Tutorial on Internet of Thing (IoT) Paradigm in Consumer Applications given at 11th IEEE Consumer Communication and Networking Conference 2014 - Las Vegas. R.Giaffreda slides subset
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Tutorial on Internet of Thing (IoT) Paradigm in Consumer Applications
1. Internet Connected Objects for
Reconfigurable Eco-systems
Tutorial on Internet of Thing (IoT) Paradigm
in Consumer Applications
Las Vegas (US), 10 Jan 2014
Raffaele Giaffreda (CREATE-NET)
R. Venkatesha Prasad (TU Delft)
Michael Koster (OSIOT / ARM)
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CCNC 2014 Tutorial - 10 Jan 2014
2. Brief Introduction to the speakers
Raffaele Giaffreda
Smart IoT Research Area Head at CREATE-NET, International
Research Center located in Trento, Italy
iCore EU Project Coordinator, 3 yrs project on Cognitive IoT
Michael Koster (from remote)
From the Open Source Internet Of Things Silicon Valley
community and meetup group. Has been modeling and building
systems in hardware and software for over 20 years. Building the
experimental software IoT Toolkit to further develop high level
interoperability. Joining the ARM IoT research group next week.
R. Venkatesha Prasad
Assistant Professor at Embedded Software group, Delft University
of Technology (Netherlands). He is an active member of TCCN,
IEEE SCC41, and reviewer of many IEEE Transactions and
Elsevier Journals.
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3. Seminar outline (3h 30min)
Part I – Broad introduction to IoT (45min) RG
Part II – Protocols for IoT (20min) VP
Part III – Open Horizontal platforms for IoT (35min) MK
coffee break (30min)
Part IV – the iCore Cognitive IoT, rationale and architecture
(30min) RG
Part V – Academic research issues (50min) VP
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4. Part I. Broad introduction on Internet of Things
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5. Let’s start with a definition...
from the IERC (the European Research Cluster on Internet of Things http://www.internet-of-things-research.eu/)
Internet of Things (IoT) is a dynamic global network infrastructure with selfconfiguring capabilities based on standard and interoperable
communication protocols where physical and virtual “things” have
identities, physical attributes and virtual personalities and use intelligent
interfaces and are seamlessly integrated into the information network.
6. IoT vs. M2M vs. CPS
not clear cut distinction, these terms are often used interchangeably;
however, based on personal experience...
M2M – Machine-to-Machine
TelCo world origins, tied to the network implications of connecting machines
rather than people, explosion of # of connections with limited bit-rate, ETSI is the
main standardisation body); think of telemetry applications
M2M is the glue of the IoT
CPS – Cyber Physical Systems
merging real and virtual (cyber) worlds, focusing on systems that based on duly
sampled representation of the physical world can intervene through digitized
actuators to change behaviours in the physical world; think of car ABS
CPS is the science bricks behind IoT
IoT hailed as a broader concept, where the focus is more on wide
applications
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13. 1 user – many devices
looking at mere numbers and statistics
1950: 2.5bln people on earth; 2050: 9.3bln!
2012: Internet of 2.4 bln connected people
20 devices per person on average...easy to think of 200 or
2000
Picture focusing on
wearable IoT only!!!
http://www.forbes.com/sites/danmunro/2013/05/19/credit-suisse-says-wearable-tech-the-next-big-thing/
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14. take away message
no question about the fact that IoT will be BIG
M2M and CPS will contribute to IoT success
many more people on earth, need for more efficient usage
of resources
however...problems to be solved until we get there:
human in the loop
configuring, using, maintaining
handling huge amounts of data produced
15. how did we get here???
VISION
Technology trends
+
Internet evolution
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16. Marc Weiser vision (1991)
Ubiquitous Computing
The most profound technologies are those that disappear.
They weave themselves into the fabric of everyday life until
they are indistinguishable from it.
Timeline
1990 – WorldWideWeb first browser invented in by Tim
Berners-Lee
the UC vision
1980
16
1990
2000
2010
2020
the IoT timeline
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17. transistor density / space efficiency
Turing’s Pilot ACE: Automatic
Computing Engine
19. technology trends vs. vision
tech maturity
level to realise
vision
how to close this gap?
IDEAL
the UC vision
1980
19
1990
2000
today
2010
2020
the IoT timeline
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20. 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
22. 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
23. 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
management of object-generated-data in the context of wider
applications
need for widely adopted “object browsing” apps and for more
Cognitive and Machine Learning supported technologies (too many
connected objects to expect human-users to consistently leverage
on such wealth)
browser displays content, TCP/IP is the glue, the user’s interest
drives innovative apps (search engines, social networks etc.)
