Cognitive Management of Objects and
Applications for the Internet of Things
Raffaele Giaffreda (CREATE-NET)
Keynote at GII...
Outline
• Introduction, IoT vs. the Internet
• object virtualisation – separation between object data and
object mgmt conc...
transistor density / space efficiency

Turing’s Pilot ACE: Automatic
Computing Engine
bandwidth / spectral efficiency
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
others...

Source: IDATE
some initial considerations
•
•
•
•
•

IoT will be BIG
problems
human in the loop
configuring, using, maintaining
handling...
the Internet parallel
• imagine the Internet with no browser, no
plugins
• collection of bespoke, non interoperable
conten...
The Internet parallel
HTTP/WWW

search engines

HTML

represent info / aggregate info
connect your info
TCP/IP

WWW

find ...
The Internet parallel
early stages for the IoT...
HTTP/WWW

search engines

HTML

object
connect your info

represent info...
Internet vs. Internet of Things
• files vs. objects
• static memory cells vs. energy standalone
units
• need to separate d...
Introducing Virtual Objects
the VO concept
Exposed APIs

•

VO exposes several APIs to the upper
layers

VO SW agent host

– Features, functionalities...
what do VOs achieve: logical level
Application: pure function

VO Front
End

VO Front
End

VO Back
End:
Net Driver

VO Bac...
fostering automation - discovery
• description associated with an IoT Object, it better be
machine readable
• i.e. semanti...
VO Information Model – semantic
search
Examples (energy efficiency for
sensors)
• besides discovering and selecting
• virtual representative “takes the heat off”...
added value besides sensing efficiency

HUMAN
MACHINE

cars increasingly more
complex
OBD
increasing competition On Board ...
added value besides sensing
efficiency – Innovation potential
we make “machines” step-in, assisting us!

HUMAN
MACHINE

“I...
the story so far...
• increasing number of objects
• discovery and self-management of objects
• connect and virtualise you...
DATA / INFORMATION OVERLOAD, BUT...

siloed and bespoke IoT applications

SENSORS

SENSORS

SENSORS

SENSORS

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

SENSORS

SENSORS

SENSORS

SENSORS

APPS
APPS
APPS
...
of course that’s a dream far from
becoming true...

http://readwrite.com/2013/06/14/whats-holding-up-the-internet-of-thing...
the IoT standardisation jungle
M2M
Real-World Knowledge Model (RDF Concepts & Facts)

Service Templates
Repository

SES

A...
some (good) candidates
• imagine the Internet with no browser, no plugins
• collection of bespoke, non interoperable conte...
fostering interoperability
• at service level (ESBs)
• at communication level (PUB/SUB MQTT bus)
• at device level (GSN)
•...
useful ingredients?
• common interfaces to interact with
objects (i.e. REST)
• + extra containers for metadata
• let the s...
the story so far...
• increasing number of objects
• discovery and self-management of objects
• connect and virtualise you...
once achieved the means to access
an objects as a service...
• object redundancy would allow me to cope with resource
cons...
Introducing the CVO
CVO concept allows for approximate services...

PATIENT

APPS

FRIDGE

APPS

HOUSE

APPS

CAR

APPS

SENSORS

SENSORS

SEN...
CVOs allow Automatic Composition

CVOType 1

CVO 1

FIND

VOType :: Temp sensor
getTemp()

Subject to constraints:
- Dist ...
CVO templates
• factoring “smart logic algorithms” out of users /
developers concerns
– IF “crash” THEN “alertRSA”
– “cras...
workflow-based SEP for CVOs
Car’s sensors/actuators

courtesy of Michele Stecca (M3S)
more info: http://www.slideshare.net...
Event based CVO execution
CVO Container

Observer
Observer

CVO

CVO

CVO

CVO

Machine Learning
extensions

CEP engine
Ev...
Internet vs. IoT
• a page + a page + a page...connect info
• represent info – HTML
• aggregate info – hyperlink
• a (senso...
the story so far...bottom-up
what’s in here?
user friendliness and
wide adoption...
the story so far...
•
•
•
•

increasing number of objects
discovery and self-management of objects
connect and virtualise ...
a ‘top-down’ view
• routine jobs: water the plants, feed the fish,
take my pills, track sent items etc.
• there are object...
unlocking a huge potential
patterns exist ...

