Internet de las Cosas: del Concepto a la Realidad

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Internet de las Cosas: del Concepto a la
Realidad
Bizkaia Enpresa Digitala, Parque Tecnológico de Bizkaia. Edificio Tecnalia, #204
27 de Octubre de 2016, 9:00-13:00
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es

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Abstract
• Esta jornada explicará el concepto de Internet de las
Cosas (IoT) y su encaje dentro de lo que se denomina
como la Internet del Futuro
– Describirá las tecnologías que lo hacen posible
– Ofrecerá ejemplos de aplicación de IoT a diferentes
ámbitos como salud, ciudades inteligentes o industria
– Identificará su grado de desarrollo actual
– Explorará su potencial implantación en nuestras entornos
vitales e influencia en nuestras actividades cotidianas en
un futuro cercano

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Agenda
1. Encaje dentro del ámbito de la Internet del Futuro: Web de
Datos y Cloud Computing
2. ¿Qué es la Internet de las Cosas (IoT)?
3. Tecnologías que hacen posible IoT: RFID, NFC, Arduino,
Protocolos, Cloud & Edge Computing …
4. Áreas de aplicación de la IoT: salud, bienestar, transporte,
industria
5. Casos de éxito de IoT
6. IoT como habilitador de las Ciudades Inteligentes
7. Perspectivas de crecimiento de IoT: realidad o promesa
8. Conclusión

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¿Qué es la Internet del Futuro?
• Término que resume los esfuerzos para
progresar a una mejor Internet, bien
mediante:
– Pequeños pasos evolutivos incrementales o
– Un rediseño completo (clean slate) y nuevos principios
arquitectónicos
• Future Internet –
– http://www.future-internet.eu/

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Misión de la Future Internet (FI)
• Ofrecer a todos los usuarios un entorno seguro,
eficiente, confiable y robusto, que:
– Permita un acceso abierto, dinámico y
descentralizado a la red y a su información y
– Sea escalable, flexible y adapte su rendimiento a
las necesidades de los usuarios y su contexto

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Visión de la Internet del Futuro

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Los Pilares de la Internet del
Futuro
• La Internet del Futuro consta de 4 pilares apoyados
en una nueva infraestructura de red como base:
– Internet Por y Para la Gente
– Internet de los Contenidos y del Conocimiento
– Internet de los Servicios
– Internet de las Cosas

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Arquitectura de la Internet del
Futuro

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Internet de las Cosas (IoT):
Motivación
• ¿Quieres saber cuántos pasos
has andado?
• ¿Los kilómetros que has
conducido?
• ¿Los watios que has consumido?
• ¿Cómo mejorar la eficiencia y
seguridad en mi fábrica?
• Internet de las Cosas te puede
decir eso y mucho más

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Internet de las Cosas … conectando
información, gente y cosas

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Evolución hacia IoT
• Desde la Web a la Web Social hacia IoT

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Historia IoT
• El concepto de dispositivo
inteligente conectado fue
acuñado en 1982 con máquina
expendedora conectada en
CMU
• El artículo de Mark Weiser en
1991 "The Computer of the
21st Century", y los conceptos
académicos de UbiComp y
PerCom fueron el germen de
IoT
• El término IoT fue acuñado
por Kevin Aston del MIT en
1999

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Internet of Things: Definition (I)
• Internet of Things (IoT) is a dynamic global network
infrastructure with self-configuring 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.
from the IERC (the European Research Cluster on Internet of Things
http://www.internet-of-things-research.eu/)
– Things can range from tagged objects (RFID, NFC, QR codes, Barcodes,
Image Recognition) to Wireless Sensor Networks (WSN), machines,
vehicles and consumer electronics

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Internet of Things: Definition (II)
• The internet of things (IoT) is the network of physical
devices, vehicles, buildings and other items—
embedded with electronics, software, sensors, and
network connectivity that enables these objects to
collect and exchange data
– Opportunity for more direct integration of the physical
world into computer-based systems, and resulting in
improved efficiency, accuracy and economic benefit
– Encompasses technologies such as Smart Grids, Smart
Homes, Intelligent Transportation and Smart Cities

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6 facts about IoT
1. IoT is the term used to describe any kind of application that
connected and made “things” interact through the Internet
2. IoT is a communication network connecting things which
have naming, sensing and processing abilities
3. IoT is the next stage of the information revolution, i.e. the
inter-connectivity of everything from urban transport to
medical devices to household appliances
4. Intelligent interactivity between human and things to
exchange information & knowledge for new value creation
5. IoT is not just about gathering of data but also about the
analysis and use of data
6. IoT is not just about “smart devices”; it is also about devices
and services that help people become smarter

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IoT = Sensors + Connectivity +
Processing for People

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Internet de las Cosas
• Red universal de objetos interconectados
y direccionables basada en protocolos de
comunicación estándar
– IoT exhibirá un alto nivel de heterogeneidad,
combinando objetos de distinta funcionalidad,
tecnología o campos de aplicación
– Protocolos semánticos noveles serán
desarrollados para permitir a IoT escalar y
coordinar a los millones de objetos que nos
rodean
– RFID y redes de sensores proporcionan un
mecanismo de bajo coste y robusto de
identificación y sensibilidad al contexto
• El uso de Internet pasará de modelo
request/reply a push-and-process

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Internet de las Cosas: mucho más
que cosas inteligentes

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IoT: 3rd wave of Internet
• Key attributes that distinguish IoT from “regular” Internet, as
captured by Goldman Sachs’s S-E-N-S-E framework: Sensing,
Efficient, Networked, Specialized, Everywhere

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Internet of Things (IoT) Promise
• There will be around 25 billion devices connected to the
Internet by 2015, 50 billion by 2020
– A dynamic and universal network where billions of identifiable
“things” (e.g. devices, people, applications, etc.) communicate
with one another anytime anywhere; things become context-
aware, are able to configure themselves and exchange
information, and show “intelligence/cognitive” behaviour

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Internet of Everything (I)
• CISCO view: “From the Internet of Things (IoT), where we are today, we
are just beginning to enter a new realm: the Internet of Everything (IoE),
where things will gain context awareness, increased processing power,
and greater sensing abilities”
– IoE brings together people, process, data, and things to make networked
connections more relevant and valuable than ever before-turning information
into actions that create new capabilities, richer experiences, and
unprecedented economic opportunity.

