Tutorial on Internet of Thing (IoT) Paradigm in Consumer Applications

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)
1

CCNC 2014 Tutorial - 10 Jan 2014

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.
2


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

3


Part I. Broad introduction on Internet of Things

4


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.

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

6


BOSCH

7 bln connected devices by 2015

SAP


INTEL


CISCO

37-50 bln connected devices by 2020
99.4%, 1.5tln unconnected!

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/

13


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

how did we get here???

VISION
Technology trends
+
Internet evolution
15


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


transistor density / space efficiency

Turing’s Pilot ACE: Automatic
Computing Engine

bandwidth / spectral efficiency

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


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

The Internet parallel
1982

1990

today

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

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

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
Internet of people

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?? :-)

let’s start digging deeper
Applications

Middleware
IOT

Virtualise
Connectivity
Devices

25


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


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

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

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

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.
30


Nice apps, how are they realised?

31


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


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.

33


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!

34


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.
35


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

36


IoT-A Architecture Reference Model
Minimum set of “functionality groups”
application
IoT service
communication
device

37


IoT-A functional model

38


let’s put that in practice...

39


how to create a vertical IoT application
connect a device to a server

40


Open Source Hardware Platforms

ARDUINO

OpenPicus Flyport

41


log / read monitored data, do stg with it

objects

42


link it to an application and BINGO! here is you
“Hello IoT World!” first application...however:

objects
application

43


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


IoT Platforms

45


What do these IoT Platforms have in
common?
value-add
(lock-in)
features
objects

IoT
platform
applications

objects

IoT
platform
applications

46


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


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.

Part II – IoT Protocols
handover to VP slideset

49


Part III – Open Horizontal Platforms for IoT
Michael Koster slideset

50


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


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

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

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

Introducing the VO concept
talked about
“virtualisation” earlier
DISCOVER & SELECT
USE & MANAGE

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

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

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

VO Information Model – semantic search

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

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?

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

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

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

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

http://readwrite.com/2013/06/14/whats-holding-up-the-internet-of-things

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

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

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...

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

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...

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

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

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

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

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)

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

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

the story so far...bottom-up

what’s in here?
how to ensure user
friendliness and wide
adoption...

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

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...

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

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

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

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

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 info: http://www.iot-icore.eu/attachments/article/66/iCore_FuNeMS%2713_ALU.pdf

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

Trento Hospital S. Chiara CREATE-NET, Trilogis +
ZIGPOS

Part V. Part V – Academic research issues
handover to VP

91


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