IEEE Education Society: Reshaping the Future of Technology

European Erasmus Plus Projects
and Certification
in Cloud Computing and IoT
IN-CLOUD & IoT4SMEs Projects
Prof. Dr. Manuel Castro, http://www.slideshare.net/mmmcastro/
Electronics Technology Professor, UNED, Madrid, SPAIN
IEEE Fellow, Sr Past President, IEEE Education Society

EDUCATION SOCIETY
http://ieee-edusociety.org/

Motivation
 SMEs are fundamental for European
economy …
 But many fail in the first 5 years
 New strategic skills must be acquired to
ensure survival. E.g.: Cloud computing

Cloud computing benefits
Availability
encrease
Shorter time to
market
Flexible
model
Cost reduction
On-demand
scaling
Try before
buying

Reasons why SME don’t give
the jump to the cloud
 Lose control over their infrastructures
 Lack of understanding of infrastructures, costs and
application scenarios
 ICT knowledge of users, managers and entrepreneurs

IN-CLOUD aims
1. Raise awareness on how the cloud can boost
economic growth and innovation to:
 SMEs, public administrations and Universities
2. Create VET qualifications, based on sector analysis,
to give training about cloud computing

IN-CLOUD partners
 8 partners
 6 countries
 Geographic coverage
 6 languages

IN-CLOUD courses will provide …
 Business and financial skills
 Technical skills
 Cloud project management
 Contract negotiation
 Security
 Data integration

Certifications
1. Cloud Professional for Public Administrations
2. Cloud Professional for Business
3. Cloud Professional for Education
4. Cloud Technology Professional

Cloud Professional for Public
Administrations
 Unit 1: Introduction to cloud computing
 Unit 2: Security basics
 Unit 3: Cloud models
 Unit 4: Cloud services and applications for public
administrations and for citizens/community
 Unit 5: Legal and technical aspects of cloud computing for
public administration and for citizens/community

Cloud Professional for Business
 Unit 4: Cloud business services and applications
 Unit 5: Legal and technical aspects of cloud computing
for business

Cloud Professional for Education
 Unit 4: Cloud services and applications for education and
training
 Unit 5: Legal and technical aspects of cloud computing for
education and training

Cloud Technology Professional
 Unit 1: Cloud security
 Unit 2: Cloud models and providers
 Unit 3: Cloud architecture
 Unit 4: Cloud virtualization
 Unit 5: Cloud service and application development and
implementation

Motivation
 6.4 billion connected things will be in use worldwide in
2016, up 30 percent from 2015, and will reach 20.8
billion by 2020. Gartner
 While in 2014 just 300,000 developers contributed to
the IoT, 4.5 million developers by 2020 are expected:
57% compound annual growth rate and a massive market
opportunity. VisionMobile
 Expected to grow from 130.33 B$ in 2015 to 883.55 B$
by 2022.
 IoT market value in the EU will exceed one trillion euros
in 2020

National initiatives
 USA: “Manufacturing USA” network of excellence institutes and labs to
support the diffusion of the Industry 4.0 and ICT related technologies,
through a PPP., with about 500 M$ public investment.
 UK: national investment of 40 M£ on IoT announced in 2015, including a
10 M£ single project to realize a demonstrator to exhibit the capacity of
IoT in a city region.
 Germany: “Industrie 4.0” action plan sponsored at the federal level with
the involvement of large industry players and technology public, with 1
B€ public investment.
 France: “Industrie du Futur” reindustrialization plan and investment in
technologies I4.0 , with a public commitment € 10 B€.
 Italy: “Piano Nazionale Industria 4.0” launched in Sept. 2016, with 10 B€
public investments and 32 B€ private investments in 4 years.

Objectives
Aim: Qualifying new professionals able to support the digital
transformation of the European companies exploiting to the
advantages offered by the IoT technology.
This objective is reached by pursuing the specific objectives of:
 raising awareness among European Small and Medium Enterprises
of the IoT technologies and applications and of the potential
benefits for their competitiveness and economical growth;
 creating VET qualifications for professionals inside European
Companies, enhancing their digital competences and training
them to introduce
 and manage IoT technologies and applications.

