemi ISHIDA, distributed under CC BY-NC 4.0

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