CVOs

data

data
data

H/W

data

VOs

data
data

data data
data
data

data...
it’s a complex IoT world...
the need for cognitive technologies
• rather than for the selection of appropriate templates,
here focus is on refinement ...
Real World and System Knowledge
models
interpret
data

Real World Knowledge
(RWK)
Models
derive patterns of ...
presence

...
Cognitive Inside where and why...
• Service Level: gather data relate to actions /
situations
• support users (OBSERVE – L...
Some examples please?
• tracking cars in a smart city
• medical equipment tracking and asset
management
tracking cars in a smart city

Best demo
award at
FuNeMS 2013

courtesy of Marc Roelands (Bell Labs – Alcatel Lucent)
more...
tracking medical equipment
5

Execute

3

Validate

Database of location
information(spatial &
temporal) of objects

2a

I...
Trento Hospital S. Chiara
Trilogis + ZIGPOS
IoT, Cloud and Big Data
the challenges ahead...
• Big data: “big” relates to the huge number of data
sources
– have data, ...
Conclusions
•
•
•
•
•
•
•
•
•

increasing number of objects
discovery and self-management of objects
connect and virtualis...
Further info / links
[REF1] IERC April 2013 Newsletter – Foreword (see THIS LINK)
[REF2] P. Vlacheas, R. Giaffreda et al. ...
Thank you!
Raffaele Giaffreda
Smart IoT (RIoT) Research Area Head
(CREATE-NET)
EU FP7 iCore Project Coordinator
raffaele.g...
Backup slides
the iCore Architecture

iCore User

User Profiling

Real World Knowledge/Model

Natural Language
Processing
iCore User
Pre...
Cognitive Inside – take-away messages
more dependable IoT

(RWK)
Models

support users of future
Smart Cities applications...
Dublinked initiative

IBM Research Ireland
mash-up data across domains

build models and predict!
personalised journey tip...
iCore ID
ID Card

3 yrs EU FP7 Integrated Project
(started 1st Oct 2011)
20 Partners with strong industrial
representation...
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20131031 giis 2013 keynote r.giaffreda

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This is the presentation supporting the invited keynote I gave at the IEEE ComSoc 5th Global Information Infrastructure and Networking Symposium GIIS 2013

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20131031 giis 2013 keynote r.giaffreda

