SlideShare a Scribd company logo
1
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
2
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
3
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
4
¿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/
5
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
6
Visión de la Internet del Futuro
7
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
8
Arquitectura de la Internet del
Futuro
9
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
10
IoT: Infografías
11
12
Internet de las Cosas … conectando
información, gente y cosas
13
Evolución hacia IoT
• Desde la Web a la Web Social hacia IoT
14
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
15
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
16
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
17
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
18
Sensors
19
Connectivity
20
People & Process
21
IoT = Sensors + Connectivity +
Processing for People
22
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
23
Internet de las Cosas: mucho más
que cosas inteligentes
24
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
25
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
26
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.
27
Internet of Everything (II)
28
How big is IoT?
29
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
30
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
$1
“
31
Tipos de Internet de las Cosas
• Al menos dos sabores:
– Consumer IoT (CIoT): orientada a consumidores
– Industrial IoT (IIoT)
• Industria 4.0
32
Consumer Internet of Things (CIoT)
• The Consumer Internet of Things (CIoT) represents
the class of consumer-oriented applications where:
– Devices are consumer devices, such as smart appliances,
e.g. refrigerator, washer, dryer, personal gadgets such as,
fitness sensors, Google Glasses, etc.
– Data volumes and rates are relatively low
– Applications are not mission or safety critical, e.g., the
failure of fitness gadget will make you, at worse, upset, but
won’t cause any harm
– CIoT applications tend to be “consumer-centric”
33
Sectores IoT de Consumo
34
IoT impulse: Smart Cities, consumer
objects, mobile sensing, smart metering
35
Personal data: SmartWatch & Health-
promoting Data Devices
36
Quantified Self & Life
Logging
• Quantified self is self-knowledge through self-tracking with technology
– Movement to incorporate technology into data acquisition on aspects of a
person's daily life in terms of inputs (e.g. food consumed, quality of
surrounding air), states (e.g. mood, arousal, blood oxygen levels), and
performance (mental and physical)
• Self-monitoring and self-sensing through wearable sensors (EEG, ECG, video, etc.)
and wearable computing  lifelogging
• Application areas:
– Health and wellness improvement
– Improve personal or professional productivity
• Products and companies:
– Apple Watch, Fitbit tracker, Jawbone UP, Pebble, Withings scale
37
SmartWatch Comparison
37
38
Google Glass
• Su misión es producir un ubiquitous computer de venta
masiva
– Lanzadas para los desarrolladores de Google I/O por
1500$ en el año 2013
• Muestra información disponible sin utilizar las manos,
accede a Internet mediante órdenes de voz, de manera
comparable a Google Now
39
• Google Home
– Features
• Amazon Echo
– Alexa API
Audible Computing
• Apple AirPods
– Comparison
40
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/
41
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”
42
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
43
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.
44
Telehealth system
45
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".
46
Industria 4.0 y IoT
47
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
48
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
49
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
50
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):
51
IoT Key Components
52
IoT Architecture
Sensing Layer
Communication Layer
Management Layer
53
Ecosistema de IoT
54
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
55
What do IoT apps do? (II)
56
Dominios de Aplicación
57
Exemplary IoT Solutions
58
Combinando Dominios IoT
59
Combinando Dominios IoT
60
Combinando Dominios IoT
61
De la Granja Digital a la Mesa
62
IoT in
Precision
Agriculture
and Farming
63
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)
64
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.
65
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
66
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
67
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:
68
The Maker Movement
69
The Maker
Movement
70
3D Printing
71
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
72
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.
73
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)
74
IPv6 vs. IPv4
75
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
76
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
77
Protocolos
para IoT (I)
78
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
80
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)
82
• 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
83
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)
85
EddyStone & Physical Web
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
101
Mayores Proveedores
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]
134
Silver Economy
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)
Source: Machina Research 2014
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
156
Las 4 Vs de Big Data
157
IoT & Big Data
• The more data that is created, the better understanding and
wisdom people can obtain
158
Types of Analytics (I)
159
Types of Analytics (II)
• Predictive analysis enables you to move from
sense and respond to predict and act
160
Types of Analytics (III)
161
Types of Analytics (II)
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
Source: Machina Research 2014
166
Data Management Solutions in IoT (II)
167
Summary: IoT Benefits (I)
168
Summary: IoT Benefits (II)
169
IoT Benefits
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

More Related Content

What's hot

Promoting Sustainability through Energy-aware Linked Data Devices
Promoting Sustainability through Energy-aware Linked Data DevicesPromoting Sustainability through Energy-aware Linked Data Devices
Promoting Sustainability through Energy-aware Linked Data Devices
Diego López-de-Ipiña González-de-Artaza
 
