The document summarizes an IoT conference presentation about current and future trends in the Internet of Things. It notes that the number of connected devices is expected to grow exponentially to 50 billion by 2020, putting substantial demands on communication infrastructure. Current research aims to tackle bottlenecks through distributed edge computing and data processing to avoid overloading networks. Going forward, connected things will be able to socialize their status and integrate with human social networks, enabling spontaneous service provisioning through relevance and proximity-based discovery.

1. IoT Day 2014, Trento
Trento at the centre of the
Italian Internet of Things
Connect your Things!
Don’t be an alien!
organised by @iotitaly
hashtag for this event #trentoiot
iotitaly

2. iCore: smart things for smart applications
Raffaele Giaffreda, IoT Day Italy, TrentoRaffaele Giaffreda, IoT Day Italy, Trento
9th April 2014

3. outline
• the scale of Internet of Things
• the bottlenecks
• some research results
• some future trends• some future trends
• conclusions

5. some numbers
• 9.3bln people on earth by 2050 (70% urban, source WHO) –
2.5bln in 1950
• increasing stress on planet resources
– up to 50% world produced food goes to waste (source IMECHE –
2013)
– 30% water lost in transit (source WHO and various reports)– 30% water lost in transit (source WHO and various reports)
– substantial room for improvement
• age 65+: 516m 2009, 1.53bln by 2050 (source US Census
Bureau) healthcare issues!
• future cities have no choice but to become smart...IoT gives
them the opportunity to sense problems and adjust...
• 50bln connected things by 2020?

6. some more numbers
• BOSCH: 7bln by 2015
• ABI research: 30bln by
2020
• Cisco: 50bln by 2020• Cisco: 50bln by 2020
• Morgan Stanley: 75bln
by 2020

7. wearables at CES2014

10. substantial demands
on communication
infrastructure
substantial demands
for resource
constrained IoT
datafrom50blndevices
infrastructure
substantial demands
on centralised
targeted user
network

11. some research results
pics from IoT Italy Hackathon 2013

12. The Dumb IoT The Craft IoT The Cognitive IoT
YESTERDAY TODAY TOMORROW
from IoT Italy 2013
Bear with us, we are building it!

13. An example...
• “I am hot”
• thermostat lowers temperature
• but...
• energy efficiency (target)• energy efficiency (target)
• light sensor, outdoor temperature (available)
• cognitive IoT system lowers the blinds instead
or it shuts the windows or ...
compare user intervention and
automated system based on templates

14. other examples
• “I am going out of the house”
– switch off lights, water plants, activate iRobot,
close curtains...
• “I am unwell”• “I am unwell”
– monitor health parameters, stream data to doctor,
remind me of taking medicines
• moreover “social” things can discover and
support each other based on relatedness

15. the basics – how do things become
smart?
• acquire and log data
• analyse it and learn patterns
• create predictive models
– anticipate user intentions in routine jobs (water– anticipate user intentions in routine jobs (water
the plants, feed the fish, switch off lights)
– produce non-expert alerts (a leak, a fire, a fault in
a car etc.)
how is that achieved?

16. iCore Real World and System
Knowledge models
Real World Knowledge
(RWK)
Models
derive patterns of ...
interpret
data
data goldmine
System Knowledge
(SK)
Models
presence
derive patterns of ...
What are these
good for?
data goldmine

17. Service Execution
Domain
Expert /
Developer
Service-
requesting
Actor
RWK Model
Adp.
Hypothesis
ML trained
algorithm
Service Request
(maintain room comfort)
Service Execution
Request
CVO
observers
CTRL PLANE
RWK Model
RED: associated with RWK
modeling
CHANGING THE RWK MODEL
DE for SK
DE for RWK
Observed
(and Actuated)
RW Entities
Observed
(and Impacted)
Actor
Real
World
Sensor
Source
Data Source/Sink Virtualization
Sensor
Source
Sensor
Source
Actuator
Sink
Virtual
Sensor
Virtual
Actuator
Service Execution
Hut.
Virtual
Sensor
Cor.
Adp.
RWK Validation CVO
Execution CVO
Actuate
VOs
DATA PLANE
FB1 FB2
qr
s
p
CVO
observers
EXEC PLANE
BLUE: associated with
CVO / VO
CHANGING THE ROOM
CONDITIONS
SK ModelSK Validation
CVO

18. Some examples please?
• tracking cars in a smart city
• medical equipment tracking and asset
management

19. 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

20. tracking medical equipment
Validate
Train
Execute
RWO parameterreconfiguration
recommendations toimprove energy
efficiencyof location sensorsIn the demo implementation,
Databaseof location
information(spatial &
temporal)of objects
3
4
5
Train efficiencyof location sensorsIn the demo implementation,
locationdata of objects is
simulated
1
2
2a
4a
6
7
8

21. Trento Hospital S. Chiara
Trilogis + ZIGPOS

23. where are we heading?
• connected things will be able to “socialise”
their status (i.e. friends by ownership)
– relevance, proximity based discovery and
selectionselection
• integration with existing “human” social
networks
– spontaneous service provisioning
L. Atzori, A. Iera, G. Morabito From “Smart Objects” to “Social Objects”: The Next
Evolutionary Step of the Internet of Things IEEE Communications Magazine, Jan 2014
B. Kim, T. Kim, D. Lee, and S. J. Hyun, "SpinRadar: A Spontaneous Service Provision Middleware for
Place-aware Social Interactions," PERSONAL AND UBIQUITOUS COMPUTING, April 2013

24. cloud and IoT for big-data “avoidance”?
• tackling the other potential bottlenecks (wise use of network
resources and resource constrained nature of IoT)
• need to ensure robustness for IoT
• low-latency services
• high-interoperability• high-interoperability
• use of edge cloud technology and distributed data processing
Data
Sources
Data to Apps:
Hadoop and batch
processing
Data
Sources
Apps requirements:
live stream (processing
“guidelines” travel opposite
compared to data)CBA
transmitted data sizes

25. datafrom50blndevices
edge processing loaded network
application domainsdatafrom50blndevices
network
networkingcomputation

26. Conclusions
• Internet of Things is part of our present
• efficient usage of planet Earth resources is a
challenge with growing and aging population
• tremendous growth potential (50bln only 3%• tremendous growth potential (50bln only 3%
of all objects :-)
• there is work to do

27. Further info / links
[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)
[REF1] IERC April 2013 Newsletter – Foreword (see THIS LINK)
[REF3] iCore website (www.iot-icore.eu/latest-news)[REF3] iCore website (www.iot-icore.eu/latest-news)
Best Demo Award at FuNeMS 2013

28. Thank you!
Raffaele Giaffreda
Smart IoT (RIoT) Research Area Head
(CREATE-NET)
EU FP7 iCore Project CoordinatorEU FP7 iCore Project Coordinator
raffaele.giaffreda@create-net.org
Websites:
www.create-net.org/research/research-areas/riot
www.iot-icore.eu