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Internet of Everything (II)

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Rapid growth of connected things
"Fixed" computing Mobility/BYOD Internet of things Internet of everything
Source: Cisco IBSG, 2013
(you go to the device) (the device goes with you) (age of devices) (people, process, data, things)
1995 2000 2013 2020
200M
10B
50B

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IoT Predictions (by 2020-22)
7,1tn IoT Solutions Revenue | IDC
1,9tn IoT Economic Value Add | Gartner
309bn IoT Supplier Revenue | Gartner
50bn Connected Devices | Cisco
14bn Connected Devices | Bosch SI
http://postscapes.com/internet-of-things-market-size
Peter Middleton, Gartner:
“By 2020, component
costs will have come
down to the point that
connectivity will become a
standard feature, even for
processors costing less
than
$1
“

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Tipos de Internet de las Cosas
• Al menos dos sabores:
– Consumer IoT (CIoT): orientada a consumidores
– Industrial IoT (IIoT)
• Industria 4.0

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Consumer Internet of Things (CIoT)
• The Consumer Internet of Things (CIoT) represents
the class of consumer-oriented applications where:
– Devices are consumer devices, such as smart appliances,
e.g. refrigerator, washer, dryer, personal gadgets such as,
fitness sensors, Google Glasses, etc.
– Data volumes and rates are relatively low
– Applications are not mission or safety critical, e.g., the
failure of fitness gadget will make you, at worse, upset, but
won’t cause any harm
– CIoT applications tend to be “consumer-centric”

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IoT impulse: Smart Cities, consumer
objects, mobile sensing, smart metering

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Personal data: SmartWatch & Health-
promoting Data Devices

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Quantified Self & Life
Logging
• Quantified self is self-knowledge through self-tracking with technology
– Movement to incorporate technology into data acquisition on aspects of a
person's daily life in terms of inputs (e.g. food consumed, quality of
surrounding air), states (e.g. mood, arousal, blood oxygen levels), and
performance (mental and physical)
• Self-monitoring and self-sensing through wearable sensors (EEG, ECG, video, etc.)
and wearable computing  lifelogging
• Application areas:
– Health and wellness improvement
– Improve personal or professional productivity
• Products and companies:
– Apple Watch, Fitbit tracker, Jawbone UP, Pebble, Withings scale

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Google Glass
• Su misión es producir un ubiquitous computer de venta
masiva
– Lanzadas para los desarrolladores de Google I/O por
1500$ en el año 2013
• Muestra información disponible sin utilizar las manos,
accede a Internet mediante órdenes de voz, de manera
comparable a Google Now

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• Google Home
– Features
• Amazon Echo
– Alexa API
Audible Computing
• Apple AirPods
– Comparison

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Features of Audible Computing
Products
Google Home Amazon Echo
Price $130 $180
Responds to voice
commands
Yes Yes
Always listening Yes Yes
Wake word "Okay Google" Alexa, Echo, or Amazon
Music streaming
options
Google Play Music, YouTube Music, Spotify, Pandora,
iHeartRadio, TuneIn, others
Amazon Prime Music, Spotify, Pandora,
iHeartRadio, TuneIn, others
Smart home
partnerships
Nest, SmartThings, Philips Hue, IFTTT
Nest, Ecobee, SmartThings, Wink, Insteon,
Belkin WeMo, Philips Hue, Lifx, Big Ass Fans,
IFTTT, other devices via "skills"
Customizable
appearance
Yes No
Output to stereo
system
Yes, via Chromecast No (yes with Amazon Dot)
Synced audio playback
to multiple devices
Yes, to any Google Cast device No
Personal assistant
highlights
Search Google, get a personalized daily briefing, check
traffic, add items to calendar, make a shopping list,
make a to do list, check flight status, track a package
Add items to calendar, make a shopping list,
make a to do list, check flight status, track a
package
Other features
Cast to your TV with Chromecast, launch and control
Netflix and YouTube via Chromecast, send photos to
your TV via Chromecast
Order a pizza, play a game, arrange an Uber
pickup. Echo has an ever-growing list of 900+
skills and counting
https://www.cnet.com/news/google-home-vs-amazon-echo/

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Industrial Internet of Things (IIoT)
• The Industrial Internet of Things (IIoT)
represents industry-oriented applications
where:
– Devices are machines operating in industrial,
transportation, energy or medical
environment
– Data volumes and rates tend to be from
sustained to relatively high
– Applications are mission and or safety
critical, e.g. the failure of a smart grid has
severe impact on our life and economy, the
misbehaving of a smart traffic system can
threaten drivers
– IIoT applications tend to be “system centric”

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Differences among IoT, M2M & CPS
• Not clear cut distinction, these terms are often used
interchangeably;
– 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

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Smart Grid
• A Smart Grid is an
electrical grid which
includes a variety of
operational and
energy measures
including smart
meters, smart
appliances,
renewable energy
resources, and
energy efficiency
resources.

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Industry 4.0
• Industry 4.0, Industrie 4.0 or the fourth industrial revolution,
is the current trend of automation and data exchange in
manufacturing technologies.
– It includes cyber-physical systems, the Internet of things and Cloud
Computing.
– Industry 4.0 creates what has been called a "smart factory".

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Industry 4.0: Features
• Ingredients for paradigm shift in manufacturing: autonomous robotics, additive
manufacturing (3D printing), cloud computing and sensor technology (IoT)
• Opportunities for innovation in terms of:
– Smarter industrial processes
– New business models and
– Customised products
• The new technological wave builds on the concept of cyber-physical systems:
profound interaction of the real and virtual worlds in the manufacturing process

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Internet of Things: Challenges
1. To process huge amounts of data supplied by “connected
things” and to offer services as response
2. To research in new methods and mechanisms to find,
retrieve, and transmit data dynamically
– Discovery of sensor data — both in time and space
– Communication of sensor data: complex queries (synchronous),
publish/subscribe (asynchronous)
– Processing of great variety of sensor data streams: correlation,
aggregation and filtering
3. Ethical and social dimension: to keep the balance between
personalization, privacy and security

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La Ecuación de IoT
• Conexión en red de cosas aumentadas da lugar a
agregación de datos y orquestación de servicios
para mejorar procesos
THING IT
[HW | SW]
THING-BASED
FUNCTION
[Local | Business
models known]
IT-BASED
SERVICE
[Global | Business
models required]
Example SERVICE: Send ambulance
in case of accident (detected by sensors)
Example FUNCTION:
Drive from A to B
A B
Source: University of St. Gallen, Prof. Dr. Elgar Fleisch

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Information flow in IoT
• Information within the Internet of Things creates value in a
never-ending value loop consisting of 5 stages (CREATE … to ACT):

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IoT Architecture
Sensing Layer
Communication Layer
Management Layer

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What do IoT apps do? (I)
• Remote monitoring
• Distributed and accurate sensing
• Tracking location / presence (inventory, belongings)
• Tracking usage / conditions
• Statistics data generation
– Health, energy, traffic etc.
• Actuation

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De la Granja Digital a la Mesa

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IoT in
Precision
Agriculture
and Farming

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IoT Companies
• House:
– http://smartthings.com/
– https://nest.com/
– http://sen.se/ (“mother”)
– http://bounceimaging.com/ (emergency & rescue)
• Car:
– http://www.automatic.com
• Health & Activity
– Pebble (smart watch, personal assistant)
– Fitbit (personal trainer, fitness, health monitoring)
– Samsung
• IBM (Smart cities, dublinked)
• Cisco (Internet of Everything)

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IoT Enablers (I)
RFID Sensor Smart Tech Nano Tech
To identify
and track
the data of
things
To collect
and process
the data to
detect the
changes in
the physical
status of
things
To enhance the
power of the
network by
devolving
processing
capabilities to
different part of
the network.
To make the
smaller and
smaller things
have the
ability to
connect and
interact.