  1. 1. Cognitive Management of Objects and Applications for the Internet of Things Raffaele Giaffreda (CREATE-NET) Keynote at GIIS conference Trento, 31 Oct 2013
  2. 2. Outline • Introduction, IoT vs. the Internet • object virtualisation – separation between object data and object mgmt concerns • overview of IoT standardisation activities • interoperability and objects as services – IoT reliability and resilience • a top-down perspective on the IoT – user friendliness, wide adoption • Real World Knowledge modelling and use of cognitive technologies in IoT • examples of ongoing trials • conclusions
  3. 3. transistor density / space efficiency Turing’s Pilot ACE: Automatic Computing Engine
  4. 4. bandwidth / spectral efficiency
  5. 5. a bit of IoT infographics...
  6. 6. BOSCH 7 bln connected devices by 2015
  7. 7. SAP 24 bln connected devices by 2020
  8. 8. INTEL 31 bln connected devices by 2020
  9. 9. CISCO 37-50 bln connected devices by 2020
  10. 10. others... Source: IDATE
  11. 11. some initial considerations • • • • • IoT will be BIG problems human in the loop configuring, using, maintaining handling huge amounts of data produced
  12. 12. the Internet parallel • imagine the Internet with no browser, no plugins • collection of bespoke, non interoperable content specific applications enabling access and visualisation of connected files
  13. 13. The Internet parallel HTTP/WWW search engines HTML represent info / aggregate info connect your info TCP/IP WWW find info personalised knowledge collections, blogs... VALUE! The Semantic Web
  14. 14. The Internet parallel early stages for the IoT... HTTP/WWW search engines HTML object connect your info represent info / aggregate info find info WWW personalised knowledge collections, blogs... TCP/IP VALUE! today The Semantic Web
  15. 15. Internet vs. Internet of Things • files vs. objects • static memory cells vs. energy standalone units • need to separate data source from data mgmt and operations • objects virtualisation
  16. 16. Introducing Virtual Objects
  17. 17. the VO concept Exposed APIs • VO exposes several APIs to the upper layers VO SW agent host – Features, functionalities and resources can be re-used • • VO APIs Cognitive control enabled by exposing APIs which can be used to optimize the behaviour of the ICT object VO SW agent may or may not be installed on the ICT object VO SW agent – Depends on ICT object capabilities • • Association management between ICT and non-ICT is a real challenge! RESILIENCE ASPECTS (remote) proprietary API calls ICT object ICT APIs ICT object processes Association 17 A simple example: VO SW agent host = laptop with Zigbee dongle ICT object = Zigbee temperature meter non-ICT object = room non-ICT object
  18. 18. what do VOs achieve: logical level Application: pure function VO Front End VO Front End VO Back End: Net Driver VO Back End: Net Driver iCore FW VO Front End VO Back End: Net Driver VO Back End: RWO Driver Gateway RWO1 18
  19. 19. fostering automation - discovery • description associated with an IoT Object, it better be machine readable • i.e. semantic enrichment based on info model for semanticbased selection • what is this good for? – selection “by relevance”: performance and “selection quality” is dependent upon combination of enrichment + algorithm that exploits it... – assessment of “proximity” is a prerequisite in achieving more automatic and scalable solutions
  20. 20. VO Information Model – semantic search
  21. 21. Examples (energy efficiency for sensors) • besides discovering and selecting • virtual representative “takes the heat off” real sensors becoming their actual “manager” – – – – energy efficiency reuse resilience self-x for constrained resource devices • conflict resolution (actuators) • Examples – compression algorithms, data caching, pub/sub schemes, rules for self-x management
  22. 22. added value besides sensing efficiency HUMAN MACHINE cars increasingly more complex OBD increasing competition On Board Diagnostics for owner’s attention what happens when it becomes easier and easier to tap into object produced data?
  23. 23. added value besides sensing efficiency – Innovation potential we make “machines” step-in, assisting us! HUMAN MACHINE “Innovation”: one can focus on apps!!! MACHINE HUMAN OBD On Board Diagnostics
  24. 24. the story so far... • increasing number of objects • discovery and self-management of objects • connect and virtualise your objects, unlock value • no mention of application domains...
