cognitive iot at first italian iot day 9 april 2013
DESCRIPTION
TRANSCRIPT
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
APPS
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DATA / INFORMATION OVERLOAD, BUT...
CA
R
SENSORS
HO
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SENSORSF
RID
GE
SENSORS
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SENSORS
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SENSORS
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SENSORS
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SENSORS
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SENSORS
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SENSORS
APPS
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SENSORS
APPS
TR
UC
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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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SENSORS SENSORS SENSORS SENSORS
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
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
Abdur Rahim
EU + China and Japan