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IoT Enablers (II)
Networked
heating systems
Networked
surveillance systems
Connected
vehicles
Smart sensor
platforms
Network
capability of
devices
Low power
consumption
Small form
factor
Energy
harvesting
capability
Wireless
technologies
Applications
Appropriate
cost
Enablers

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IoT Enabling Technologies
• Low-cost embedded computing and communication
platforms, e.g. Arduino or Rapsberry PI
• Wide availability of low-cost sensors and networks
• Cloud-based Sensor Data Management Frameworks:
Xively, Sen.se
 Democratization of Internet-connected Physical Objects

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IoT Hardware prototyping platforms
– Self-contained
– Cheap
– Easy to program and extend
– Often under Open Source and/or Open
Hardware license
– Self-contained
– Strong online community for learning and
support
– Focus on easy onboarding for non-experts
– Strong success in hobbyist / maker /
education areas
• An electronic board and associated software for easily connecting electronics
to software and the Cloud which differs from professional electronics
development kits:

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IPv6 a key IoT enabler (I)
• Latest revision of the Internet Protocol (IP), provides an identification
and location system for computers on networks and routes traffic across
the Internet.
– Developed by the Internet Engineering Task Force (IETF) to deal with the long-
anticipated problem of IPv4 address exhaustion
• IPv6 is intended to replace IPv4, which still carries the vast majority of Internet
traffic.
– As of October 2016, the percentage of users reaching Google over IPv6 surpassed 14%:
https://www.google.com/intl/en/ipv6/statistics.html#tab=ipv6-adoption&tab=ipv6-
adoption
• To make the switch, software and routers will have to be changed
• IPv6 uses a 128-bit address, allowing 2128, or approximately 3.4×1038
addresses, or more than 7.9×1028 times as many as IPv4, which uses 32-bit
addresses.
• IPv6 addresses are represented as eight groups of four hexadecimal digits
separated by colons
– E.g. 2001:0db8:85a3:0042:1000:8a2e:0370:7334

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IPv6 a key IoT enabler (II)
• The future of IoT will not be possible without the support of IPv6
– The global adoption of IPv6 in the coming years will be critical for the successful
development of the IoT in the future
• The ability to network embedded devices with limited CPU, memory and
power resources means that IoT finds applications in nearly every field
– IoT systems could also be responsible for performing actions, not just sensing
things
• 6LoWPAN is an acronym of IPv6 over Low power Wireless Personal Area
Networks
– The 6LoWPAN concept originated from the idea that "the Internet Protocol could and
should be applied even to the smallest devices“ and that low-power devices with
limited processing capabilities should be able to participate in the Internet of Things.
– The 6LoWPAN group has defined encapsulation and header compression mechanisms
that allow IPv6 packets to be sent and received over IEEE 802.15.4 (Zigbee) based
networks.

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IPv6 vs. IPv4
• Other important changes:
• No more NAT (Network Address Translation), Auto-configuration, no
more private address collisions, better multicast routing, simpler header
format, simplified, more efficient routing, true quality of service (QoS), also
called "flow labeling“, built-in authentication and privacy support, flexible
options and extensions, easier administration (say good-bye to DHCP)

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HTTP 2.0
• HTTP 2.0 is the next planned version of the HTTP network protocol used
by the World Wide Web.
– HTTP 2.0 is being developed by the Hypertext Transfer Protocol Bis (httpbis)
working group of the IETF.
– Based on Google's SPDY protocol, Microsoft's HTTP Speed+Mobility proposal
(SPDY based)
• HTTP 2.0 would be the first new version of the HTTP protocol since HTTP
1.1 was described by RFC 2616 in 1999.
– In May 2015 it was published as HTTP/2 as RFC 7540
• Goals:
– Include asynchronous connection multiplexing, header compression, and
request-response pipelining, while maintaining full backwards compatibility
with the transaction semantics of HTTP 1.1
– Enable Server-Push
• Documentation:
– http://chimera.labs.oreilly.com/books/1230000000545/ch12.html

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HTTP 2.0 streams, messages and frames
Binary Framing Layer Stream, Messages & Frames
A connection carries any number of bidirectional
streams. In turn, each stream communicates in
messages, which consist of one or multiple frames,
each of which may be interleaved and then
reassembled via the embedded stream identifier in
the header of each individual frame
Request & Response Multiplexing

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Protocolos para IoT (II)
• NFC y BLE también entran en esta categoría:
Protocol CoAP XMPP RESTful HTTP MQTT
Transport UDP TCP TCP TCP
Messaging Request/Response
Publish/Subscribe
Request/Response
Request/Response
Publish/Subscribe
Request/Response
2G, 3G, 4G
Suitability (1000s
nodes)
Excellent Excellent Excellent Excellent
LLN Suitability
(1000s nodes) Excellent Fair Fair Fair
Compute
Resources 10Ks RAM/Flash 10Ks RAM/Flash 10Ks RAM/Flash 10Ks RAM/Flash
Success Stories
Utility Field Area
Networks
Remote
management of
consumer white
goods
Smart Energy
Profile 2 (premise
energy
management/hom
e services)
Extending
enterprise
messaging into IoT
applications

79
Near Field Communication (NFC)
• Near field communication (NFC) is a set of standards for smartphones and
similar devices to establish radio communication with each other by touching
them together or bringing them into close proximity, usually no more than a few
centimetres
– Operates at 13.56 MHz, has data transfer rate ranging from 106 kbit/s to 424 kbit/s
– NFC tags contain data and are typically read-only, but may be rewriteable
• Uses RFID (Radio Frequency Communication) chips that enable devices to
communicate between them, bi-directionally.
– Application examples:
• NFC headsets and electronic wallets, eliminates the need to pair devices in Bluetooth
or WiFi Direct (e.g. Android Beam / S-Beam), data exchange through NFC tags
• Wider availability of NFC-enabled SmartPhones is propelling its usage:
http://www.nfcworld.com/nfc-phones-list/
– Apple iPhone 6s supports NFC as part of Apple Pay

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NFC in Use
• Get info from posters,
make payments,
exchange connections

81
Bluetooth Low Energy (BLE)
• Bluetooth low energy (BLE) is a wireless computer network technology which
is aimed at novel applications in the healthcare, fitness, security, and home
entertainment industries.
– Compared to "Classic" Bluetooth, it is intended to provide considerably reduced
power consumption and lower cost, while maintaining a similar communication
range
• Power consumption is drastically reduced via a low pulsing method that keeps devices
connected without the need of a continuous information stream
• Features:
– Operates in the same spectrum range (the 2.400 GHz-2.4835 GHz ISM band) as
Classic Bluetooth technology, but uses a different set of channels.
– Uses a star topology
– Nodes act as presence/state indicators
– Internet enabled devices as Gateways
• Available devices supporting BLE (most of the new SmartPhones feature it)

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• In the market, we can encounter two types of BLE devices:
– Bluetooth Smart Ready refers to devices that use a dual-mode radios, which
can handle both the 4.0 technology, as well as classic Bluetooth abilities, such
as transferring files, or connecting to a hands-free device.
– Bluetooth Smart represents a new breed of Bluetooth 4.0 peripherals: sensor-
type devices like heart-rate monitors or pedometers that run on small
batteries and are designed to collect specific pieces of information.
• Only connect to BT Smart Ready devices

