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[GLOBECOM2022] Room-by-Room Device Grouping for Put-and-Play IoT System

[GLOBECOM2022] Room-by-Room Device Grouping for Put-and-Play IoT System

Presented in IEEE GLOBECOM2022, Rio de Janeiro, Brazil (+Online)

paper: not ready
pdf: https://pman0214.netlify.app/static/e93bf89a75f05d1a0bf868f7cc76b415/ishida22-ieee-globecom.pdf

Shigemi ISHIDA

December 09, 2022
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  1. Room-by-Room Device Grouping
    for Put-and-Play IoT System
    Shigemi Ishida*1, Tomoki Murakami*2, Shinya Otsuki*2
    *1Future University Hakodate, JAPAN
    *2Nippon Telegraph and Telephone Corporation, JAPAN

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  2. Outline
    n Motivation and background
    n Room-by-room device grouping system
    n Evaluation
    n Summary
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN
    2

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  3. Motivation, Background
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN
    3

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  4. Smart House
    4
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  5. Smart (?) House
    n Smart (?) speaker
    p Specify almost everything to use services
    n IoT device coordination
    p Provides smart (?) life by manually
    configuring IoT devices
    5
    Which light?
    Turn off the light
    Restroom
    There is no place
    named restroom
    Bathroom!
    Livingroom
    Bedroom
    Bath-
    room
    Wash-
    room
    Dining/Kitchen
    Nature
    Remo
    Switchbot
    Switchbot
    Hub
    Echo
    Flex
    Echo
    Flex
    Smart
    power strip
    There is no device
    named bathroom
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  6. Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    Put-and-Play (PnP) IoT
    n Configures automatically after
    installation and becomes in service
    6
    I m in living
    room
    This TV is in
    the same room
    The light in this
    room is this one
    4 people here
    (After taking shower)
    Turn off the light
    Bathroom
    becomes
    empty
    Okay!
    (Turn off the light
    in the bathroom)

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  7. Goal
    7
    Network
    config
    Grouping
    Room-by-
    room
    grouping
    WPS
    Localize
    Ask user the
    location
    when used
    Coordinate
    Turn of the
    light
    Must be
    bathroom
    light
    Okay!
    (Turn off the
    bathroom light)
    Learn from
    actual
    usage
    Where is the
    microwave
    installed?
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  8. Goal
    8
    Network
    config
    Grouping
    Room-by-
    room
    grouping
    WPS
    Localize
    Ask user the
    location
    when used
    Coordinate
    Turn of the
    light
    Must be
    bathroom
    light
    Okay!
    (Turn off the
    bathroom light)
    Learn from
    actual
    usage
    Where is the
    microwave
    installed?
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  9. Room-by-Room Device Grouping
    System
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN
    9

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  10. Key Idea
    n Group IoT devices based on CSI changes
    p Affected by human movement
    èAnalyze CSI changes when humans are
    moving around IoT devices
    10
    E and F are in
    the same room!
    v
    v
    A
    B
    C
    D
    F
    E
    G
    A
    D
    E
    G
    B
    C
    F
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  11. System Overview
    12
    Remove phase rotation
    by calculating sin, cos
    Room-by-room grouping
    using clustering
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  12. Feature Extraction
    Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    13
    sin ∅!"
    , cos ∅!"
    instead of ∅!" mean
    median
    max
    min
    std
    p2p
    iqr

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  13. Feature Extraction
    Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    14
    Group IoT devices by clustering based on
    feature vector
    Used k-means, but
    is not limited to

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  14. Evaluation
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN
    15

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  15. Experiment Setup
    n 1BD smart house
    p AP, CSI collector in dining room
    p 2 of Galaxy S7 edge in each room
    l Height: 0 90cm
    p Sample CSI at 10Hz
    for 5 min
    p While human is
    walking
    p With doors
    opened/closed
    16
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  16. Datasets
    n Wildcard * represents combined datasets
    p NH/* = NH/OP + NH/CL
    p */OP = NH/OP + DN/OP + LV/ OP + BD/OP
    Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    17
    Dataset (5 min each) Abbrv Human walking in Doors
    No human w/ opened doors NH/OP Opened
    No human w/ closed doors NH/CL Closed
    Dining room w/ opened doors DN/OP Dining room Opened
    Dining room w/ closed doors DN/CL Dining room Closed
    Living room w/ opened doors LV/OP Living room Opened
    Living room w/ closed doors LV/CL Living room Closed
    Bedroom w/ opened doors BD/OP Bedroom Opened
    Bedroom w/ closed doors BD/CL Bedroom Closed