Certifications
1) Iot Decision Maker
2) IoT with microcontrollers
3) IoT with microprocessor
4) IoT Data Analyst

1. Iot for Decision Makers
1.1) IoT technology
1.2) IoT business stragegy
1.3) Overview of data analysis
1.4) Legal aspects
1.5) Basics of networking and security

2. IoT with microcontrollers:
Arduino
2.1) Introduction (technology + business)
2.2) Device architecture and sensors for microcontrollers
2.3) Programming microcontrollers
2.4) Platforms for microcontrollers and applications
2.5) Networking and Security (for microcontrollers)

3. IoT with microprocessor:
Raspberry Pi
3.2) Device architecture and sensors for microprocessors
3.3) Programming microprocessors
3.4) Platforms for microprocessors and applications
3.5) Networking and Security (for microprocessors)

4. IoT Data Analyst
4.2) Device architecture and sensors
4.3) Networking and Security
4.4) IoT data analysis 4.5) IoT platforms

MOOC - Foundations to Open
Education and OERs repositories
IEEEx > joint effort with edX
 First MOOC of the IEEE Education Society
 19th August – 17th September 2017
 Future actions

MOOC - Foundations to Open
Education and OERs repositories
 Week 1. Introduction to Open Education & OERs
 Week 2. Repositories
 Week 3. Applications to Academia & Industry
 Week 4. OER Applications

IEEE EDUCATION SOCIETY: RESHAPING
THE FUTURE OF TECHNOLOGY
Internet of Vehicles and
autonomous vehicles
Unai Hernández Jayo
Unai.hernandez@deusto.es
June 2017
Mobility Unit, DeustoTech

2
Faro (Portugal). June 2017
Motivation
Context
• Thanks to the concept of Internet of Things
(IoT), all kind of devices, stuffs, etc. can be
interconnected, “without” human interaction

3
Purpose of IoT
• “The purpose of the IoT is to give humans
superpowers” (Tim O’Reilly and Cory Doctorow)
– Produce and consume information in real time
– Automatic response of “the system” according to the
environment
– Humans provide intelligence to the devices

4
New paradigms
• Smart cities

5
New paradigms
• Industry 4.0

6
New paradigms
• Smart Grid

7
IoT combines a lot of concepts

8
Then…
Imagine that vehicles could talk

9
Internet of Vehicles
• The Internet of Vehicles (IoV) is the convergence of
the mobile Internet and the Internet of Things.
• Comprising new and current vehicles, either fitted
or integrated with RF equipment.
• Vehicles already have:
– Human machine interface
– Sensors
– Actuators

10
IoV and ITS
• Although the IoV is a fashion concept, Intelligent
Transport Systems (ITS) combine all the technologies
that have been included in this term

11
What is the IoV?
• IoV technology refers
to dynamic mobile
communication
systems that
communicate
between vehicles and
public networks using
V2V (vehicle‐to‐
vehicle), V2I (vehicle‐
to‐Infrastructure) and
V2P (vehicle‐to‐
Pedrestians)
interactions.

12
What is the IoV?
• It enables information sharing and the gathering of information
on vehicles, roads and their surrounds. Moreover, it features the
processing, computing, sharing and secure release of information
onto information platforms
It could be a distributed or
centralized architecture

13
ITS station architecture

14
ITS architecture

15
Cooperative autonomous vehicles

16
Autonomous driving levels
Source: https://iq.intel.com/autonomous‐cars‐road‐ahead/

17
Future vehicles: autonomous or
cooperative
• Isolated autonomous vehicles:

18
Future vehicles: isolated autonomous
or cooperative

19
or cooperative

20
or cooperative
• Advantages of isolated autonomous vehicles:
– Without the need for a driver, cars could become mini‐
leisure rooms
– Increase auto‐safety?
– Travelers would be able to journey overnight and sleep for
the duration
– Reduced or non‐existent fatigue from driving?
– Speed limits could be increased?
– Reduction in insurance premiums for car owners?
– Efficient travel also means fuel savings
– (…)