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iBeacon – a class of BLE devices that broadcast their
identifier to nearby portable electronic devices (I)

84
iBeacon – a class of BLE devices that broadcast their
identifier to nearby portable electronic devices (II)

86
Short range communication: NFC,
QR and iBeacons

87
Web of Things (I)
• The Web of Things (WoT) is a computing concept that
describes a future where everyday objects are fully
integrated with the Web.
– The prerequisite for WoT is for the "things" to have embedded
computer systems that enable communication with the Web, i.e. HTTP
microserver
– Such smart devices would then be able to communicate with each
other using existing Web standards: HTTP & REST
– http://www.webofthings.org/

88
Web of Things (II)
• Term used to describe approaches, software architectural
styles and programming patterns that allow real-world
objects to be part of the World Wide Web
– Similarly to what the Web (Application Layer) is to the Internet
(Network Layer) the Web of Things provides an Application Layer that
simplifies the creation of Internet of Things applications
– Rather than re-inventing completely new standards, the Web of Things
reuses existing and well-known Web standards used in the
programmable Web (e.g., REST, HTTP, JSON), semantic Web (e.g.,
JSON-LD, Microdata, etc.), the real-time Web (e.g., Websockets) and
the social Web (e.g., oauth or social networks).

89
Web of Things Architecture
• The following layers compose WoT:
– Layer 1 (ACCESS): ensures things
have a Web accessible API,
transforming them into
programmable things
– Layer 2 (FIND): reuses Web
semantic standards to describe
things and their services
– Layer 3 (SHARE): data generated by
things can be shared in an efficient
and secure manner
– Layer 4 (COMPOSE): integrates the
services and data offered by things
into higher level Web tools

90
The Programmable World
• Los siguientes pasos para alcanzar la quimera de
Programmable World:
1. Transformar los objetos cotidianos en inteligentes
2. Conectar estos objetos entre ellos y hacer que
“conversen”, algo de lo que productos como SmartThings
están tratando
3. Construir aplicaciones basadas en esta conectividad,
interconectándolas con datos externos para predecir, por
ejemplo, patrones de tiempo o consumo eléctrico
• Soluciones como IFTTT facilitan esa conectividad
entre diferentes canales de datos

91
IFTTT
• IFTTT is a service that lets you create powerful connections
with one simple statement:
– IFTTT is pronounced like “gift” without the “g”
• Channels are the basic building blocks of IFTTT: Facebook,
Evernote, Email, Weather, LinkedIn
• Each channel has its own Triggers and Actions:
– The this part of a Recipe is a Trigger, e.g. “I’m tagged in a photo on
Facebook”
– The that part of a Recipe is an Action, e.g. “send me a text message”
– Pieces of data from a Trigger are called Ingredients
• Demos: https://ifttt.com/myrecipes/personal

92
Atooma
• Es como un IFTTT pero para
SmartPhones
• Permite definir eventos
condicionales (IF) que lanzan
automáticamente tareas (DO)
asociadas actividades que pueden
ser detectadas por tu móvil (hora,
localización, estado de la batería,
etc.)
– URL: http://www.atooma.com/

93
IoT & Cloud Computing
Interdependency
• Cloud computing and IoT are tightly coupled
– The growth of IoT and the rapid development of
associated technologies create a widespread connection of
“things.”
• Leads to production of large amounts of data, which needs
to be stored, processed and accessed
– Cloud computing as a paradigm for big data storage and
analytics
• The combination of cloud computing and IoT will
enable new monitoring services and powerful
processing of sensory data streams.

94
Infraestructura Virtualizada:
Cloud Computing
Un paradigma de computación emergente donde los datos y servicios
residen en centros de datos muy escalables que pueden ser accedidos
ubicuamente desde cualquier dispositivo conectado a Internet.

95
Cloud Computing es …
• … capacidad computacional y
almacenamiento virtualizada expuesta
mediante infraestructura agnóstica a la
plataforma y accedida por Internet
– Recursos IT compartidos en demanda, creados y
eliminados eficientemente y de modo escalable a
través de una variedad de interfaces programáticos
facturados en base a su uso

96
Evolución hacia Cloud Computing
• La coexistencia y limitaciones de cluster
computing y supercomputing dieron
lugar a grid computing
• De grid computing progresamos hacia
utility computing, i.e. Servicios
computacionales empaquetados como
agua, electricidad, etc.
• Esto derivó en Cloud Computing, es
decir, todo como servicio (XaaS) :
• Plataforma como Servicio
• Software como Servicio
• Infraestructura como Servicio

97
Clasificación de Cloud Computing

98
Fisonomía de Cloud Computing
Tipos de despliegue
• Cloud privada
– Propiedad de o alquilada por una
empresa (centros de datos,…)
• Cloud comunitaria
– Infraestructura compartida por
una comunidad específica
• Cloud pública
– Vendida al público, gran escala
(ec2, S3,…)
• Cloud híbrida
– Composición de dos o más
clouds
Manifestaciones
• Cloud Software as a Service (SaaS)
– Uso de la aplicación del proveedor sobre
la red, e.j., Salesforce.com,…
• Cloud Platform as a Service (PaaS)
– Despliega aplicaciones creadas por los
clientes a la nube, e.j. Google App Engine,
Microsoft Azure, IBM BlueMix …
• Cloud Infrastructure as a Service (IaaS)
– Alquilar procesamiento, almacenamiento,
capacidad de red y otros recursos
computacionales e.j., EC2 – Elastic
Compute Cloud, S3 – Simple Storage
Service, Simple DB,…

99
Arquitectura Cloud Computing

100
Ventajas y Retos de Cloud
Computing

102
Cloud Computing Limitations for IoT
• Connectivity to the Cloud is a MUST but …
– Some IoT systems need to be able to work even when connection is
temporarily unavailable or under degraded connection
• Cloud Computing assumes that there is enough bandwidth to
collect the data
– That can become an overly strong assumptions for Industrial Internet
of Things applications
• Cloud Computing centralises the analytics thus defining the
lower bound reaction time of the system
– Some IoT applications won’t be able to wait for the data to get to the
cloud, be analysed and for insights to get back

103
Edge Computing
• Pushing the frontier of computing applications,
data, and services away from centralized nodes to
the logical extremes of a network.
– It enables analytics and knowledge generation to occur at
the source of the data.

104
Edge Computing: Benefits
• Locally confines regional data processing of M2M/big data applications
that incur large data traffic to edge-servers, and reduces network
bandwidth.
– Executes real-time applications that require high-speed response at the
nearer edge-servers which will satisfy the severe real-time requirement.
• Offloads some of the computation intensive processing on the user’s
device to edge servers and makes application processing less dependent
on the device’s capability.

105
Fog Computing = IoT + Cloud Computing (I)
• The industry’s three building blocks, subject to Moore’s law, are: storage,
computing and network
– The problem is, right now everything is sorted in the cloud, which means you
have to push all this data up, just to get the distilled big data feedback down.
• Fog computing is a decentralized computing infrastructure in which
computing resources and application services are distributed in the most
logical, efficient place at any point along the continuum from the data
source to the cloud
o improve efficiency and reduce the amount of data
that needs to be transported to the cloud for data
processing, analysis and storage.
o done for efficiency reasons, but it may also be
carried out for security and compliance reasons.

