1.
1
A planet of cities
In 2007, for the first time in history, the majority of the world’s population —
3.3 billion people — lived in cities. By 2050, city dwellers are expected to make
up 70% of Earth’s total population, or 6.4 billion people.

2.
2

3.
3

4.
4

5.
Smart Cities
5
Smart traffic
New smart
systems
Safety
2012
Future
Environmental Sensors
Mobile
Sensing
Smart Energy
2020

6.
Connect our life with social infrastructure and make the life comfortable
safer, eco friendly
Smart City Vision
Water
management
ＩＴ
Data center
Network
communication
Transportation
Energy
Water
Shop
Station
Recycle facility
Energy station
FactoryFinancial institution
Hotel
School Hospital
Public facility
Office building
Housing
Growing City
Energy Transportation
Home Energy
Management
Smart
Grid
Community
Energy
Grid
Renewable Energy
Water, Environment
Green
Mobility
Intelligent Water
Smart Navigation
City Management
・City Planning
・Security
・Traceability
・Management Support
・Customer Service
・Operation
6IERC documents: http://www.internet-of-things-research.eu/documents.htm

7.
7
77
Cities require smarter solutions
 The systems are under increasing environmental, social and economic pressures
 For sustainable prosperity, the systems need to be managed optimally
 The systems need to become smarter!
Not more… ...but SMARTER!

8.
8
‘Smart’ solutions are instrumented, interconnected
and intelligent
Instrumented
Deep discovery, analysis and
forecasting
Event capture and filtering for
timely response
Any to any linkage of people,
process, and systems
Interconnected Intelligent+ +
= Smart

9.
Chaotic or Ordered?
Emergent, yet governed
Semi-deterministic

10.
Now, what’s up?
Internet-1
Internet-2
Internet-3
0
Internet-0:
the Internet
of Things
BorrowedfromN.Gershenfeld
ON THE INTERNET NOBODY KNOWS YOU’RE A LIGHT BULB!

11.
UMU Smart Building and Smart
Campus Project
• Smart buildings. Open Data Project.
12

12.
13
Energy
Management
Power generation
Energy monitoring
Efficient Power Management through
decision support
Tele-monitoring
Machines and devices
monitoring
Fault and anomalies
detection
Service management
Access Control
RFID personal identification
Number of users per room
Indoor Comfort
Thermal
Visual
Air quality
Exampleofthe ServicesProvided
Smart Campus Use Case

13.
Scenario of Validation
More of 30 buildings of the University of Murcia connected to
City explorer

14.
UMU Smart Building and Smart
Campus Project
15
15
• Smart Buildings Service: Smart Energy Control System
Home Automation Module (HAM) N
SMART ENERGY CONTROL SYSTEM
EIBUS/X10
CAN
SERIAL
ZIGBEE
Generated Energy
Environmental
Parameters
Lighting level
ZIGBEE EIBUS/X10 SERIAL CAN
CAN NODES
SENSOR
NETWORKS
INPUT DATA
HVAC
EIBUS/X10
DEVICES
SERIAL COM
DEVICES
LIGHTS
SETTING
Electrical devices
Consumed Energy
User Interactions
LOCALIZATION SYSTEM
User Negotiations
Time Data
User Location
User Identifier
1
2

15.
UMU Smart Building and Smart
Campus Project
16

16.
INTRODUCTION
Architecture Layers
Context Ontology
Information Processing
Distributed Knowledge Management
Actuators
Sensors
Interaction / Data & Events Capture
BBDD
Transportation
Energy Efficiency
Security
Smart Buildings
Context Data Consumption/Production
Monitoring & Control
Technologies
Context InformationMiddleware
Management
Services
Complex Event
Publish-
Subscribe
Intelligent Data
Processing
(Filters, DBMS)
Complex Event
Processing
(Rules, Fusion)
Data
Information
Knowledge
Services
ContextProducer
ContextConsumer
Services
Publish-subscribe
PUSH-PULL
Broadcasting
Intelligent Service-Providing Framework
Input Data
Abstraction XML
RDF
RDF-S
OWL OTHER (DAML, etc.)
OCP EXTENSIONSSOUPA
Open Context Platform Ontology
Context Service
Open Context Platform (OCP)
Automation System (DOMOSEC)
Home Automation Module (HAM)
Scada-Web
Indoor Comfort

17.
Smartdata Platform

18.
21
Smart Campus Use Case: Energy Efficiency
Usecasescenarios
Considering the facilities and deployments already available in the
Region of Murcia, we can focus on three examples of scenario:
Scenario 1: Smart Buildings (considering the Pleiades building fully
monitored and automated since their early stages of design).
Scenario 2: Smart Campus (considering the Campus of Espinardo of
the University of Murcia).
Scenario 3: Smart Public Facilities (considering the monitoring data
available and provided by the INFO partner about the energy
consumption of some relevant facilities distributed throughout the
Region of Murcia).

