cognitive iot at first italian iot day 9 april 2013

iCore: use of virtualised objects

and cognitive technologies in IoTand cognitive technologies in IoT

Raffaele Giaffreda (CREATE-NET)

EU FP7 iCore Project Coordinator

First Italian IoT Day

Trento 9 March 2013 #iotitaly

transistor density / space efficiency

Turing’s Pilot ACE: Automatic

Computing Engine

bandwidth / spectral efficiency

Space Efficiency + Spectral Efficiency =

MAZE OF TINY, CONNECTED THINGS

Trend: more and more widespread sensing and monitoring data available

an example: the car

DATA /

INFORMATION

MACHINEHUMAN

HUMANMACHINE

INFORMATION

OVERLOAD

Trend: much more data than we can cope with

siloed and bespoke IoT applications

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DATA / INFORMATION OVERLOAD, BUT...

CA

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SENSORS

APPS

HUMANMACHINE

IoT innovation potential...

“Innovation”: one

can focus on apps!!!

OBD

On Board Diagnostics

MACHINEHUMAN

IF A WELL-DEFINED INTERFACE INTO CAR SENSORS BRINGS SUCH POTENTIAL...

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iCore concepts

• Virtual Object

• Composite Virtual

Object

• Service / User Level

• Service / User Level

Virtual Object as OBD across silos

IoT services

Virtual Object SW Agent

IoT services

VO registry

To upper iCore levels and Internet

Semantic VO descriptions

Ab

stra

ctio

n

ICT objects

(heterogeneous world)

Sensors and actuators

Proprietary servicesIoT services

Associated physical objects

Ab

stra

ctio

n

10

what ingredients?

• common interfaces to interact with objects

(i.e. REST)

• + extra containers for metadata

• let the systems know what the object is good • let the systems know what the object is good

for, its location (“I am a Temp sensor in Room

A”), its accuracy, its energy levels etc.

WHAT ARE VOs GOOD FOR?

• OBJECTS REUSE

– reuse across different apps, increase availability,

hence, increase monitoring / sensing granularity

• OBJECTS MGMT • OBJECTS MGMT

– i.e. energy management, contextualised sensing

(accuracy vs. sensing frequency) etc.

• OBJECTS PROXIMITY

– automated selection “by relevance” (see arguments

for cognitive technologies in a minute...)

Providing IoT systems the ability to self-configure, based on various requirements, and ...

...providing IoT systems the ability to adapt

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SENSORS SENSORS SENSORS SENSORS

PATIENT is near the FRIDGE

CAR is near the HOUSE

PATIENT is driving the CAR

objects reuse

across domains

KitchenPresDetect PatientStatusDetect

Easy for us...not for a “dumb” computer...

the need for cognitive technologies

• iCore Composite Virtual Object (CVO)

– aggregation of simple sensing capability

– self-maintenance (service maintained in case of

failure) increased sensing granularity needed!failure) increased sensing granularity needed!

– System Knowledge

• what is available to meet reqs?

“smart but not so much...”

ability to select alternatives based on

what metadata we put in the extra VO

containers

CT 1

the need for cognitive technologies

• iCore Service Level and overall Cognitive

Management Framework

CT 2,3

distributed

sensing

sight

DATA / INFORMATION OVERLOAD

<

IF “crash”

THEN “alertRSA”

smell

sight hearing

touch

centralised

sensing

>CT 2

the need for cognitive technologies

• factoring “smart logic algorithms” out of developers

concerns

– IF “crash” THEN “alertRSA”

– “crash” (IF VO_x = TRUE THEN crash := TRUE)

– (IF VO_x = TRUE AND VO_y = TRUE THEN crash := TRUE)

TAG:

crash

detect

VO_x

TAG:

crash

detect

VO_yIF (VO_x = TRUE) AND (VO_y = TRUE)

THEN crash := TRUE

IF VO_x = TRUE

THEN crash := TRUE

IF (VO_x > TH_x) AND (VO_y > TH_y)

THEN crash := TRUE

factor out cognitive technologies

CT 2

• iCore community: foster “ready meals” for IoT apps

the need for cognitive technologies

• rather than for the selection of appropriate templates,

here focus is on refinement of selected one according

to observed system-reality matching

• Real-World-Knowledge “growing”

TAG:

crash

detect

VO_x

TAG:

crash

detect

VO_yIF (VO_x > TH_x) AND (VO_y > TH_y)

THEN crash := TRUE

CT 3

assess

QUALITY of

PREDICTION

REFINE

TH_x, and TH_y

iCore and Cognitive Technologies

• CVO Level “system knowledge” – SLA-driven VO selection / maintainance

– semantic enrichment � semantic-based reasoning

– selection by relevance to the needs of the application

• deal with data / information overload– template select

Summary

CT 1

– template select

– given VO / CVO “types” find best algorithms that combine these for desired output

• deal with data / information overload– learn and predict

– given an algorithm, tweak parameters to better align iCoresystem behaviour to the observed real situation

– Real World Knowledge (RWK) “growing”

CT 2

CT 3

main envisaged applications of iCore results

• smart-cities and IoT-based monitoring

[REF1] P. Vlacheas, R. Giaffreda et al. "Enabling Smart Cities Through

a Cognitive Management Framework for the Internet of Things“,

to appear in IEEE Communications Magazine - Special Issue onto appear in IEEE Communications Magazine - Special Issue on

Smart Cities (June 2013)

[REF2] 7th May 2013

The Sensing Smart City workshop

The Internet of Things evolution timeline

The Dumb IoT The Craft IoT The Cognitive IoT

YESTERDAY TODAY TOMORROW

Bear with us, we are building it!

thank you!

iCore Website

www.iot-icore.eu

Contacts:

Raffaele Giaffreda

raffaele.giaffreda@create-net.org

3 yrs EU FP7 Integrated Project

(started 1st Oct 2011)

20 Partners with strong industrial

representation

8.7mEur EU Funding

EU + China and Japan

ID Card

Japan

raffaele.giaffreda@create-net.org

Abdur Rahim

abdur.rahim@create-net.org

EU + China and Japan