106
Fog Computing = IoT + Cloud
Computing (II)

107
Cloudlet
• A cloudlet is a new architectural element that arises
from the convergence of mobile computing and
cloud computing.
• It represents the middle tier of a new 3-tier
hierarchy:
– mobile device --- cloudlet --- cloud.
• A cloudlet can be viewed as a "data center in a box"
whose goal is to "bring the cloud closer".

108
Web Semántica
• Problema de la Web Actual:
– El significado de la web no es comprensible por máquinas
• Web Semántica  crea un medio universal de
intercambio de información, aportando semántica a
los documentos en la web
– Añade significado comprensible por ordenadores a la Web
– Usa técnicas inteligentes que explotan esa semántica
– Liderada por Tim Berners-Lee del W3C
• Misión  “turning existing web content into
machine-readable content“

109
Web of Data: Limitaciones de la Web
de Documentos
• Demasiada información con muy poca estructura y
hecha además para consumo humano
– Es una web sintáctica no semántica
– La búsqueda de contenidos es muy simplista
• Se requieren mejores métodos
• Los contenidos web son heterogéneos
– En términos de contenido
– En términos de estructura
– En términos de codificación de caracteres
• El futuro requiere integración de información inteligente

110
Linked Data
• “A term used to describe a recommended best practice for
exposing, sharing, and connecting pieces of data, information,
and knowledge on the Semantic Web using URIs and RDF.“
• Allows to discover, connect, describe and reuse all sorts of data
– Fosters passing from a Web of Documents to a Web of Data
• In September 2011, it had 31 billion RDF triples linked through 504 millions of
links
• Thought to open and connect diverse vocabularies and semantic
instances, to be used by the Semantic community
• URL: http://linkeddata.org/

111
Linked Data Principles
1. Uses URIs to identify things
2. Uses HTTP URIs to enable those
things to be dereferenced by both
people and user agents
3. Provides useful info (structured
description and metadata) about a
thing/concept referenced by an URI
4. Includes links to other URIs to
improve related information
discovery in the web

112
Linked Data Example
http://…/isb
n978
Programming the
Semantic Web
978-0-596-15381-6
Toby Segaran
http://…/publi
sher1
O’Reilly
title
name
author
publisher
isbn
http://…/isb
n978
sameAs
http://…/rev
iew1
Awesome
Book
http://…/rev
iewer
Juan
Sequeda
http://juanseque
da.com/id
hasReview
hasReviewer
description
name
sameAs
livesIn
Juan Sequedaname
http://dbpedia.org/Austin

113
Linked Data Life Cycle
• Linked Data must go through several stages (several
iterations on Linkage) before are ready for exploitation:

114
Schema.org
• Initiative launched in 2011 by Bing, Google, Yahoo and then Yandex
• Objective: “create and support a common set of schemas for structured data
mark-up on web pages.”
– Propose to use their schemas to annotate contents in a web page with metadata
• Metadata are recognized by search engines and other parsers, thus accessing to the
“meaning” of portals
• Their vocabularies were inspired by earlier formats like Microformats, FOAF,
GoodRelations and OpenCyc
• Offer schemas in the following domains
(http://schema.org/docs/schemas.html):
– Events, health, organization, person, place, product, offer, revisión and so on.
• To map declarations in microdata to RDF the following tools can be used:
http://tools.seochat.com/category/schema-generators
• More info at: http://schema.org/
• Examples:
– http://schema.org/CreativeWork
– http://paginaspersonales.deusto.es/dipina/ (microdata.reveal Chrome plugin)

115
Avoiding Data Silos through
Semantics in IoT
• Cut-down semantics is applied to enable machine-
interpretable and self-descriptive interlinked data
– Integration – heterogeneous data can be integrated or one
type of data combined with other
– Abstraction and access – semantic descriptions are
provided on well accepted ontologies such as SSN
– Search and discovery – resulting Linked Data facilitates
publishing and discovery of related data
– Reasoning and interpretation –new knowledge can be
inferred from existing assertions and rules

116
Actionable Knowledge from
Linked Data
• Don’t care about the data sources (sensors) care about
knowledge extracted from their data correlation &
interpretation!
– Data is captured, communicated, stored, accessed and shared
from the physical world to better understand the surroundings
– Sensory data related to different events can be analysed,
correlated and turned into actionable knowledge
– Application domains: e-health, retail, green energy,
manufacturing, smart cities/houses

117
Data Understanding through Linked
Statistics & Visualizations

118
Bringing together IoT and Linked Data:
Sustainable Linked Data Coffee Maker
• Hypothesis: “the active collaboration of people and
Eco-aware everyday objects will enable a more
sustainable/energy efficient use of the shared
appliances within public spaces”
• Contribution: An augmented capsule-based coffee
machine placed in a public spaces, e.g. research
laboratory
– Continuously collects usage patterns to offer
feedback to coffee consumers about the energy
wasting and also, to intelligently adapt its
operation to reduce wasted energy
• http://socialcoffee.morelab.deusto.es/

119
Social + Sustainable + Persuasive +
Cooperative + Linked Data Device
1. Social since it reports its energy consumptions via social
networks, i.e. Twitter
2. Sustainable since it intelligently foresees when it should be
switched on or off
3. Persuasive since it does not stay still, it reports misuse and
motivates seductively usage corrections
4. Cooperative since it cooperates with other devices in order
to accelerate the learning process
5. Linked Data Device, since it generates reusable energy
consumption-related linked data interlinked with data from
other domains that facilitates their exploitation

120
Persuasive Interfaces to Promote Positive
Behaviour Change
GreenSoul, H2020
project 2016-2018, EE11

121
Linked Data by IoT Devices
• Modelling not only the sensors but also their features of
interest: spatial and temporal attributes, resources that
provide their data, who operated on it, provenance and so on
– With SSN, SWEET, SWRC, GeoNames, PROV-O, … vocabularies

122
IoT Platform Requirements
Devices
Connectivity
Platforms
Internet of
Things
Connected things,
products, services,
systems, etc.
Security
Networks
Apps &
Analytics
Databases
Source:
Machina Research 2014

123
IoT Platforms
• Allow to manage remote devices and exchange messages to enable
building IoT applications
– Remote Device Management
• Manage the device life cycle from onboarding till decommissioning
• Receive device information
• Configure devices remotely
• Send commands to devices
– Message Management
• Collect sensor data and store it in the HCP persistence layer
• Supports various transport protocols and message formats
– Application Enablement
• Use Device Management and Message Management functionality in your
applications
• IoT software platform can be classified according to the following criteria:
device management, integration, security, protocols for data collection,
types of analytics, and support for visualizations