  25. 25. DATA / INFORMATION OVERLOAD, BUT... siloed and bespoke IoT applications SENSORS SENSORS SENSORS SENSORS APPS APPS APPS APPS APPS APPS APPS PATIENT PATIENT PATIENT PATIENT PATIENT PATIENT TRUCK PATIENT APPS FRIDGE APPS HOUSE APPS CAR APPS SENSORS SENSORS SENSORS SENSORS SENSORS SENSORS SENSORS
  26. 26. IF A WELL-DEFINED INTERFACE INTO CAR SENSORS BRINGS SUCH POTENTIAL... SENSORS SENSORS SENSORS SENSORS APPS APPS APPS APPS APPS APPS APPS PATIENT PATIENT PATIENT PATIENT PATIENT PATIENT TRUCK PATIENT APPS FRIDGE APPS HOUSE APPS CAR APPS SENSORS SENSORS SENSORS SENSORS SENSORS SENSORS SENSORS
  27. 27. of course that’s a dream far from becoming true... http://readwrite.com/2013/06/14/whats-holding-up-the-internet-of-things
  28. 28. the IoT standardisation jungle M2M Real-World Knowledge Model (RDF Concepts & Facts) Service Templates Repository SES API User Characterisation Situation Projection Service Request Analysis Learning Mechanisms Situation Recognition ITU-T FG Distraction Situation Detection ISO/IEC JTC1 WG7 M2M RDF Rules Inference Engine Intent Recognition M2M API EPCGlobal IoT-GSI Authentication W3C PROV PROV-DM / PROV-O / PROV-AQ / PROV-LINK M2M ITU-T CVO Registry Access Control CLOUD W3C PROV PROV-DM /PROV-O / PROV-AQ / PROV-XML Orchestration / Workflow Management Approximation & Reuse Opportunity Detection Authentication M2M LWM2M Access Control VO Registry 3GPP SPS SES W3C PROV PROV-DM /PROV-O / PROV-AQ / PROV-XML SAS ITU-T IoT-GSI M2M CVO Execution Request SOS LWM2M VO Factory WNS 3GPP ISO/IEC JTC1 WG7 VO VO VO VO VO SensorML LWM2M CoAP ISO/IEC JTC1 WG7 VO Templates Repository Device manufactu rer Actuator VO Management Unit SPS 3GPP MQTT SPS VO Lifecycle Manager Resource Optimisation M2M LWM2M VO ISO/IEC Back End: RWO Driver ITU-T JTC1 WG7 FG M2M EPCGlobal 3GPP GTW/Controller ………….. Sensor LWM2M VO Front End VO VO VO VO VO ITU-T IoT-GSI MQTT GTW/Controller Resource VO Container (WS host) Resource Actuator Quality Assurance ITU-T FG Distraction SAS ITU-T FG M2M Coordination Performance Management SOS CVO CVO CVO CVO CVO AQ / PROV-CONSTRAINT CVO Lifecycle Manager CLOUD CVO Container (Execution) SOS LWM2M CVO Management Unit CVO CVO Situation Observer CVO W3C PROV Situation Observer CVO PROV-DM / PROV-O / PROV- Situation Observer Situation Observer ….. API SSN-XG Installer/ User O&M Installs Sensor/Ac EPCGlobal tuator Devices M2M ITU-T IoT-GSI SPS SOS Learning Mechanisms System Knowledge Model SIR CoAP CSW CLOUD Service Execution Request Learning Statistics Real-World Information DB W3C PROV PROV-DM /PROV-O / PROV-AQ / PROV-XML CVO Factory CVO LWM2M Composition Engine CVO Templates Repository SOR Semantic Query Matcher Queried Fact Collector W3C PROV PROV-DM /PROV-O / PROV-AQ / PROV-XML Data Processing Domain Expert / Developer Authentication Service Analysis W3C PROV PROV-DM / PROV-O / PROVAQ / PROV-CONSTRAINT Sensor courtesy of Panagiotis Vlacheas and Vera Stavroulaki (Piraeus University ) Coordination Data Manipulation / Reconciliation ITU-T IoT-GSI Authentication Situation Classification Administration & Management I/F Domain Expert / Knowledge Engineer Authentication Situation Awareness ITU-T FG M2M Service Request (SPARQL) Natural Language Processing (NLP) ….. API P1723 GUI Service Requester (Technology Agnostic) Authentication
  29. 29. some (good) candidates • imagine the Internet with no browser, no plugins • collection of bespoke, non interoperable content specific applications enabling access and visualisation of connected files an IP based web services view from Sensinode Courtesy of Zach Shelby (Sensinode) http://www.iot-week.eu/presentations/thursday/02_Shelby-IoT-Smart-Cities.pdf
  30. 30. fostering interoperability • at service level (ESBs) • at communication level (PUB/SUB MQTT bus) • at device level (GSN) • no silver bullet...a lot of it will depend on application context...
  31. 31. useful ingredients? • common interfaces to interact with objects (i.e. REST) • + extra containers for metadata • 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. “I am a webpage and I talk about Paris (city of France) history” take inspiration from HTML and the Semantic Web Integration at “application level” with all pros and cons associated with it
  32. 32. the story so far... • increasing number of objects • discovery and self-management of objects • connect and virtualise your objects, unlock value • interoperability across application domains and reliability still big issues...