21
or cooperative
• Connected and Autonomous Vehicles:
combination of IoV + Autonomous Vehicles.
Source: http://www.4erevolution.com/en/la‐meteo‐prochain‐secteur‐a‐etre‐uberise/

22
or cooperative
• Connected and Autonomous Vehicles:
combination of IoV + Autonomous Vehicles.
Source: http://safecarnews.com/australia‐stablishes‐connected‐vehicle‐network‐c‐its‐by‐2017//

23
or cooperative
• Advantages of connected and Autonomous
Vehicles:
– Public safety
– Traffic management
– Traffic coordination
– Traffic information support
– Emission reduction
– New business models
– (…)
Source: http://safecarnews.com/australia‐stablishes‐connected‐vehicle‐network‐c‐its‐by‐2017//

24
So… what could be the future?

Commercial Of-The-Shelf
(COTS) Wearables Supporting
Self-Quantification
Manuel Caeiro Rodríguez
UNIVERSITY OF VIGO
TELEMATIC ENGINEERING DEPARTMENT

VIRTUAL CITY ENVIRONMENT FOR
ENGINEERING PROBLEM BASED
LEARNING
2014-2015
http://ecity-project.eu
VIRTUAL CITY ENVIRONMENT FOR
ENGINEERING PROBLEM BASED
LEARNING
2014-2015
http://ecity-project.eu
THIS PROJECT HAS BEEN FUNDED WITH SUPPORT FROM THE
EUROPEAN COMMISSION. THIS PUBLICATION REFLECTS THE
VIEWS ONLY OF THE AUTHOR, AND THE COMMISSION
CANNOT BE HELD RESPONSIBLE FOR ANY USE WHICH MAY
BE MADE OF THE INFORMATION CONTAINED THEREIN.
© eeGeo
Virtual City
Design and manage your own
city
Problem-based Learning
8 scenarios avaliable to engage into engineering

eCity Results: a set of 8 problems
Energy Distribution Renewable Energies
Internet Server
Providers
Mobile Connection
Earthquake
Protection
Flood Protection Pollution Public Transportation

Wearables Variety: Wrist (186)
8
Source:IDC

Wearables Variety: Head (81)
9
● Google Glasses
● Snapchat Spectacles
● Microsoft Hololens
● Sony Playstation VR
Source vandrico.com/wearables

Wearables Variety: Ear (12)
10
● Jabra Elite Sport

Wearables Variety: Feet (13)
11
● IDM Perform
● Kinemaitx Tune

Wearables Variety: Arm (10)
12
● Shot Tracker
● Qardio Qardioarm

Wearables Variety: Chest (20)
13
● Polar H7
● Monbaby

Wearables Variety: Other Locations
14
● Ankle (4)
● Back (1)
● Body (10+39)
● Fingers (9)
● Legs (14)
● Neck (11)
● Pelvis (2)
● Shoulders (3)
● Thighs (2)
● Waist (10) Source vandrico.com/wearables

Wearable Applications
15
Lifestyle
Medical
Industrial
Entertainment
Fitness
Gaming

Wearables and Self-quantification
17
When do you wear your wearable?
● 40% of wearable users say they feel naked when not
wearing them
● 25% even
sleep with
them
Wearables are
the most
personal
devices
Source Ericson Consumerlab Wearable Technology and Internet of Things, 2016

COTS Wearables: Problems and Issues
Whom to share personal data?
● 50% of wearable users
share data from
wearables online
● 67% are open to sharing
data with third party
entities provided its
anonymous
● 70% perceive wearable
manufacturers to be
serious in protecting their
wearable data
18
Source Ericson Consumerlab Wearable Technology and Internet of Things, 2016
Wearable Manufactures as Personal Data Brokers

Wearable Applications: Education
19
Education
Education
● Two different kinds of projects
o As educational tools
o As quantification supports to feature students
 Sleep
 Stress