124
IoT Platforms
IoT Software
Platform
Device
management?
Integration Security
Protocols for data
collection
Types of analytics
Support for
visualizations?
2lemetry - IoT
Analytics Platform**
Yes
Salesforce, Heroku,
ThingWorx APIs
Link Encryption (SSL),
Standards ( ISO 27001,
SAS70 Type II audit)
MQTT, CoAP,
STOMP,M3DA
Real-time analytics
(Apache Storm)
No
Appcelerator No REST API
Link Encryption (SSL,
IPsec, AES-256)
MQTT, HTTP
Real-time analytics
(Titanium [1])
Yes (Titanium UI
Dashboard)
AWS IoT platform Yes REST API
Link Encryption
(TLS), Authentication
(SigV4, X.509)
MQTT, HTTP1.1
Real-time analytics
(Rules Engine, Amazon
Kinesis, AWS Lambda)
Yes (AWS IoT
Dashboard)
Bosch IoT Suite - MDM
IoT Platform
Yes REST API *Unknown
MQTT, CoAP,
AMQP,STOMP
*Unknown
Yes (User Interface
Integrator)
Ericsson Device
Connection Platform
(DCP) - MDM IoT
Platform
Yes REST API
Link Encryption
(SSL/TSL),Authenticati
on (SIM based)
CoAP *Unknown No
EVRYTHNG - IoT
Smart Products
Platform
No REST API Link Encryption (SSL)
MQTT,CoAP,
WebSockets
Real-time analytics
(Rules Engine)
Yes (EVRYTHNG IoT
Dashboard)
IBM IoT Foundation
Device Cloud
Yes
REST and Real-time
APIs
Link Encryption ( TLS),
Authentication (IBM
Cloud SSO), Identity
management (LDAP)
MQTT, HTTPS
Real-time analytics
(IBM IoT Real-Time
Insights)
Yes (Web portal)
ParStream - IoT
Analytics Platform***
No R, UDX API *Unknown MQTT
Real-time analytics,
Batch analytics
(ParStream DB)
Yes (ParStream
Management Console)
PLAT.ONE - end-to-
end IoT and M2M
application platform
Yes REST API
Link Encryption (SSL),
Identity Management
(LDAP)
MQTT, SNMP *Unknown
Yes (Management
Console for application
enablement, data
management, and
device management)
ThingWorx - MDM IoT
Platform
Yes REST API
Standards (ISO 27001),
Identity Management
(LDAP)
MQTT, AMQP, XMPP,
CoAP, DDS,
WebSockets
Predictive
analytics(ThingWorx
Machine Learning),
Real-time analytics
(ParStream DB)
Yes (ThingWorx
SQUEAL)
Xively- PaaS enterprise
IoT platform
No REST API
Link Encryption
(SSL/TSL)
HTTP, HTTPS,
Sockets/ Websocket,
MQTT
*Unknown
Yes (Management
console)
Source: https://dzone.com/articles/iot-software-platform-comparison

125
IoT como habilitador de las
Ciudades Inteligentes
• IoT allows for the pervasive interaction
with/between the smart things leading to an
effective integration of information into the digital
world.
– Smart things - instrumented with sensing, actuation, and
interaction capabilities - have the means to exchange
information and influence the real world entities and
other actors of a smart city eco-system in real time,
forming a smart pervasive computing environment to
achieve a more livable city

126
The need for Smart Cities
• Challenges cities face today:
– Growing population
• Traffic congestion
• Space – homes and public space
– Resource management (water and energy use)
– Global warming (carbon emissions)
– Tighter city budgets
– Aging infrastructure and population

127
Society Urbanisation & Ageing
• Urban populations will grow by an estimated 2.3 billion over the
next 40 years, and as much as 70% of the world’s population will
live in cities by 2050
[World Urbanization Prospects, United Nations, 2011]
• By 2060, 30% of European population will be 65 years or older
[EUROSTAT. Demography report 2010. “Older, more numerous and diverse Europeans”, March 2011.]

128
What is a Smart City?
• Smart Cities improve the efficiency and
quality of the services provided by governing
entities and business and (are supposed to)
increase citizens’ quality of life within a city
– This view can be achieved by leveraging:
• Available infrastructure such as Open Government
Data and deployed sensor networks (IoT) in cities
• Citizens’ participation through apps in their
smartphones
– Or go for big companies’ “smart city in a box”
solutions

129
What is a Smart Sustainable City?
A smart sustainable city is an innovative city that uses
information and communication technologies and
other means to improve quality of life, efficiency of
urban operation and services, and competitiveness,
while ensuring that it meets the needs of present and
future generations with respect to economic, social and
environmental aspects
https://itunews.itu.int/en/5215-What-is-a-smart-sustainable-city.note.aspx

130
Smart Diamond of Smart Cities

131
Smart City Applications
• sadfafd

132
What is an Ambient
Assisted City?
• A city aware of the special needs of ALL its citizens,
particularly those with disabilities or about to lose
their autonomy:
– Elderly people
• The "Young Old" 65-74
• The "Old" 75-84
• The "Oldest-Old" 85+
– People with disabilities
• Physical
• Sensory (visual, hearing)
• Intellectual

133
Age-friendly Smarter Cities
• The main attribute of a Smart City is efficiency
• An Age-friendly city is an inclusive and accessible
urban environment that promotes active ageing
• The main attributes of an Ambient Assisted
(Smarter) City are:
– Livable
– Accessible
– Healthy
– Inclusive
– Participative
[WHO Global Network of Age-friendly Cities]

135
• Smart Cities seek the participation of citizens:
– To enrich the knowledge gathered about a city
not only with government-provided or networked
sensors' provided data, but also with highly
dynamic user-generated data
• BUT, how can we ensure that users and their
generated data can be trusted and has enough
quality?
– W3C has created the PROV Data Model, for provenance
interchange
Citizen Participation

136
User-generated Data: Google Maps vs.
Open Street Map
• OSM is an excellent cartographic product driven by user contributions
• Google Maps has progressed from mapping for the world to mapping from the world,
where cartography is not the end product, but rather the necessary means for:
– Google’s autonomous car initiative, combine sensors, GPS and 3D maps for self-driving cars.
– Google’s Project Wing: a drone-based delivery systems to make use of a detailed 3D model
of the world to quickly link supply to demand
• By connecting the geometrical content of its Google Maps databases to digital traces
that it collects, Google can assign meaning to space, transforming it into place.
– Mapping by machines if not about “you are here”, but to understand who you are, where
you should be heading, what you could be doing there!