23
no widely adopted solutions for “browsing” the IoT, there will be a
higher proportion of “machines” as end-users for IoT compared to
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Internet of people
24. take away message
From Marc Weiser’s vision to IoT passing by Moore’s and
Cooper’s laws
connecting tiny devices is not an issue anymore
the internet of people will be ready to integrate “things”
lack of unified solutions for consistently tapping into such
technology supported wealth of connected devices
but we are getting there...
good rationale for IoT getting stronger and stronger
as we evolve our modern lifestyles...CES anyone?? :-)
26. got a rough idea about what IoT is now,
why do we need it?
remote monitoring
distributed and accurate sensing
tracking location / presence (inventory, belongings)
tracking usage / conditions
statistics data generation
health, energy, traffic etc.
actuation
human in the loop issue
time-sharing, location-sharing user attention
Car example to reinforce the concept here!
food for thoughts: it’s all about achieving more
efficiency...thousands of objects are around us already...what do we
gain from “connecting them” or having remote access to them???
26
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27. Health Applications
Infant monitoring system
provides parents with real-time information
about their baby's breathing, skin
temperature, body position, and activity
level on their smartphones.
Monitoring an aging family member
Using a wearable alarm button and other
discrete wireless sensors placed around the
home to track your loved one's daily routine
and alerts you to any serious disruptions
detected in their normal schedule
28. Fitness and Smart Spaces Applications
Track your activity levels
Using your smartphone's range of sensors and
connectivity options your device can
automatically monitor your movements,
location, and workouts throughout the day.
Home Energy Monitor
Smart thermostats, real-time weather forecasts,
and the actual activity in your home to control
the heating of the house
Smart outlets allow you to instantly turn on and
off any plugged in device from across the world
or just your living room
29. Structure, Buildings, Systems …
Monitoring Structures
uses wireless sensors embedded within
concrete foundation piles to ensure the
quality and integrity of a structure.
These sensors provide load and event
monitoring to the projects both during
and after its completion.
Environment Monitoring
Water distribution
Air pollution
Wildlife
Disaster warning
30. IoT applications
Companies
http://smartthings.com/ (house)
http://www.automatic.com/ (car)
https://nest.com/ (house)
http://sen.se/ (“mother”)
http://bounceimaging.com/ (emergency & rescue)
pebble (smart watch, personal assistant)
fitbit (personal trainer, fitness, health monitoring)
IBM (smart cities, dublinked)
Cisco (Internet of Everything)
SAMSUNG (smart-devices, home, personal)
CES anyone?
etc. etc.
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31. Nice apps, how are they realised?
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32. what are “things”?
objects
real world things
can be observed / monitored (through a camera, a scale, a
position tag)
sensors
light, acceleration, noise, temperature, heart rate, blood
pressure etc.
actuators
can change status of things
How can we remotely interact with these objects??
32
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33. Connecting to objects
GSM (!) – wide coverage
802.15.4 – low-power, low-rate (ZigBee)
Bluetooth – energy efficient, portable devices
WiFi – widespread, easy
Ethernet – wideband access
Weightless (exploiting white spaces)
etc.
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34. Using “things”
“remotely interact”
communication
create a “virtual counterpart” – objects virtualisation
why?
need for manipulation / interaction
what do we do with Real World Objects? interact with
them using our senses
look for them, see them, know what they are good for
need to recreate that functionality at machine level!
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35. Virtualising objects
objective of achieving more automated and
widely adopted solutions: need to make a clean
break between physical objects and applications /
business logic that use these
virtual representation of an object, if it has well
defined interfaces for interaction, can be easily
reused
semantic enrichment, not easy to achieve on
object / constrained devices is more easily dealt
with within object virtual counterparts sitting on
resource intensive machines (i.e. refer to virtual
machines in the Cloud)
etc.
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36. So, what’s in the middle?
SOUTHBOUND: control and management of objects /
devices
NORTHBOUND: logic to tailor how data becomes
information and useful knowledge to the applications
application specific, but there might be invariants
architectural considerations
an overview of the IoT-A ARM (Architecture Reference
Model)
ref: iot-a EU project www.iot-a.eu
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37. IoT-A Architecture Reference Model
Minimum set of “functionality groups”
application
IoT service
communication
device
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39. let’s put that in practice...
