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ADOS: An advanced AI-based oracle system for securing off-chain IoT data integrity when injecting in the blockchain

ADOS: An advanced AI-based oracle system for securing off-chain IoT data integrity when injecting in the blockchain

Slides presented at the ONTOCHAIN Summit for Trustworthy Internet by Juan Miguel Navarro, Professor at UCAM university & ADOS and AirTrace technical coordinator

ONTOCHAIN

June 02, 2022
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  1. CLICK TO EDIT
    MASTER TITLE STYLE
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    Location
    Date
    ADOS (AIRTRACE
    DECENTRALIZED
    ORACLE SYSTEM)
    An advanced AI-based oracle
    system for securing off-chain
    IoT data integrity when
    injecting in the blockchain
    OntoChain Summit 22
    Berlin, 2 June
    ®
    ANY IOT SENSOR, BLOCKCHAIN-ENABLED

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  2. | ONTOCHAIN.NGI.EU
    2
    Visual: Web user interface for fast integration
    Any IoT sensor, blockchain-enabled
    (Learn more at https://airtrace.io)
    ®

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  3. | ONTOCHAIN.NGI.EU
    3
    Programmatic: RESTFUL API and IoT protocols
    Any IoT sensor, blockchain-enabled
    (Learn more at https://airtrace.io)
    ®

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  4. | ONTOCHAIN.NGI.EU
    4
    WHY
    ANOMALY
    DETECTION
    IN
    BLOCKCHAIN
    -IOT
    DOMAINS?

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  5. | ONTOCHAIN.NGI.EU
    1.-Machine Learning algorithm with
    distributed computation
    2.- Ontology for IoT-ML models
    3.- API for integration with 3rd parties
    Contributions of the project

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  6. | ONTOCHAIN.NGI.EU
    6
    ML MODELS FOR DEEPER UNDERSTANDING
    Three types of anomalies
    Point: irregularity that happens randomly and may
    have no particular reason.
    Contextual: abnormal behavior happening within
    some specific context.
    Collective: a collection of individual data points
    showing anomalies.

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  7. | ONTOCHAIN.NGI.EU
    7
    SUPPORTING ARTIFICIAL
    INTELLIGENCE IN IOT FOR
    EARLY ANOMALY DETECTION,
    TIMELY ALERTING, AND DATA-
    QUALITY SCORING, BEFORE
    INJECTING INTO THE
    BLOCKCHAIN
    ADOS - GNN
    Deng, A., & Hooi, B. (2021, February). Graph neural network-based anomaly detection in multivariate time
    series. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 5, pp. 4027-4035).

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  8. | ONTOCHAIN.NGI.EU
    8
    ARCHITECTURE OF THE SOLUTION
    Client n
    Client 1
    ADOSTM
    IoT System Integrators
    Model weights
    S3/IPFS
    Periodic model
    training
    Sensor
    readings
    Worker 2
    Worker 1
    Worker 3
    Worker 4
    Worker n
    Worker 5
    Worker 6
    Data quality
    factors
    Middleware (WoT)
    Blockchain

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  9. | ONTOCHAIN.NGI.EU
    9
    USE STORIES AND USE-CASE
    Different scenarios:
     Water quality sensors: in water treatment plants, it is possible to find
    several sensors measuring water levels, water quality, valve status, flow
    rates, etc. Data measured by these IoT sensors can be related in complex,
    nonlinear ways.
     Acoustic sensors: in smart cities, acoustic changes can reveal changes in
    car traffic, weather conditions, etc.. Acoustic sensors are used to monitor
    the sound pressure level (noise pollution) of the city.
     Transportation sector: to understand the existing dynamics arising in
    scenarios of car traffic in order to understand how vehicular mobility of
    several vehicles can affect each other .

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  10. 10
    ONTOLOGICAL
    RESOLUTION
    MIDDLEWARE
    (WOT)
    Name Description Domain
    New/MyUpdated/
    MyReused/Reused
    Language
    AOM (ADOS
    Ontology
    Model)
    Regulates data for
    anomaly detection
    models to work
    properly (e.g. model
    version, TEE needed,
    etc.)
    Machine Learning
    model specification
    New WoT
    SSN
    Sensors and their
    observations, the
    involved procedures,
    and the observed
    properties.
    Systems (sensors and
    actuators)
    Reused WoT
    WGS84
    Represents
    geocoordinates
    (latitude and
    longitude), with
    class geo:point and
    properties
    geo:latitude,
    geo:longitude,
    geo:altitude.
    Geolocation
    (outdoors)
    Reused WoT
    DUL
    By combining
    WSG84 properties
    with dul:hasLocation
    from DUL ontology
    we can represent
    the sensor location
    indoors
    Geolocation (indoors) Reused WoT
    FIEMSER
    Represents the way
    to communicate
    with the IoT devices,
    including, at least,
    communication
    protocol and its
    version.
    Communications Reused WoT
    QUDT
    Represents a
    comprehensive list
    of quantities, units
    and dimensions to
    define IoT readings
    formats.
    Observations Reused WoT
    FOAF
    Information about
    IoT vendor and
    version of the device
    Vendor, version and
    deployment time
    Reused WoT

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  11. | ONTOCHAIN.NGI.EU
    11
    DEMO LINK

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  12. - Training models with
    distributed computation
    - Graph tools for anomaly
    interpretation and analysis
    - Exploration of other ML
    techniques
    Future extensions

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  13. THANKS FOR
    YOUR
    SUPPORT
    QUESTIONS??

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