Cognitive Platform for Industrial IoT (IIoT)Antonis Mygiakis, CLMS Hellas

1st Workshop

Rome, 11 October 2018

1

“TOWARDS A COGNITIVE COMPUTING PLATFORM SUPPORTING A UNIFIED APPROACH TOWARDS PRIVACY, SECURITY AND SAFETY (PSS) OF IOT SYSTEMS”

CHARIOT – 1st Workshop, 11 October 2018, Rome

Terminology

Cognitive Computing implies a technology platformthat is based on the scientific and engineeringdisciplines of artificial intelligence and signalprocessing.

Such platforms embody machine learning,reasoning, natural language processing, analytics,and other technologies in pursuit of a cognitivesystem that aims to model human behavior.

2

CHARIOT Platform: IoT system and methodology that iscapable of learning and adapting in pursuit of increasedefficiency of Privacy, Security and Safety protection,and moving towards autonomous decisions.

Fog computing or fog networking, is anarchitecture that uses one or more of acollaborative multitude of end-user clients ornear-user edge devices, to carry out a substantialamount of storage, communication, control,configuration, measurement and management.

Cognitive IoT is the use of cognitive computingtechnologies in combination with datagenerated by connected devices and the actionsthose devices can perform

CHARIOT – 1st Workshop, 11 October 2018, Rome

Why cognitive is significant to IoT

3

CognitionUnderstanding

Reasoning

Learning

Cognitive IoT is the next leap in improving the accuracyand efficiency of complex, sensor-driven systems throughlearning and infusing more human awareness into thedevices and environments we interact with.

IoT is evolving to the single largest source of data on theplanet

CURRENTSTATUS

Rate and scale of data generation

The systems have to begin to understand people

Integration of multiple data sources and typesWHYCOGNITIVE

CONCEPT

CHARIOT – 1st Workshop, 11 October 2018, Rome

Cognitive Challenges

4

↔ tons of IoT generated data

↔ largely unstructured

↔multi-modal and

↔ growing really fast

A lot of sensor data is simply thrown away or is “dark” i.e. not really utilized, or stuck in single-purpose silos (where its productivity is limited)

1

2

3 Concerns on SECURITY, PRIVACY and PROTECTION of personal, proprietary, business and confidential information of all kinds

CHARIOT – 1st Workshop, 11 October 2018, Rome

Example - IIoT Predictive Maintenance

▪ Equipment maintenance is a costly challenge

▪ IIoT connects critical machines and sensors in high-stakes industries such as aerospace and defense, healthcare and energy. These are systems in which failure often results in life-threatening or other emergency situations.

▪ The Industrial Internet of Things (IIoT) uses built-in sensors on everyday objects, from fuel gauges to tires and brakes, to gather data and share it across a network.

▪ A Cognitive system uses ML to analyze data such as temperature and humidity to predict performance and future outcomes (e.g. failures, cost savings, etc.).

5

Real life ExampleCaterpillar, a company that manufactures marine power systems, uses IoT and machine

learning to uncover patterns in equipment and device data. In one example, Caterpillar

identified that fuel meter readings were correlated with the amount of power used by

on-board refrigerated containers. By doing multivariate predictive maintenance analysis

in Pentaho, the customer discovered that running more generators at lower power was a

more efficient approach than maxing out a few. The resulting savings was $30 per hour, or

$650,000 over a year, for 50 ships.

CHARIOT – 1st Workshop, 11 October 2018, Rome

The CHARIOT Proposition

CHARIOT Cognitive Platform for industrial IoT

7

Cognitive IoT

Platform

IoT Cognitive

Architecture

IPSS

Engine

Fog Computing

Blockchain Security Services

Semantics

Domain Specific

Language (IoTL)

Digital Twin

IoT gatewaySensors

Blockchain Handshaking

All the processing happens on thefog, physically closer to where thedata is collected, instead of sendingdata to the cloud.

Fog Computing

CHARIOT – 1st Workshop, 11 October 2018, Rome

CHARIOT Cognitive Platform

Fog NodeCloud Services

ML Repository

Watson IoT Train Model

Cloud Storage

GatewayPANTHORA

Local DB

SouthboundDispatcher

NorthboundDispatcher

Alerts

DASHBOARD

ML Models Repository

Cloud Services

SensorsFire, Pressure,

Temperature, Motion etc.

Block-chain secured

CHARIOT – 1st Workshop, 11 October 2018, Rome

Safety Engine

Security Engine

Privacy Engine

EXTERNAL DATASOURCES

CHARIOT Cognitive Platform – Cloud computation services

Train ModelCreate global prediction threat model(s)

Cloud StorageIBM Cloudant

Watson IoTSouthbound dispatcher forwards all the data to Watson IoT.

Global ModelTrained model over the data stored in the cloud.

ML RepositoryPull the latest prediction threat model.

Anomaly DetectionTry to find anomalies on the global dataset

FOG NODE FOG NODEDASHBOARD

CHARIOT – 1st Workshop, 11 October 2018, Rome

Semantics in IoT

Benefits

Data access & integration

Resource discovery

Interoperability (heterogeneity)

Provenance

Linked Data

Reasoning on Data

Data Validation

Knowledge extraction

Compliment ML approach

Example – Airport Dashboard

Example – Area Evacuation

HighTemperature

VentilationSystemMalfunction

DetectedSmoke

evacuateActionB2R210

PotentialFire

Next Steps

Evolve the approach

Work on the Machine Learning models

Utilize Semantics (ontologies, reasoning)

Test and Validate the approach in the Living Labs

Contact Details

a.mygiakis@clmsuk.com

Antonis Mygiakis

CLMS Hellas

CHARIOT – 1st Workshop, 11 October 2018, Rome 15