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Supporting all-IP IoT
with Virtual Objectswith Virtual Objects
Raffaele Giaffreda (CREATE-NET)
EU FP7 iCore Project Coordinator
FIA Dublin – 7th May 2013
a bit of IoT infographics...
BOSCH
7 bln connected devices by 2015
SAP
24 bln connected devices by 2020
INTEL
31 bln connected devices by 2020
CISCO
37-50 bln connected devices by 2020
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
...Tiny but Powerful devices
what’s beyond IP connectivity?
The Internet parallel...
HTTP/WWW
search engines
connect your info
TCP/IP
HTML
represent info / aggregate info
WWW
personalised knowledge
collections, blogs...
The Semantic Web
find info
VALUE!
IoT, what’s beyond IP connectivity?
early stages for the IoT...
HTTP/WWW
hundreds of bespoke
IoT applications
The Semantic Web
VALUE!
personalised knowledge
collections, blogs...
represent info / aggregate info
search engines
connect your info
TCP/IP
HTML
WWW
find info
object
today
HUMAN
MACHINE
IoT innovation potential...
“Innovation”: one
can focus on apps!!!
OBD
On Board Diagnostics
MACHINE
HUMAN
lesson #1
• connect your objects, unlock value
siloed and bespoke IoT applications
APPS
HOUSE
APPS
FRIDGE
APPS
PATIENT
APPS
PATIENT
APPS
APPS
APPS
APPS
APPS
APPS
DATA / INFORMATION OVERLOAD, BUT...
CAR
SENSORS
HOUSE
SENSORS FRIDGESENSORS
PATIENT
SENSORS
PATIENT
SENSORS
PATIENT
SENSORS
PATIENT
SENSORS
PATIENT
SENSORS
PATIENT
SENSORS
APPS
PATIENT
SENSORS
APPS
TRUCK
SENSORS
APPS
IF A WELL-DEFINED INTERFACE INTO CAR SENSORS BRINGS SUCH POTENTIAL...
APPS
HOUSE
APPS
FRIDGE
APPS
PATIENT
APPS
PATIENT
APPS
APPS
APPS
APPS
APPS
APPS
CAR
HOUSE
FRIDGE
PATIENT
PATIENT
SENSORS
PATIENT
SENSORS
PATIENT
SENSORS
PATIENT
SENSORS
PATIENT
SENSORS
APPS
PATIENT
SENSORS
APPS
TRUCK
SENSORS
APPS
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
Abstraction
ICT objects
(heterogeneous world)
Sensors and actuators
Proprietary services IoT services
Associated physical objects
Abstraction
17
what ingredients?
• common interfaces to interact with
objects (i.e. REST)
• + extra containers for metadata
• let the systems know what the object• 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.
replicate what the HTML (HyperText Markup Language)
has done for simply connected info
“I am a webpage and I talk about Paris history”
WHAT ARE VOs GOOD FOR?
• OBJECTS PROXIMITY
– automated selection “by relevance” (see arguments
for cognitive technologies in a minute...)
• OBJECTS REUSE• OBJECTS REUSE
– reuse across different apps, increase availability,
hence, increase monitoring / sensing granularity
• OBJECTS MGMT
– i.e. energy management, contextualised sensing
(accuracy vs. sensing frequency) etc.
Providing IoT systems the ability to self-configure, based on various requirements, and ...
Ability to dynamically aggregate
ProxShape(QUADRILATERAL)
ProxColour(GREEN)
ProxColour(RED)
APP / SRV
APP / SRV
APP / SRV
Cognitive.prep
Cognitive.select
VO
...providing IoT systems the ability to adapt
CAR
APPS
HOUSE
APPS
FRIDGE
APPS
PATIENT
APPS
HOUSE
FRIDGE
PATIENT
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...
lesson #2
• connect your objects across domains, unlock
further value
Internet vs. IoT
• a page + a page + a page...connect info
• represent info – HTML
• aggregate info – hyperlink
• a (sensor) feed + a feed + a feed...
• represent feeds – VO
• aggregate feeds – CVO
VALUE?
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
use of cognitive technologies in the IoT
• Build models for Real World
Knowledge representation
• Help predict based on observation
of past (training)
• Assist rather than replace human• Assist rather than replace human
decisions
• Personalise the behaviour of IoT
systems to tailor user changing
needs and situations
Putting it all together, iCore Cognitive Mgmt Framework
Service Level
Service
Execution
Request
Service
Request
select, notification
Service
Request
Alert / Predict
ACK
Application
CVO Level
VO Level
CVO/VO
Execution
Request
RWO
interactions
select,
deploy
(bind), run
set of
running
processes
satisfying
Service
Request
notification
according
to service
template
ACK
RWOs
dynamic
binding
Internet vs. IoT
• find info
• personalised knowledge collections, blogs as
“ready info meals”...