Citizen-centric Linked Data Services for Smarter Cities
Citizen-centric Linked Data Services for Smarter CitiesCitizen-centric Linked Data Services for Smarter Cities
Citizen-centric Linked Data Services for Smarter Cities
Diego López-de-Ipiña González-de-Artaza
 
Towards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and CitizensTowards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and Citizens
Diego López-de-Ipiña González-de-Artaza
 
IES Cities Project Overview and API: IES Cities Hackathon, Zaragoza
IES Cities Project Overview and API: IES Cities Hackathon, ZaragozaIES Cities Project Overview and API: IES Cities Hackathon, Zaragoza
IES Cities Project Overview and API: IES Cities Hackathon, Zaragoza
Diego López-de-Ipiña González-de-Artaza
 
Towards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and CitizensTowards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and Citizens
Diego López-de-Ipiña González-de-Artaza
 
Bringing together smart things and people to realize smarter environments sho...
Bringing together smart things and people to realize smarter environments sho...Bringing together smart things and people to realize smarter environments sho...
Bringing together smart things and people to realize smarter environments sho...
Diego López-de-Ipiña González-de-Artaza
 
Collaboration centred cities through urban apps based on open and user-genera...
Collaboration centred cities through urban apps based on open and user-genera...Collaboration centred cities through urban apps based on open and user-genera...
Collaboration centred cities through urban apps based on open and user-genera...
Diego López-de-Ipiña González-de-Artaza
 
Enabling Citizen-empowered Apps over Linked Data
Enabling Citizen-empowered Apps over Linked DataEnabling Citizen-empowered Apps over Linked Data
Enabling Citizen-empowered Apps over Linked Data
Diego López-de-Ipiña González-de-Artaza
 
SofwarøSfera Presentation
SofwarøSfera PresentationSofwarøSfera Presentation
SofwarøSfera Presentation
Diego López-de-Ipiña González-de-Artaza
 
Dealing with the need for Infrastructural Support in Ambient Intelligence
Dealing with the need for Infrastructural Support in Ambient IntelligenceDealing with the need for Infrastructural Support in Ambient Intelligence
Dealing with the need for Infrastructural Support in Ambient Intelligence
Diego López-de-Ipiña González-de-Artaza
 
The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...
The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...
The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...
Diego López-de-Ipiña González-de-Artaza
 
Transiting to Open Knowledge by fostering Collaboration through CO-CREATION
Transiting to Open Knowledge by fostering Collaboration through CO-CREATIONTransiting to Open Knowledge by fostering Collaboration through CO-CREATION
Transiting to Open Knowledge by fostering Collaboration through CO-CREATION
Diego López-de-Ipiña González-de-Artaza
 
Towards more Elderly-friendly Ambient Assisted Cities
Towards more Elderly-friendly Ambient Assisted CitiesTowards more Elderly-friendly Ambient Assisted Cities
Towards more Elderly-friendly Ambient Assisted Cities
Diego López-de-Ipiña González-de-Artaza
 
Internet de las Cosas: del Concepto a la Realidad
Internet de las Cosas: del Concepto a la RealidadInternet de las Cosas: del Concepto a la Realidad
Internet de las Cosas: del Concepto a la Realidad
Diego López-de-Ipiña González-de-Artaza
 
Towards Citizen Co-created Public Service Apps
Towards Citizen Co-created Public Service AppsTowards Citizen Co-created Public Service Apps
Towards Citizen Co-created Public Service Apps
Diego López-de-Ipiña González-de-Artaza
 
Empowering citizens to turn them into cocreators of demand driven public serv...
Empowering citizens to turn them into cocreators of demand driven public serv...Empowering citizens to turn them into cocreators of demand driven public serv...
Empowering citizens to turn them into cocreators of demand driven public serv...
Diego López-de-Ipiña González-de-Artaza
 
Combining ICT and User Participation to give place to Smarter Cities through ...
Combining ICT and User Participation to give place to Smarter Cities through ...Combining ICT and User Participation to give place to Smarter Cities through ...
Combining ICT and User Participation to give place to Smarter Cities through ...
Diego López-de-Ipiña González-de-Artaza
 
VET4SBO Level 1 module 3 - unit 1 - v1.0 en
VET4SBO Level 1   module 3 - unit 1 - v1.0 enVET4SBO Level 1   module 3 - unit 1 - v1.0 en
VET4SBO Level 1 module 3 - unit 1 - v1.0 en
Karel Van Isacker
 
Internet of People: towards a Human-centric computing for Social Good
Internet of People: towards a Human-centric computing for Social GoodInternet of People: towards a Human-centric computing for Social Good
Internet of People: towards a Human-centric computing for Social Good
Diego López-de-Ipiña González-de-Artaza
 
What makes smart cities “Smart”?
What makes smart cities “Smart”? What makes smart cities “Smart”?
What makes smart cities “Smart”?
PayamBarnaghi
 

What's hot (20)