137
CrowdSensing
• Individuals with sensing and computing devices collectively
share data and extract information to measure and map
phenomena of common interest

138
Personal Data
• Defined as "any information
relating to an identified or
identifiable natural person
("data subject")”

139
• There is a need to analyze the impact that
citizens may have on improving, extending
and enriching the data
– Quality of the provided data may vary from one
citizen to another, not to mention the possibility
of someone's interest in populating the system
with fake data
• Duplication, miss-classification, mismatching and data
enrichment issues
Problems associated to
User-provided Data

140
Urban Intelligence / Analytics
• Broad Data aggregates data from heterogeneous sources:
– Open Government Data repositories and IoT deployments
– User-supplied data through social networks or apps
– Public private sector data or
– End-user private data
• Humongous potential on correlating and analysing Broad
Data in the city context:
– Leverage digital traces left by citizens in their daily interactions
with the city to gain insights about why, how and when they do
things
– We can progress from Open City Data to Open Data Knowledge
• Energy saving, improve health monitoring, optimized transport
system, filtering and recommendation of contents and services

141
Smarter Cities
• Smarter Cities  cities that do not only manage their
resources more efficiently but also are aware of the
citizens’ needs.
– Human/city interactions leave digital traces that can be
compiled into comprehensive pictures of human daily facets
– Analysis and discovery of the information behind the big
amount of Broad Data captured on these smart cities
deployment
Smarter Cities= Internet of Things + Broad Data + Citizen
Participation through Smartphones + Urban Analytics

142
Data challenges of Smart
Cities
• Data coverage and access (openness)
• Data integration and interoperability (data standards) –
overcoming the silo and resistance to change
• Data quality and provenance: veracity (accuracy, fidelity),
uncertainty, error, bias, reliability, calibration, lineage
• Quality, veracity and transparency of data analytics
• Data interpretation and management issues
• Paradigm shift towards data-driven decision making
• Security and privacy: stem data breaches and fraud
• Skills and organizational capabilities and capacities

143
Analytics in the Smart City: Data-
driven decision making

144
Standardization in Smart Cities:
Vocabularies and Indicators
• UNE 178301 rule developed by AENOR (Spanish Association of
Normalization and Certification) establishes a set of requisites for the
reuse of Open Data generated by Public Administrations in Smart Cities.
– http://www.aenor.es/aenor/actualidad/actualidad/noticias.asp?campo=1&codigo=3526
4#.VjmsffmrQU1
• ISO 37120:2014 indicators a) themes and b) energy example

145
From Open Data to Open Knowledge

146
Perspectivas de crecimiento de
IoT: realidad o promesa
• Success stories in the following domains:
– Intelligent Waste Management
– Animals and Environment Monitoring
– Smart Grids: IoT and knowledge based control for energy
efficiency
– Comprehensive system for agriculture intelligence
• Internet of Things Success Stories #1 to #3:
– https://www.smart-
action.eu/publications/archive/2015/10/55099c948b1ac6
826c142aa6fcd402e4/

147
IoT & Big Data
• IoT is also expected to generate large
amounts of data from diverse locations, with
the consequent necessity for quick
aggregation of the data, and an increase in
the need to index, store, and process such
data more effectively

148
IoT & Big DataTensHundredsThousandsMillionsBillionsConnections
Internet of Things
Machine-to-Machine
Isolated
(autonomous, disconnected)
Monitored
Smart Systems
(Intelligence in Subnets of Things )
Telemetry
and
Telematics
Smart Homes
Connected Cars
Intelligent Buildings
Intelligent Transport
Systems
Smart Meters and Grids
Smart Retailing
Smart Enterprise
Management
Remotely controlled
and managed
Building
automation
Manufacturing
Security
Utilities
Internet of Things
Sensors
Devices
Systems
Things
Processes
People
Industries
Products
Services
Growth in connections generates
an unparalleled scale of data
Source: Machina Research 2014

149
From M2M to IoT towards Big Data
Data
Big data
Changing data
models
Real-time Processing
Aggregation
Internet of Things
Large estates of devices
Evolving applications
All forms of data
Data streaming and
processing
Pre-IoT (M2M)
Limited estate of
devices
Single purpose
applications
Structured / Semi-
structured
Data transfers
(sensors and actuators)

150
Data has changed
• 90% of the world’s data
was created in the last two
years
• 80% of enterprise data is
unstructured
• Unstructured data growing
2x faster than structured

151
Nature of Data in IoT
• Heterogeneity makes IoT devices hardly interoperable
• Data collected is multi-modal, diverse, voluminous
and often supplied at high speed
• IoT data management imposes heavy challenges on
information systems

152
¿Qué es Big Data?
• "Big Data are high-volume, high-velocity, and/or high-variety
information assets that require new forms of processing to
enable enhanced decision making, insight discovery and
process optimization“ Gartner, 2012
– El término “Big Data” se originó dentro de la comunidad open source,
donde hubo un esfuerzo por desarrollar procesos de análisis que
fueran más rápidos y escalables que el data warehousing tradicional,
y pudieran extraer valor de los inmensos volúmenes de datos no
estructurados producidos a diario por usuarios web
• Es una oportunidad para encontrar percepciones en nuevos y
tipos emergentes de datos y contenidos, para hacer a tu
negocio más ágil, y para responder preguntas que fueron
consideradas con anterioridad fuera de tu alcance.

153
Big Data Evolution
• Data explosion!!
– 48 hours of data from stock market ~ 5 TB
– Semi and non-structured data provided in real-time through social networks
– Google processes PB/hour
• Bioinformatics – huge datasets about genetics and drugs
• Money whitening / terrorist funding, Spatial Data
• 85% of Fortune 500 organizations are not able to process Big Data to gain
competitive advantage – Gartner
• Currently more than 1.9 zettabytes of data are being produced

154
Necesidad de Big Data Analytics
• La percepción de los procesos de Data Warehousing es que
son lentos y limitados en escalabilidad
• La necesidad de converger datos de varias fuentes, tanto
estructuradas como no estructuradas
• Es crítico el acceso a la información para extraer valor de las
fuentes de datos incluyendo dispositivos móviles, RFID, la web
y otro largo listado de tecnologías sensoriales automatizadas.

155
Características de Big Data

157
IoT & Big Data
• The more data that is created, the better understanding and
wisdom people can obtain

159
Types of Analytics (II)
• Predictive analysis enables you to move from
sense and respond to predict and act

162
How does Big Data Analytics work?
Source: Virtualisation and Validation of Smart City Data. Dr Sefki Kolozali. Dr Payam Barnaghi

163
Apache Hadoop
• Hadoop es una framework gratuita en Java para procesar grandes
volúmenes de datos en un entorno de computación distribuido
– Hace posible la ejecución de aplicaciones sobre sistemas con miles de nodos
que procesan miles de terabytes
– Su sistema de ficheros distribuido facilita la rápida transferencia de datos
entro nodos y permite al sistema seguir operando ininterrumpidamente en
caso de fallo de un nodo
– Inspirado por Google MapReduce, un modelo de computación donde una
aplicación se divide en varias partes
• Cada una de esas partes (fragmentos o bloques) puede ser ejecutada en cualquier
nodo de un clúster
– El ecositema actual de Apache Hadoop consiste de:
• Hadoop kernel, MapReduce, el sistema de ficheros distribuido de Hadoop (HDFS) y
otros proyectos relacionados como Apache Hive, HBase and Zookeeper.
– Usado por los grandes agentes de la industria Google, Yahoo and IBM

164
Apache Spark
• Apache Spark provides programmers with an application programming
interface centered on a data structure called the resilient distributed
dataset (RDD), a read-only multiset of data items distributed over a cluster
of machines, that is maintained in a fault-tolerant way.
– It was developed in response to limitations in the MapReduce cluster
computing paradigm, which forces a particular linear dataflow structure on
distributed programs
– Oriented to stream data processing allowing for CEP (Complex Event
Processing)