  33. 33. once achieved the means to access an objects as a service... • object redundancy would allow me to cope with resource constraint nature of objects as well as with the diversity of interfaces – if I had a bunch of VO temp objects to chose from I would be much more likely to tell you what the temperature is... • semantic enrichment allows me to find alternatives, to foster object reuse and achieve service approximation concepts • here we start entering more the “cognitive-inside” IoT object management territory • having a logic for choosing the appropriate Virtual Objects according to the application expectations • having the means to easily connect objects together in a more or less complex graph (CEPs, PUB/SUB channels) • features of Composite Virtual Objects and associated “CVO Templates” cognitive mash-ups of semantically interoperable VOs (and their offered services) which render services matching the application requirements
  34. 34. Introducing the CVO
  35. 35. CVO concept allows for approximate services... PATIENT APPS FRIDGE APPS HOUSE APPS CAR APPS SENSORS SENSORS SENSORS SENSORS PATIENT is driving the CAR CAR is near the HOUSE PATIENT is near the FRIDGE objects reuse across domains KitchenPresDetect PatientStatusDetect
  36. 36. CVOs allow Automatic Composition CVOType 1 CVO 1 FIND VOType :: Temp sensor getTemp() Subject to constraints: - Dist (Pos, myPos) < 10m - Not already allocated VOType :: Press sensor getPressure() VOx CP Solver to find VO allocations that satisfies all constraints and minimizes network traffic USE Logic: If getTemp() > 20° and getPressure() > 2bar then NiceWeather leveraging on System Knowledge (i.e. VOx is good and fully charged) to maintain IoT-based services... VOy
  37. 37. CVO templates • factoring “smart logic algorithms” out of users / 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) • “ready meals” for IoT apps VO_x TAG: crash detect VO_y TAG: crash detect factor out cognitive technologies IF VO_x = TRUE THEN crash := TRUE IF (VO_x = TRUE) AND (VO_y = TRUE) THEN crash := TRUE IF (VO_x > TH_x) AND (VO_y > TH_y) THEN crash := TRUE
  38. 38. workflow-based SEP for CVOs Car’s sensors/actuators courtesy of Michele Stecca (M3S) more info: http://www.slideshare.net/steccami/ieee-icin-2011 Open Data (Web)
  39. 39. Event based CVO execution CVO Container Observer Observer CVO CVO CVO CVO Machine Learning extensions CEP engine Event / (C)VO Bus (pub/sub based on MQTT) VO Container Sensor VO Sensor VO Sensor VO Actuator VO Actuator VO courtesy of Walter Waterfeld (Software AG) more info: http://terracotta.org/downloads/universal-messaging
  40. 40. 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
  41. 41. the story so far...bottom-up what’s in here? user friendliness and wide adoption...
  42. 42. the story so far... • • • • increasing number of objects discovery and self-management of objects connect and virtualise your objects, unlock value exploit redundancy pick the most suitable / interoperable / reliable objects • VO / CVO services like Lego bricks fostering innovation from IoT makers • cognitive inside? so far only application-driven matchmaking • ultimate goal: user-friendly IoT services fostering wide adoption
  43. 43. a ‘top-down’ view • routine jobs: water the plants, feed the fish, take my pills, track sent items etc. • there are objects, sensors, actuators • there are people (busy lives, forgetful patients, green fingers vs. fingers that “kill every plant they look after”) • objects can be connected • objects can be mashed-up • create your own IoT apps (this is what IoT makers do) vs. provide some input and have this interpreted so the right actions are set to achieve your goals • make the IoT easy to use and rely upon...
  44. 44. unlocking a huge potential patterns exist ... CVOs data data data H/W data VOs data data data data data data data data data data data data SENSING Real World Objects (RWO) data goldmine and lots of siloed applications interpret data presence derive patterns of ... presence
  45. 45. it’s a complex IoT world...
  46. 46. 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 VO_x TAG: crash detect VO_y TAG: crash detect REFINE TH_x, and TH_y IF (VO_x > TH_x) AND (VO_y > TH_y) THEN crash := TRUE assess QUALITY of PREDICTION