Objectives
●Collecting data: different solutions
●Homogenization: data models, reference points,
sensors (Heart Rate, Skin Temperature, Galvanic Skin
Response, Accelerometer).
●Proposing indicators for an academic environments:
o Sleep indicators: sleep quality, sleepiness,
chronotype, sleep sensitivity and sleep regularity.
o Stress indicators: snapshot, accumulated, latent
and variability.
20

How to collect Data?
● Wearables are still
tethered to the
smartphone via
Bluetooth and follow
a walled garden
approach
● 4 data transfer mode
o Warehouse vs. wearable
transfer
o Direct vs. indirect access
21

tethered to the
smartphone via
a walled garden
approach
transfer
22

tethered to the
smartphone via
a walled garden
approach
transfer
23

tethered to the
smartphone via
a walled garden
approach
transfer
24

COST Wearables
Data models:
●Data integration problems
oDifferences in the names
oDifference in the beginning of measurements
oDifferences related to the sensors available.
●Lack of in accuracy sensors
25

Sleep indicator
●Sleep apps
26

Sleep indicator
●Chronotype
27
Type Start Bed Time Values End Bed Time Values
Extreme
morning -/21:30 2 -/05:00 2
Moderately
morning 21:30/22:45 1 05:00/06:30 1
Intermediate 22:45/00:45 0 06:30/08:30 0
Moderately
evening 00:45/02:00 1 08:30/10:00 −1
Definitely
evening 02:00/- 2 10:00/- −2

Proposal for education
●Services for teachers/developers
32
● Moderately morning
● Traditional evaluation
● Most stressful
activities: 3.2, 4.1, 5.2
● No problems detected
● Moderately evening
● Continuous evaluation
● Most stressful
activities: 2.2, 4.1, 5.2
● Irregular sleep quality
warning
● Latent stress high

Conclusions
●Problems in wearables
oLow battery duration
oInteroperability problems
oSensors precision
oTethered to smartphones
●Opportunities
oApplication in new environments
oFast evolution
33

Thanks for your attention.
34
mcaeiro@gist.uvigo.es
farriba@uvigo.com
The images shown in this presentation have been used without any commercial purpose

Reconfigurable Computing
A new computing paradigm
Manuel G. Gericota
Department of Electrical Engineering
School of Engineering – Polytechnic of Porto
&
Faculty of Technology, Natural Sciences and Maritime Sciences
University College of Southeast Norway
Kongsberg, Norway

Official At Last: Intel Completes $16.7 Billion Buy of Altera
Barb Darrow
Dec 28, 2015
Chip giant Intel has completed its $16.7 billion mega-deal to
buy Altera, thus getting a big toe hold in the burgeoning market
for a new type of chip that is much more flexible than the
microprocessors Intel has ridden to fame and fortune.
Manuel Gericota 2

Why and what?
• Why Intel paid an astonishing $16.7 billion dollars for
it?
• What chips are that?
• What can be better than Intel microprocessors?
Manuel Gericota 3

The Moore’s Law
“The complexity for minimum
component costs has increased at
a rate of roughly a factor of two
per year. Certainly over the short
term this rate can be expected to
continue, if not to increase.”
G. E. Moore, “Cramming more components
onto integrated circuits,” Electronics, vol. 38,
no. 8, pp. 114‐117, April 19, 1965.
Manuel Gericota 4

Device technology
• Device technology is
the silicon area used
by a circuit,
expressed by the
length of the
smallest transistor
that can be
fabricated
Manuel Gericota 5
Source: International Technology Roadmap for Semiconductors, 2011

Device technology
by a circuit,
expressed by the
length of the
smallest transistor
that can be
fabricated
Manuel Gericota 6

Device technology
by a circuit,
expressed by the
length of the
smallest transistor
that can be
fabricated
Manuel Gericota 7

Microprocessors
• Computers based on microprocessors
• Implement software solutions based on pure microprocessor systems
• Fixed hardware → one or more generic and/or specific processors
(GPU (Graphic Processing Unit), DSP (Digital Signal Processor), …)
Manuel Gericota 8

