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

3

4

Smart Cities

5

Smart traffic

New smart

systems

Safety

2012Future

Environmental Sensors

Mobile

Sensing

Smart Energy

2020

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

Factory Financial 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

6 IERC documents: http://www.internet-of-things-research.eu/documents.htm

7 7 7

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

‘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

Chaotic or Ordered?

Emergent, yet governed

Semi-deterministic

Now, what’s up? Internet-1 Internet-2 Internet-3

0

Internet-0:

the Internet

of Things

Bo

rro

wed

fro

m N

. G

ers

hen

feld

ON THE INTERNET NOBODY KNOWS YOU’RE A LIGHT BULB!

UMU Smart Building and Smart Campus Project

• Smart buildings. Open Data Project.

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

Example of the Services Provided

Smart Campus Use Case

Scenario of Validation

More of 30 buildings of the University of Murcia connected to

City explorer

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

UMU Smart Building and Smart Campus Project

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 Information Middleware

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 EXTENSIONS SOUPA

Open Context Platform Ontology

Context Service

Open Context Platform (OCP)

Automation System (DOMOSEC)

Home Automation Module (HAM)

Scada-Web

Indoor Comfort

Smartdata Platform

21

Smart Campus Use Case: Energy Efficiency

Use case scenarios

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).

UMU Use Case

03/09/2015 22

Total services provided for energy efficiency

• 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

23

Example of the Scenario – Example of actions

• 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

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.

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 BA

Cn

et

4- 20m

A

0-10

V

Mo

db

us

DA

LI

IEE

E

802.

15.4

g

Met

erin

g

RF

ID

KN

X

X10

Bal

astr

o 6L

oW

PA

N

RS

232/

485

iBu

tto

n

CAN bus

Sensor Level

Gateway Level

SCADAWeb Level

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

27

Example of the Scenario – Data Collection Software

Smart Campus Use Case

28

Example of the Scenario 1 – Automation IP Controllers

Smart Campus Use Case

29

Platform Components

- Sensors: temperature, humidity, lighting, power meter, presence sensor,

RFID System, etc.

- Control Panel:

Smart Campus Use Case

www.odins.es

Smart Campus Use Case

Integral Management

Energy reduction

31

Graphic Editor to define Energy Saving Strategies

Rules Designer HVAC Control

Lighting Control

Smart Campus Use Case

23,12% of Annual

Energy Saving in

Buildings

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.

38

Smart City Applications Based on Big Data Analytics

Cross-correlation between outdoor environmental conditions and indoor temperature

39

Smart City Applications Based on Big Data Analytics

40

Smart City Applications Based on Big Data Analytics

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

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.

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

Thank you for your attention!