19.
UMU Use Case
03/09/2015 22
Totalservicesprovidedforenergyefficiency
• Access control management. Services features:
• Presence detection
• Comfort. Services features:
• HVAC management.
• Lighting management.
• Air quality monitoring. Services features:
• Monitor of Environmental Sensors.
• Electrical consumption monitoring in some test areas.
• Info about voltage
• Info about current
• Info about active power
• Info about reactive power
• Info about energy
• Energy production monitoring.
• Monitoring of inverters connected to solar panels
in different areas along the Campus.
• Sensors involved:
• Power Meters
• Temperature and lux meters
• Presence sensors
• Actuators involved:
• ON/OFF lighting
• ON/OFF HVAC
• Temperature set point HVAC

20.
23
ExampleoftheScenario – Exampleofactions
• Halls and corridors
• Lighting control: regulating light intensity depending on presence
of people and daylight (readings from luxmeters)
• Offices, laboratories and classrooms
• Lighting control: automated switch on/off depending on daylight
(luxmeters), and presence of people (presence sensors and RFID
access control).
• HVAC control: regulating HVAC depending on ambient
parameters (indoor and outdoor temperature/humidity), presence
of people, and window open/close sensors.
• Access control management.
• Multimedia devices management (in classrooms).
• Air quality monitoring.
• Electrical consumption monitoring in some test areas.
Smart Campus Use Case

21.
24
“How to connect to the platform…”
• Interfaces to connect with the platform are divided in three levels
The Smart Energy Management use case includes three different levels of
communication, that are Sensor Level, Gateway Level and SCADAWeb Level,
each with their interfaces.
The interfaces to interact with each level have been set in accordance with the
load each device is able to manage. In this sense, sensors as constrained devices
will support little load in contrast with the server.
• Sensor Level: At this level a CoAP interface can be used to interact with the
sensors. CoAP is a protocol targeted for constrained devices due to their
special needs.
• Gateway Level: This devices are more capable, and are enabled with both
MQTT and CoAP interfaces.
• SCADA Web Level: At this level supported protocols for the interfaces are
MQTT, CoAP and REST.

22.
03/09/2015
SMARTIE Project - Aveiro
Meeting 25
“How to connect to the platform…”
• Sensor to platform: IP sensors and actuators.
• Gateways to platform: both hardware and software gateways.
• SCADAweb to platform: Data Collection Software.
Internet
LAN EDIFICIO 1 LAN EDIFICIO 2
BACnet
4-
20mA
0-10V
Modbus
DALI
IEEE
802.15.4g
Metering
RFID
KNX
X10
Balastr
o
6LoWPA
N
RS232/485
iButto
nCAN bus
Sensor
Level
Gateway
Level
SCADAWeb
Level

23.
Odin Solutions
Spin-off of the University of Murcia (Spain) with more
of 10 years of experience on the design and
development of monitoring and control products
www.odins.es

24.
27
ExampleoftheScenario –DataCollectionSoftware
Smart Campus Use Case

25.
28
ExampleoftheScenario1–AutomationIPControllers
Smart Campus Use Case

26.
29
PlatformComponents
- Sensors: temperature, humidity, lighting, power meter, presence sensor,
RFID System, etc.
- Control Panel:
Smart Campus Use Case

27.
www.odins.es
Smart Campus Use Case
IntegralManagement
Energyreduction

28.
31
GraphicEditortodefineEnergySavingStrategies
Rules Designer HVAC Control
Lighting Control
Smart Campus Use Case
23,12% of Annual
Energy Saving in
Buildings

29.
SMART ENERGY CONTROL SYSTEM
Evaluation/Validation and Next Work Line
 Impact of users implication with the system operation
(understanding system feedback and through their interaction) in
terms of:
Changes in their behaviour
Learning and adaptation of the system
Energy consumption
 Assessments of the system
Next Work Line:
 Integrate Mobile Crowd-Sensing Techniques in our mechanism for
considering occupant’s devices data.

30.
38
Smart City Applications Based on Big Data Analytics
Cross-correlation between outdoor environmental conditions and
indoor temperature

31.
39
Smart City Applications Based on Big Data Analytics

32.
40
Smart City Applications Based on Big Data Analytics

33.
41
Smart City Applications Based on Big Data Analytics
the Bayesian NN model implemented is
able to estimate the indoor temperature
with a mean accuracy of 0.91 oC and a
mean error deviation of 0.063 oC

34.
42
Behaviour pattern application on Energy Management
A mean energy saving of 29% meanwhile
comfort preferences of occupants was satisfied
in the 91% of the cases.

35.
Conclusion
• Definition of a platform for IoT supporting privacy and security
• Deployment of Smart Building solution based on sensors and
actuators
• Integration of Energy Efficient Management solution based on
the work of OdinS spin-off of UMU
• Testbed based on 30 buildings including HVAC, lighting and
other components
• Integration of heterogeneous sensors in a common IP-based
gateways
• KNX, HVAC, Deli, CAN, propietary alarms system, etc
• 6LoWPAN support for new sensors
• SCADA web system for monitoring and actuation over sensors with
an editor for defining interactions
43

36.
Thank you for your attention!