Promoting Sustainability through Energy-aware Linked Data Devices
Promoting Sustainability through Energy-aware Linked Data DevicesPromoting Sustainability through Energy-aware Linked Data Devices
Promoting Sustainability through Energy-aware Linked Data Devices
 
Citizen-centric Linked Data Services for Smarter Cities
Citizen-centric Linked Data Services for Smarter CitiesCitizen-centric Linked Data Services for Smarter Cities
Citizen-centric Linked Data Services for Smarter Cities
 
Towards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and CitizensTowards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and Citizens
 
IES Cities Project Overview and API: IES Cities Hackathon, Zaragoza
IES Cities Project Overview and API: IES Cities Hackathon, ZaragozaIES Cities Project Overview and API: IES Cities Hackathon, Zaragoza
IES Cities Project Overview and API: IES Cities Hackathon, Zaragoza
 
Towards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and CitizensTowards Ambient Assisted Cities and Citizens
Towards Ambient Assisted Cities and Citizens
 
Bringing together smart things and people to realize smarter environments sho...
Bringing together smart things and people to realize smarter environments sho...Bringing together smart things and people to realize smarter environments sho...
Bringing together smart things and people to realize smarter environments sho...
 
Collaboration centred cities through urban apps based on open and user-genera...
Collaboration centred cities through urban apps based on open and user-genera...Collaboration centred cities through urban apps based on open and user-genera...
Collaboration centred cities through urban apps based on open and user-genera...
 
Enabling Citizen-empowered Apps over Linked Data
Enabling Citizen-empowered Apps over Linked DataEnabling Citizen-empowered Apps over Linked Data
Enabling Citizen-empowered Apps over Linked Data
 
SofwarøSfera Presentation
SofwarøSfera PresentationSofwarøSfera Presentation
SofwarøSfera Presentation
 
Dealing with the need for Infrastructural Support in Ambient Intelligence
Dealing with the need for Infrastructural Support in Ambient IntelligenceDealing with the need for Infrastructural Support in Ambient Intelligence
Dealing with the need for Infrastructural Support in Ambient Intelligence
 
The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...
The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...
The quest for Ubiquitous Computing: from Ambient Intelligence to the combinat...
 
Transiting to Open Knowledge by fostering Collaboration through CO-CREATION
Transiting to Open Knowledge by fostering Collaboration through CO-CREATIONTransiting to Open Knowledge by fostering Collaboration through CO-CREATION
Transiting to Open Knowledge by fostering Collaboration through CO-CREATION
 
Towards more Elderly-friendly Ambient Assisted Cities
Towards more Elderly-friendly Ambient Assisted CitiesTowards more Elderly-friendly Ambient Assisted Cities
Towards more Elderly-friendly Ambient Assisted Cities
 
Internet de las Cosas: del Concepto a la Realidad
Internet de las Cosas: del Concepto a la RealidadInternet de las Cosas: del Concepto a la Realidad
Internet de las Cosas: del Concepto a la Realidad
 
Towards Citizen Co-created Public Service Apps
Towards Citizen Co-created Public Service AppsTowards Citizen Co-created Public Service Apps
Towards Citizen Co-created Public Service Apps
 
Empowering citizens to turn them into cocreators of demand driven public serv...
Empowering citizens to turn them into cocreators of demand driven public serv...Empowering citizens to turn them into cocreators of demand driven public serv...
Empowering citizens to turn them into cocreators of demand driven public serv...
 
Combining ICT and User Participation to give place to Smarter Cities through ...
Combining ICT and User Participation to give place to Smarter Cities through ...Combining ICT and User Participation to give place to Smarter Cities through ...
Combining ICT and User Participation to give place to Smarter Cities through ...
 
VET4SBO Level 1 module 3 - unit 1 - v1.0 en
VET4SBO Level 1   module 3 - unit 1 - v1.0 enVET4SBO Level 1   module 3 - unit 1 - v1.0 en
VET4SBO Level 1 module 3 - unit 1 - v1.0 en
 
Internet of People: towards a Human-centric computing for Social Good
Internet of People: towards a Human-centric computing for Social GoodInternet of People: towards a Human-centric computing for Social Good
Internet of People: towards a Human-centric computing for Social Good
 
What makes smart cities “Smart”?
What makes smart cities “Smart”? What makes smart cities “Smart”?
What makes smart cities “Smart”?
 