  47. 47. Real World and System Knowledge models interpret data Real World Knowledge (RWK) Models derive patterns of ... presence What are these good for? (SK) Models System Knowledge
  48. 48. Cognitive Inside where and why... • Service Level: gather data relate to actions / situations • support users (OBSERVE – LEARN – REPLACE) – routine jobs (watering plants, feeding the fish, taking pills, switch on/off lights) – non-expert alerts (a fire, a leak, a fault) • provide feedback – improvement of system performance
  49. 49. Some examples please? • tracking cars in a smart city • medical equipment tracking and asset management
  50. 50. tracking cars in a smart city Best demo award at FuNeMS 2013 courtesy of Marc Roelands (Bell Labs – Alcatel Lucent) more info: http://www.iot-icore.eu/attachments/article/66/iCore_FuNeMS%2713_ALU.pdf
  51. 51. tracking medical equipment 5 Execute 3 Validate Database of location information(spatial & temporal) of objects 2a In the demo implementation, location data of objects is simulated RWO parameter reconfiguration recommendations to improve energy efficiency of location sensors 4 Train 4a 6 2 1 7
  52. 52. Trento Hospital S. Chiara Trilogis + ZIGPOS
  53. 53. IoT, Cloud and Big Data the challenges ahead... • Big data: “big” relates to the huge number of data sources – have data, patterns exist – Need to purposefully aggregate data – scaling-up use of machine learning is a challenge... • Cloud: constrained devices and limited scope for data processing – dynamic deployment of data-processing resources on the data-source data-consumer path is a challenge... • IoT Networking: delivery of “object-produced” data – M2M traffic and dynamic deployment of connectivity resources is a challenge...
  54. 54. Conclusions • • • • • • • • • increasing number of objects discovery and self-management of objects connect and virtualise your objects, unlock value exploit redundancy pick the most suitable / interoperable / reliable objects VO / CVO services like Lego bricks fostering innovation from IoT makers cognitive inside: the importance of modelling the Real World Cognitive IoT: user-friendly services fostering wide adoption implementations exploiting iCore project results in real trial settings challenging times ahead!
  55. 55. Further info / links [REF1] IERC April 2013 Newsletter – Foreword (see THIS LINK) [REF2] P. Vlacheas, R. Giaffreda et al. "Enabling Smart Cities Through a Cognitive Management Framework for the Internet of Things“, IEEE Communications Magazine - Special Issue on Smart Cities (June 2013) [REF3] iCore website (www.iot-icore.eu/latest-news) Best Demo Award at FuNeMS 2013
  56. 56. Thank you! Raffaele Giaffreda Smart IoT (RIoT) Research Area Head (CREATE-NET) EU FP7 iCore Project Coordinator raffaele.giaffreda@create-net.org Websites: www.create-net.org/research/research-areas/riot www.iot-icore.eu
  57. 57. Backup slides
  58. 58. the iCore Architecture iCore User User Profiling Real World Knowledge/Model Natural Language Processing iCore User Preferences API Domain Expert / Knowledge Engineer Service Request Service Execution Request RWK Update CVO Template API Design & Store System Knowledge/Model CVO Registry CVO Container (Execution) CVO Templates Repository VO Execution Request API VO Template Repository CVO CVO CVO CVO VO Data Session VO Management Unit VO Registry Device Install er Device manufac turer CVO CVO Situation Observer CVO Factory API Data Processing Domain Expert / Developer CVO Management Unit VO Factory VO VO Container VO VO VO O Real World Objects (RWO) VO Front End VO Back End: RWO Driver API Service Request Analysis Situation Modelling System Administration & Management People modelling System Administration & Management Service Templates Repository API & Store RWK API Design API iCore System Operator
  59. 59. Cognitive Inside – take-away messages more dependable IoT (RWK) Models support users of future Smart Cities applications (routine + alerts) (SK) Models more resilient IoT IoT resilience and fault tolerance more reliable and interoperable IoT IoT reliability and durability through VOs
  60. 60. Dublinked initiative IBM Research Ireland mash-up data across domains build models and predict! personalised journey tips throughout the execution
  61. 61. iCore ID ID Card 3 yrs EU FP7 Integrated Project (started 1st Oct 2011) 20 Partners with strong industrial representation 8.7mEur EU Funding EU + China and Japan Japan

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