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  17. Evaluation Metric
    n Adjusted Rand Index (ARI)
    p Clustering performance metric
    p −1 ≤ ARI ≤ 1
    l 1 indicates perfect clustering
    l 0 indicates random
    n Not classification!
    p No confusion matrix can be derived
    Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    18
    Living
    room
    Bedroom

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  18. Evaluation
    n Feature selection
    n Human location
    n Windowing
    p Win size
    p 𝑁!"#
    Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    19

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  19. Eval (1): Feature Selection
    n Win size = 10s, 𝑁!"#
    = 4
    n Randomly select windows from each
    dataset and group IoT devices
    n Repeat 100 times, calculate mean ARI
    Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    20
    Feature */OP */CL */*
    sin ∅!"
    , cos ∅!"
    𝝍𝒍𝒋
    sin ∅!"
    , cos ∅!"
    𝝍𝒍𝒋
    sin ∅!"
    , cos ∅!"
    𝝍𝒍𝒋
    mean 0.10 0.40 0.44 0.01 0.44 0.35
    median 0.10 0.44 0.44 0.03 0.45 0.36
    max 0.47 0.39 0.41 0.43 0.44 0.39
    min 0.60 0.43 0.39 0.35 0.52 0.44
    std 0.69 0.89 0.34 0.81 0.63 0.93
    p2p 0.69 0.83 0.52 0.76 0.81 0.89
    iqr 0.45 0.85 0.28 0.87 0.36 0.93

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  20. Eval (1): Feature Selection
    n Win size = 10s, 𝑁!"#
    = 4
    n Randomly select windows from each
    dataset and group IoT devices
    n Repeat 100 times, calculate mean ARI
    Ishida Lab, Future Univ Hakodate, JAPAN
    Dec 4-8, 2022
    21
    Feature */OP */CL */*
    sin ∅!"
    , cos ∅!"
    𝝍𝒍𝒋
    sin ∅!"
    , cos ∅!"
    𝝍𝒍𝒋
    sin ∅!"
    , cos ∅!"
    𝝍𝒍𝒋
    mean 0.10 0.40 0.44 0.01 0.44 0.35
    median 0.10 0.44 0.44 0.03 0.45 0.36
    max 0.47 0.39 0.41 0.43 0.44 0.39
    min 0.60 0.43 0.39 0.35 0.52 0.44
    std 0.69 0.89 0.34 0.81 0.63 0.93
    p2p 0.69 0.83 0.52 0.76 0.81 0.89
    iqr 0.45 0.85 0.28 0.87 0.36 0.93
    Features correspond to CSI amplitude
    changes are effective

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  21. Eval (2): Human Location
    n Win size = 10s, 𝑁!"#
    = 10
    n Randomly select windows
    from each dataset and
    group IoT devices
    n Repeat 100 times,
    calculate mean ARI
    22
    Human location ARI
    0.42
    Dining room 0.27
    Living room 1.00
    Bedroom 0.29
    Anywhere 0.95
    Anywhere or no
    where
    0.95
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN
    Features correspond to many human
    locations are important

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  22. Eval (3): Windowing
    n # 𝑁!"#
    of wins
    p Big 𝑁()*
    increases ARI
    p Saturate when 𝑁()* = 15
    èSufficient number of wins
    are required to extract
    influence of human
    n Win size
    p Big window increases ARI
    p Saturate when win size = 10
    èSufficient length compared
    to human movement is
    required
    23
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  23. Summary
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN
    24

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  24. Summary
    n Put-and-Play (PnP) IoT
    p Configures automatically after installation and
    becomes in service
    n Room-by-room device grouping
    p Group IoT devices based on CSI changes
    affected by human movement
    p Use feature vector including the influence of
    human in various location
    n Conducted experiment in smart house
    p Successfully grouped devices with ARI up to 1.0
    25
    Dec 4-8, 2022 Ishida Lab, Future Univ Hakodate, JAPAN

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  25. © 2022 Shigemi ISHIDA, distributed under CC BY-NC 4.0

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