• find VOs / CVOs
• personalised IoT services, applications that
learn how to assist users
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_y
IF (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”
• Learning and adaptation to the users preferences• Learning and adaptation to the users preferences
TAG:
crash
detect
VO_x
TAG:
crash
detect
VO_y IF (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 Architecture, RWK grow and SK grow
Service Level
Service
Execution
Request
Service
Request
select,
Delta (RWK-RW)
ACK
Application
RWK
Model
tweak
parameters /
algorithms
CVO Level
VO Level
CVO/VO
Execution
Request
RWO
interactions
select,
deploy
(bind), run
set of
running
processes
satisfying
Service
Request
ACK
RWOs
RWK
SK
Delta (SK-S)
SK
Model
Personalised RWK and SK...
in one slide
srv templates
(RWK models)
srv templates
(RWK models)
srv templates
(RWK models)
CVO templates
(SK models)
instantiation of same cognitive algorithms linking sensors
gets tailored with usage to produce outputs and alerts that
match user preferences, situation, infrastructure context
CVO templates
(SK models)
CVO templates
(SK models)
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 iCore
system behaviour to the observed real situation
– Real World Knowledge (RWK) “growing”, adaptation to user
preferences
CT 2
CT 3
The Internet of Things evolution timeline
The Dumb IoT The Craft IoT The Cognitive IoT
YESTERDAY TODAY TOMORROW
The Dumb Internet The Craft Internet The Technicolor Internet
The Dumb IoT The Craft IoT The Cognitive IoT
Bear with us, we are building it!
references
• iCore application in 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] IERC Newsletter April 2013 – foreword by R. Giaffreda
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

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20130503 iCore at calipso workshop fia dublin

  • 1. Supporting all-IP IoT with Virtual Objectswith Virtual Objects Raffaele Giaffreda (CREATE-NET) EU FP7 iCore Project Coordinator FIA Dublin – 7th May 2013
  • 2. a bit of IoT infographics...
  • 3. BOSCH 7 bln connected devices by 2015
  • 4. SAP 24 bln connected devices by 2020
  • 5. INTEL 31 bln connected devices by 2020
  • 6. CISCO 37-50 bln connected devices by 2020
  • 7. transistor density / space efficiency Turing’s Pilot ACE: Automatic Computing Engine
  • 8. bandwidth / spectral efficiency
  • 9. Space Efficiency + Spectral Efficiency = MAZE OF TINY, CONNECTED THINGS Trend: more and more widespread sensing and monitoring data available ...Tiny but Powerful devices
  • 10. what’s beyond IP connectivity? The Internet parallel... HTTP/WWW search engines connect your info TCP/IP HTML represent info / aggregate info WWW personalised knowledge collections, blogs... The Semantic Web find info VALUE!
  • 11. IoT, what’s beyond IP connectivity? early stages for the IoT... HTTP/WWW hundreds of bespoke IoT applications The Semantic Web VALUE! personalised knowledge collections, blogs... represent info / aggregate info search engines connect your info TCP/IP HTML WWW find info object today
  • 12. HUMAN MACHINE IoT innovation potential... “Innovation”: one can focus on apps!!! OBD On Board Diagnostics MACHINE HUMAN
  • 13. lesson #1 • connect your objects, unlock value
  • 14. siloed and bespoke IoT applications APPS HOUSE APPS FRIDGE APPS PATIENT APPS PATIENT APPS APPS APPS APPS APPS APPS DATA / INFORMATION OVERLOAD, BUT... CAR SENSORS HOUSE SENSORS FRIDGESENSORS PATIENT SENSORS PATIENT SENSORS PATIENT SENSORS PATIENT SENSORS PATIENT SENSORS PATIENT SENSORS APPS PATIENT SENSORS APPS TRUCK SENSORS APPS
  • 15. IF A WELL-DEFINED INTERFACE INTO CAR SENSORS BRINGS SUCH POTENTIAL... APPS HOUSE APPS FRIDGE APPS PATIENT APPS PATIENT APPS APPS APPS APPS APPS APPS CAR HOUSE FRIDGE PATIENT PATIENT SENSORS PATIENT SENSORS PATIENT SENSORS PATIENT SENSORS PATIENT SENSORS APPS PATIENT SENSORS APPS TRUCK SENSORS APPS SENSORS SENSORS SENSORS SENSORS
  • 16. iCore concepts • Virtual Object • Composite Virtual Object • Service / User Level• Service / User Level
  • 17. 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 Abstraction ICT objects (heterogeneous world) Sensors and actuators Proprietary services IoT services Associated physical objects Abstraction 17
  • 18. what ingredients? • common interfaces to interact with objects (i.e. REST) • + extra containers for metadata • let the systems know what the object• 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. replicate what the HTML (HyperText Markup Language) has done for simply connected info “I am a webpage and I talk about Paris history”
  • 19. WHAT ARE VOs GOOD FOR? • OBJECTS PROXIMITY – automated selection “by relevance” (see arguments for cognitive technologies in a minute...) • OBJECTS REUSE• OBJECTS REUSE – reuse across different apps, increase availability, hence, increase monitoring / sensing granularity • OBJECTS MGMT – i.e. energy management, contextualised sensing (accuracy vs. sensing frequency) etc. Providing IoT systems the ability to self-configure, based on various requirements, and ...