165
Data Management Solutions in IoT (I)
Scalability
Heterogeneity
Agility & Flexibility
in
Applications, Devices
and Connectivity
Scalability
Flexibility
Analytics
Unified View
in
Data
M2M & IoT Application
Platforms
Data Databases
SQL
(Oracle, IBM, etc.)
for structured data
Hybrid
(SAP Hana, VoltDB, etc.)
for speed and heterogeneity
NoSQL
(MongoDB, Cassandra, etc.)
for agility and heterogeneity

166
Data Management Solutions in IoT (II)

168
Summary: IoT Benefits (II)

170
Summary: Challenges of IoT (I)
• Platform : form and design of the products (UI and UX) , analytics tools
used to deal with the massive data streaming from all products in a secure
way , and scalability which means wide adoption of protocols like IPv6 in
all vertical and horizontal markets .
• Connectivity: Connectivity includes all parts of the consumer’s day and
night using wearables, smart cars, smart homes, and in the big scheme
smart cities.
• Business Model: The bottom line is a big motivation for starting, investing
in, and operating any business, without a sound and solid business models
for IoT we will have another bubble , this model must satisfied all the
requirements for all kinds of e-commerce; vertical markets, horizontal
markets and consumer markets.
• Killer Applications: Three functions needed in any killer applications,
control “things”, collect “data”, analyze “data”.
• Security: The IoT introduces unique physical security concerns implying
that IoT privacy concerns are complex and not always readily evident.

171
Summary: Challenges of IoT (II)
• Learn how to make money with it – make it
sustainable
– Finding meaningful use cases is key to success
– Visions are allowed, but first bills have to be paid
– New business models are key to making money with IoT
– Business models will have an impact on the architecture of
solutions!
• IoT can be complex!
– Keep it simple by structured data models and good scale
– Keep it understandable for customers and consumers

172
Summary: Challenges of IoT (III)
• Society: People, security, privacy
– A policy for people in the Internet of Things: Legislation
– Decisions – do not delegate too much of our decision making and
freedom of choice to things and machines
– Privacy and Security will distinguish between success and failure
– Managing one’s own privacy will become a complex task – and needs
to be kept simple
– Historical personal data availability – who will delete the data?
• Environmental aspects
– Resource efficiency
– Pollution and disaster avoidance

173
Summary: Challenges of IoT (IV)
• Technological
– Architecture (edge devices, servers, discovery services, security, etc.)
– Governance, naming, identity, interfaces
– Service openness, interoperability
– Connections of real and virtual world
– Standards
• Establishing a common set of standards
– The same type of cabling,
– The same applications or programming
– The same protocol or set of rules that will apply to all
• Energy sources for millions -even billions - of sensors
– Wind
– Solar,
– Hydro-electric

174
Conclusión
• Internet de las Cosas al Servicio de las Personas:
– https://www.youtube.com/watch?v=Ge0q7jJuvbs

175
Conclusión
• Internet de las Cosas al Servicio de las Personas:
– https://www.youtube.com/watch?v=Ge0q7jJuvbs

176
Internet de las Cosas: del Concepto a la
Realidad
Bizkaia Enpresa Digitala, Parque Tecnológico de Bizkaia. Edificio Tecnalia, #204
27 de Octubre de 2016, 9:00-13:00
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es

177
References
• Internet of Things towards Ubiquitous and Mobile Computing
– http://research.microsoft.com/en-
us/UM/redmond/events/asiafacsum2010/presentations/Guihai-
Chen_Oct19.pdf
• 5 key questions to ask about the Internet of Things
– http://www.slideshare.net/DeloitteUS/5-questions-the-iot-internet-of-things
• Internet Connected Objects for Reconfigurable Eco-systems
– https://docbox.etsi.org/workshop/2012/201210_M2MWORKSHOP/zz_POSTE
RS/iCore.pdf
• Internet of Things and Big Data – Bosch, August 2015
– https://www.bosch-
si.com/media/bosch_software_innovations/media_landingpages/connectedw
orld_1/bcw_2016/bcw_1/download_page_1/download_page/bcw16_mongo
db_collateral_followup_sponsor.pdf
• The internet of things and big data: Unlocking the power
– http://www.zdnet.com/article/the-internet-of-things-and-big-data-unlocking-
the-power/

178
References
• Deconstructing the Internet of Things
– https://jenson.org/deconstructing-the-iot/
• Mobile in IoT Context ? Mobile Applications in "Industry 4.0“
– http://www.slideshare.net/MobileTrendsConference/karol-kalisz-vitaliy-rudnytskiy-
mobile-in-iot-context-mobile-applications-in-industry-40
• Inside the Internet of Things (IoT) – A primer on the technologies building
the IoT – Deloitte
– http://dupress.com/articles/iot-primer-iot-technologies-applications/
• Internet of Things (IoT) - We Are at the Tip of An Iceberg – Dr. Mazlan
Abbas
– http://www.slideshare.net/mazlan1/internet-of-things-iot-we-are-at-the-tip-of-an-
iceberg
• Infographic: What are Beacons and What Do They Do?
– https://kontakt.io/blog/infographic-beacons/
• iBeacon
– https://en.wikipedia.org/wiki/IBeacon

179
References
• ITU News – What is a smart sustainable city?,
– https://itunews.itu.int/en/5215-What-is-a-smart-sustainable-
city.note.aspx
• Frost & Sullivan's Predictions for the Global Energy and
Environment Market,
– http://www.slideshare.net/FrostandSullivan/frost-sullivans-
predictions-for-the-global-energy-and-environment-market
• Fog Computing with VORTEX
– http://www.slideshare.net/Angelo.Corsaro/20141210-fog
• What Exactly Is The "Internet of Things"? – A graphic primer
behind the term & technologies
– http://postscapes.com/what-exactly-is-the-internet-of-things-
infographic

180
References
• Innovating the Smart Cities, Syam Madanapalli | IEEE Smart Tech
Workshop 2015, http://www.slideshare.net/smadanapalli/innovating-the-
smart-cities
• Kitchin, R., Lauriault, T. and McArdle, G. (2015) Knowing and governing
cities through urban indicators, city benchmarking and real-time
dashboards. Regional Studies, Regional Science 2: 1-28,
http://rsa.tandfonline.com/doi/full/10.1080/21681376.2014.983149
• Towards Smart City: Making Government Data Work with Big Data
Analysis, Charles Mok, 24 September 2015,
http://www.slideshare.net/mok/towards-smart-city-making-government-
data-work-with-big-data-analysis-53176591
• Mining in the Middle of the City: The needs of Big Data for Smart Cities, Dr.
Antonio Jara, http://www.slideshare.net/IIG_HES/mining-in-the-middle-
of-the-city-the-needs-of-big-data-for-smart-cities

181
References
• The Big 'Big Data' Question: Hadoop or Spark?
– http://www.datasciencecentral.com/profiles/blogs/the-big-big-data-question-
hadoop-or-spark
• Hadoop vs. Spark: The New Age of Big Data
– http://www.datamation.com/data-center/hadoop-vs.-spark-the-new-age-of-
big-data.html
• Comparing 11 IoT Development Platforms
– https://dzone.com/articles/iot-software-platform-comparison

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