Microprocessors
• General purpose computing
• 1945 ‐ John Von Neumann computer architecture
• simple, fixed structure, able to execute any kind of
computation, given a properly programmed control,
without the need for hardware modification
• Sequential instruction fetch
• a program is coded as a set of instructions to be executed sequentially,
instruction after instruction
• In general, the execution of an instruction on a computer can be done
in five cycles: Instruction Fetch, Instruction Decode, Operand Fetch,
Instruction Execute, Write Back
Manuel Gericota 9

Microprocessors
• Von Neumann architecture constraints
• main advantage of the Von Neumann (VN) computing paradigm
• flexibility → it can be used to program almost all existing algorithms
• algorithms must be coded according to the VN rules → ‘The algorithm
must adapt itself to the hardware’
• temporal use of the same hardware for a wide variety of applications →
VN computation is often characterized as ‘temporal computation’
• all algorithms must be sequentially programmed  many algorithms
cannot be executed with their potential best performance
Manuel Gericota 10

Microprocessors
Manuel Gericota 11
Frequency (GHz)
41% /year
Source: Semiconductor Industry Association ‐ International
Technology Roadmap for Semiconductors, 2011
• Microprocessors’
frequency of operation
is no longer increasing
due to
• limits on switching speed
• limits on dissipated
power
• limits on device scaling
due to leakage current

The big question
• The big question
• how to increase performance without increasing frequency?
• The small answer!
• parallelism → multiple core processors
• Extra cores on a single processor  multiplies the amount of
data that could be handled by the CPU
• Adding more CPU cores never results in perfect scaling
Manuel Gericota 12
Intel core i9
18 cores

Amdahl’s Law
• The performance benefit of
parallelism is limited by the
amount of the application
that must run serially, e.g. is
not or cannot be parallelized
• relevant if the microprocessor
is running several applications
at a time
• unfitted to accelerate single
applications
Manuel Gericota 13
Gene Amdahl
(1922‐2015)

Multicore constraints
• The software must be written to support multithreading
• if not, threads will be primarily run through a single core thus
degrading the efficiency
• Thermal restrictions  cores will be running at lower speeds
• Sharing the same system bus and memory bandwidth limits
the performance improvement
• DRAM speed limits memory access
• CPU clock vs DRAM latency is increasing
• Multicores in a single chip drive production yields down
Manuel Gericota 14

Microprocessors
• In sum…
• microprocessors are
running out of steam
• Moore’s Law collapsed due to heat
and leakage issues
• DARK SILICON → portions of
a device that need to be shut down
in order to avoid overheating
Manuel Gericota 15

DARK SILICON
Manuel Gericota 16
DARK SILICON trends for different technology nodes
Source: M. Shafique, S.
Garg, J. Henkel, D.
Marculescu, "The EDA
challenges in the dark
silicon era," in 51st
ACM/EDAC/IEEE
Design Automation
Conference (DAC),
2014, pp.1‐6

Heterogeneous Computing
• More than one type of processor is used to perform
specialized processing capabilities
• e.g. graphics rendering system = CPU + GPU
• GPU to render 3D scenes and perform mathematically intensive
computations on large datasets
• CPU to perform operating system and data networking tasks
• CPUs are reaching performance limits & GPUs are difficult to
program + they still use the Von Newman approach  remain
sequential machines  performance is limited
Manuel Gericota 17

Application‐Specific Processors (ASIPs)
• Processor tailored to run one application only
• the processing unit is designed and optimized for that particular
application
• the hardware ‘adapts’ itself to the application
• instruction cycles are eliminated
• instruction set of the application directly implemented in hardware
• input data stream in the processor through its inputs → the processor
performs the required computation → results collected at the outputs
• ASIP = Application‐Specific Integrated Circuit (ASIC)
Manuel Gericota 18

In an ideal world
• Ideally, we would like to have the flexibility of the
general purpose processors + the performance of the
application‐specific processors in the same device
Manuel Gericota 19