39
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40. how to create a vertical IoT application
connect a device to a server
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41. Open Source Hardware Platforms
ARDUINO
OpenPicus Flyport
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42. how to create a vertical IoT application
connect a device to a server
log / read monitored data, do stg with it
objects
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43. how to create a vertical IoT application
connect a device to a server
log / read monitored data, do stg with it
link it to an application and BINGO! here is you
“Hello IoT World!” first application...however:
objects
application
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44. how to create a vertical IoT application
connect a device to a server
log / read monitored data, do stg with it
link it to an application and BINGO! here is you
“Hello IoT World!” first application...however:
infrastructure intensive exercise (need to store
data, need processing power, need connectivity
etc.)
this is where bespoke applications kits or
HORIZONTAL platforms come in...
44
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46. What do these IoT Platforms have in
common?
value-add
(lock-in)
features
objects
IoT
platform
applications
objects
IoT
platform
applications
46
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47. most of the features of an IoT system
architecture can be factored out...
different companies have opted for
different choices
bespoke, vertical, with/out GW
sen.se
smartthings.com
fitbit.com
platform, horizontal, cloud-based
thingworx
xively
evrythng
47
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48. vertical vs. horizontal
the IoT device, the gateway (if
used), and the application are all
provided and controlled by the
same company.
Open Horizontal IoT Platform
Applications
IOT
V
E
Middleware R
T
I
C
A
L
Virtualise
A
Connectivity P
P
S
Devices
48
little infrastructure in place to
support a horizontal business
The horizontal approach
makes innovation easier
and allows rapid
proliferation of new
applications and
businesses, but it needs
to gain considerable
traction before it can pay
off on its promises.
There need to be a lot of
open gateways in place
before third-party
developers can count on
having an adequate
market to serve.
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49. Part II – IoT Protocols
handover to VP slideset
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50. Part III – Open Horizontal Platforms for IoT
Michael Koster slideset
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51. Part IV – the iCore Cognitive IoT, rationale and
architecture
Applications
IOT
V
E
Middleware R
T
I
C
A
L
Virtualise
Service Level
Composite
Virtual Object
Virtual Object
A
Connectivity P
P
S
Devices
51
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52. the need for Cognitive IoT and EU iCore project
need for cognitive
and machine
learning based
framework to more
automatically deal
with the Interent of
Things without too
much end-user
know-how and
involvement
53. iCore EU-project ID
iCore Website
www.iot-icore.eu
Contacts:
Raffaele Giaffreda
raffaele.giaffreda@create-net.org
Abdur Rahim
abdur.rahim@create-net.org
ID Card
3 yrs EU FP7 Integrated Project
(started 1st Oct 2011)
23 Partners with strong industrial
representation
8.7mEur EU Funding
EU + China, Japan, Korea
54. 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
55. Introducing the VO concept
talked about
“virtualisation” earlier
DISCOVER & SELECT
USE & MANAGE
56. 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
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
Depends on ICT object capabilities
Association management between ICT
and non-ICT is a real challenge!
RESILIENCE ASPECTS
VO APIs
VO SW agent
(remote) proprietary
API calls
ICT object
ICT APIs
ICT
object processes
Association
56
A simple example:
VO SW agent host = laptop with Zigbee dongle
ICT object = Zigbee temperature meter
non-ICT object = room
non-ICT object
57. 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
Gateway
RWO1
57
iCore FW
VO Front
End
VO Back
End:
Net Driver
VO Back
End:
RWO Driver
58. 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
60. 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
61. 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?
62. 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
63. 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
64. IF A WELL-DEFINED INTERFACE INTO CAR
SENSORS BRINGS SUCH INNOVATON 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
65. of course that’s a dream far from becoming true...
http://readwrite.com/2013/06/14/whats-holding-up-the-internet-of-things
66. 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
SPS
3GPP
MQTT
SPS
M2M
VO Lifecycle
Manager
Resource
Optimisation
Coordination
Data Manipulation
/ Reconciliation
ITU-T
IoT-GSI
LWM2M
VO
ISO/IEC Back End: RWO Driver ITU-T
JTC1 WG7
FG M2M
EPCGlobal
3GPP
GTW/Controller
…………..
Sensor
LWM2M
VO Management Unit
VO Front End
VO VO VO
VO VO
ITU-T
IoT-GSI
MQTT
GTW/Controller
Resource
VO Container (WS host)
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
Resource
Actuator
Sensor
courtesy of Panagiotis Vlacheas and Vera Stavroulaki (Piraeus University )
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
67. 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
68. 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...
69. 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
70. the iCore 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...
71. 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
73. 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
74. 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
75. 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
76. 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)
77. 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
78. 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
79. the story so far...bottom-up
what’s in here?
how to ensure user
friendliness and wide
adoption...
80. 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
81. 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...
82. 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
84. 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
85. 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
86. 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
88. 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
89. 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