  • 20. Ability to dynamically aggregate ProxShape(QUADRILATERAL) ProxColour(GREEN) ProxColour(RED) APP / SRV APP / SRV APP / SRV Cognitive.prep Cognitive.select VO
  • 21. ...providing IoT systems the ability to adapt CAR APPS HOUSE APPS FRIDGE APPS PATIENT APPS HOUSE FRIDGE PATIENT 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...
  • 22. lesson #2 • connect your objects across domains, unlock further value
  • 23. Internet vs. IoT • a page + a page + a page...connect info • represent info – HTML • aggregate info – hyperlink • a (sensor) feed + a feed + a feed... • represent feeds – VO • aggregate feeds – CVO
  • 25. 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
  • 26. the need for cognitive technologies • iCore Service Level and overall Cognitive Management Framework CT 2,3
  • 27. use of cognitive technologies in the IoT • Build models for Real World Knowledge representation • Help predict based on observation of past (training) • Assist rather than replace human• Assist rather than replace human decisions • Personalise the behaviour of IoT systems to tailor user changing needs and situations
  • 28. Putting it all together, iCore Cognitive Mgmt Framework Service Level Service Execution Request Service Request select, notification Service Request Alert / Predict ACK Application CVO Level VO Level CVO/VO Execution Request RWO interactions select, deploy (bind), run set of running processes satisfying Service Request notification according to service template ACK RWOs dynamic binding
  • 29. Internet vs. IoT • find info • personalised knowledge collections, blogs as “ready info meals”... • find VOs / CVOs • personalised IoT services, applications that learn how to assist users
  • 30. 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_y IF (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
  • 31. 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” • Learning and adaptation to the users preferences• Learning and adaptation to the users preferences TAG: crash detect VO_x TAG: crash detect VO_y IF (VO_x > TH_x) AND (VO_y > TH_y) THEN crash := TRUE CT 3 assess QUALITY of PREDICTION REFINE TH_x, and TH_y
  • 32. iCore Architecture, RWK grow and SK grow Service Level Service Execution Request Service Request select, Delta (RWK-RW) ACK Application RWK Model tweak parameters / algorithms CVO Level VO Level CVO/VO Execution Request RWO interactions select, deploy (bind), run set of running processes satisfying Service Request ACK RWOs RWK SK Delta (SK-S) SK Model Personalised RWK and SK...
  • 33. in one slide srv templates (RWK models) srv templates (RWK models) srv templates (RWK models) CVO templates (SK models) instantiation of same cognitive algorithms linking sensors gets tailored with usage to produce outputs and alerts that match user preferences, situation, infrastructure context CVO templates (SK models) CVO templates (SK models)
  • 34. 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 iCore system behaviour to the observed real situation – Real World Knowledge (RWK) “growing”, adaptation to user preferences CT 2 CT 3
  • 35. The Internet of Things evolution timeline The Dumb IoT The Craft IoT The Cognitive IoT YESTERDAY TODAY TOMORROW The Dumb Internet The Craft Internet The Technicolor Internet The Dumb IoT The Craft IoT The Cognitive IoT Bear with us, we are building it!
  • 36. references • iCore application in 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] IERC Newsletter April 2013 – foreword by R. Giaffreda
  • 37. 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