Field‐programmable devices
• The most versatile non‐ASIC technology
• An array of prefabricated generic logic
cells and a general interconnect
structure both programmable
• programmable semiconductor “switches”
open‐ or short‐circuited
• customization is done by configuring the
device with a specific data file
Manuel Gericota 21
FPGA (Field‐Programmable
Gate Array) architecture

Exponential amounts of data
• Internet of Things (IoT)
• Installation of sensors everywhere
• Increases in Internet connectivity
• Data requires extensive processing
• from the point of origin, through the cloud, into systems
that perform analysis  need for a new approach to
hardware
Manuel Gericota 22
generate an
exponential
amount of data

Internet of Everything
• In the future, cheap sensors will increasingly be
attached to almost everything
• 5G data connectivity = ‘ubiquitous’ connectivity

almost everything will be connected to one or more
sensors and processed by the communications
infrastructure
Manuel Gericota 23

Faster switching and routing
• Large‐scale data centres + cloud computing +
wireless applications  high‐speed serial
transceivers for fast switching and routing
• dual‐mode transceivers  56 Gbps enable terabit data
rates
• supports both optical and copper interfaces for
applications including chip‐to‐chip, backplane and direct
attach cable
Manuel Gericota 24

Ubiquitous data and security
• Around 30 billion devices connected by 2020
• Handling security screening  substantial computing
capabilities
• FPGAs are ideally suited to performing these tasks
• they operate quickly (hardware performance)
• can be reconfigured when needed to upgrade security
settings → can be used to process streams of data that use
rapidly changing encryption or keys (software flexibility)
Manuel Gericota 25

Project Catapult (Microsoft)
• Makes extensive use of FPGAs
“By exploiting the reconfigurable nature of FPGAs, at the server,
the Catapult architecture delivers the efficiency and
performance of custom hardware without the cost, complexity
and risk of deploying fully customized ASICs into the datacentre.
In doing so, [Microsoft has] achieved an order of magnitude
performance gain relative to CPUs with less than a 30 percent
cost increase, and no more than a 10 percent power increase.”
from Microsoft’s blog
Manuel Gericota 26

FPGAs in datacentres and search engines
• Microsoft has been developing a hardware
infrastructure for its Azure Cloud computing centres
• The technology is also being used to accelerate the
speed of Internet searches at its Bing search engine
• In cases where a server has to handle a mix of
workloads (making ASICs impractical), FPGAs are a
very energy‐efficient, high performance solution
Manuel Gericota 27

FPGA flexibility advantages
Manuel Gericota 28
Results from a Forbes survey of more than 300 CTOs and system architects
Forbes Insights – The coming data avalanche – and how we’ll handle it
Forbes Insights in association with Intel ‐ October of 2016

Intel processors and FPGAs
• Intel has introduced hybrid devices that combine Intel® Xeon®
processors with an FPGA
• The combination of the multiple processors on the same
silicon will enable designers to create products with
considerably higher performance than individual devices
• Intel is simplifying the interfaces between FPGAs and other system
components, and developing programming libraries  enable
developers to more easily program an FPGA accelerating the
development of new devices
Manuel Gericota 29

Partial and dynamic reconfiguration
• The whole Virtex series and 7‐
series, UltraScale and
UltraScale+ families, including
Zynq, from Xilinx, and Stratix V
and Stratix 10 from Intel are
partial and dynamically
reconfigurable
Manuel Gericota 30

A new computing paradigm
• Reconfigurable Computing (RC)
• The ability to provide a hardware platform that
can be customized on a per‐application basis
under software control
Manuel Gericota 31

Reconfigurable computing
Manuel Gericota 32

Resource sharing
• Multiple
applications share
the same logic
resources in the
spatial and temporal
domains  increase
application
performance
Manuel Gericota 33

In the future…
Will hardware implementations be as
flexible as software ones !?
Manuel Gericota 34

Manuel Castro, Unai Hernandez-Jayo, Manuel Caeiro &
Manuel Gericota
presented by
Javier García-Zubía, Manuel Gericota
Spanish and Portuguese Education Chapter Chairs
IEEE Education Society
Reshaping the future of technology

IEEE Education Society: Reshaping the Future of Technology

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