Viewers also liked

Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2
Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2
Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2
Diego López-de-Ipiña González-de-Artaza
 
IoT and M2M for Software Developers
IoT and M2M for Software DevelopersIoT and M2M for Software Developers
IoT and M2M for Software Developers
Pascal Bodin
 
Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...
Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...
Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...
Diego López-de-Ipiña González-de-Artaza
 
Future Internet: Internet of Things, Cloud Computing & Linked Data --> Big Data
Future Internet: Internet of Things, Cloud Computing & Linked Data --> Big DataFuture Internet: Internet of Things, Cloud Computing & Linked Data --> Big Data
Future Internet: Internet of Things, Cloud Computing & Linked Data --> Big Data
Diego López-de-Ipiña González-de-Artaza
 
¿Cómo formular objetivos de investigación?
¿Cómo formular objetivos de investigación?¿Cómo formular objetivos de investigación?
¿Cómo formular objetivos de investigación?
Andres Castro
 
Sprinting to Value in Industry 4.0
Sprinting to Value in Industry 4.0Sprinting to Value in Industry 4.0
Sprinting to Value in Industry 4.0
Boston Consulting Group
 
Objetivo y cómo de redacta
Objetivo y cómo de redactaObjetivo y cómo de redacta
Objetivo y cómo de redacta
LosTresTeckels
 
Verbos que se utilizan en la redacción de objetivos
Verbos que se utilizan en la redacción de objetivosVerbos que se utilizan en la redacción de objetivos
Verbos que se utilizan en la redacción de objetivos
Centro de Investigaciones Turisticas
 
Cómo elaborar un marco teórico
Cómo elaborar un marco teóricoCómo elaborar un marco teórico
Cómo elaborar un marco teórico
SasNoizemaker
 
Paso a Paso para construir un marco teórico
 Paso a Paso para construir un marco teórico Paso a Paso para construir un marco teórico
Paso a Paso para construir un marco teórico
José Davidd Meza
 

Viewers also liked (10)

Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2
Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2
Hacia la Internet del Futuro: Web Semántica y Open Linked Data, Parte 2
 
IoT and M2M for Software Developers
IoT and M2M for Software DevelopersIoT and M2M for Software Developers
IoT and M2M for Software Developers
 
Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...
Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...
Internet del Futuro: Internet de las Cosas, Computación en la Nube y la Web d...
 
Future Internet: Internet of Things, Cloud Computing & Linked Data --> Big Data
Future Internet: Internet of Things, Cloud Computing & Linked Data --> Big DataFuture Internet: Internet of Things, Cloud Computing & Linked Data --> Big Data
Future Internet: Internet of Things, Cloud Computing & Linked Data --> Big Data
 
¿Cómo formular objetivos de investigación?
¿Cómo formular objetivos de investigación?¿Cómo formular objetivos de investigación?
¿Cómo formular objetivos de investigación?
 
Sprinting to Value in Industry 4.0
Sprinting to Value in Industry 4.0Sprinting to Value in Industry 4.0
Sprinting to Value in Industry 4.0
 
Objetivo y cómo de redacta
Objetivo y cómo de redactaObjetivo y cómo de redacta
Objetivo y cómo de redacta
 
Verbos que se utilizan en la redacción de objetivos
Verbos que se utilizan en la redacción de objetivosVerbos que se utilizan en la redacción de objetivos
Verbos que se utilizan en la redacción de objetivos
 
Cómo elaborar un marco teórico
Cómo elaborar un marco teóricoCómo elaborar un marco teórico
Cómo elaborar un marco teórico
 
Paso a Paso para construir un marco teórico
 Paso a Paso para construir un marco teórico Paso a Paso para construir un marco teórico
Paso a Paso para construir un marco teórico
 

Similar to Internet de las Cosas: del Concepto a la Realidad

Internet of Things (IoT) - IK
Internet of Things (IoT) - IKInternet of Things (IoT) - IK
Internet of Things (IoT) - IK
Ilgın Kavaklıoğulları
 
IOT
IOTIOT
The-Internet-Of-Things-4th-Industrial-Revolution.pptx
The-Internet-Of-Things-4th-Industrial-Revolution.pptxThe-Internet-Of-Things-4th-Industrial-Revolution.pptx
The-Internet-Of-Things-4th-Industrial-Revolution.pptx
HadHic
 
IoT.pptx
IoT.pptxIoT.pptx
IoT.pptx
ssuser878570
 
IoT
IoT  IoT
IoT and Big Data.pptx
IoT and Big Data.pptxIoT and Big Data.pptx
IoT and Big Data.pptx
cetabac
 
IoT and Big Data.pptx
IoT and Big Data.pptxIoT and Big Data.pptx
IoT and Big Data.pptx
Sampath737246
 
IoT and Big Data.pptx
IoT and Big Data.pptxIoT and Big Data.pptx
IoT and Big Data.pptx
ssuser2cc0d4
 
The internet of things
The internet of thingsThe internet of things
The internet of things
Augustine Micahel
 
IoT Intro.pptx
IoT Intro.pptxIoT Intro.pptx
IoT Intro.pptx
Gaurav Sumer Singh
 
Iot amey p naik
Iot amey p naikIot amey p naik
Iot amey p naik
Amey Naik
 
introduction to Internet of things presentation
introduction to Internet of things presentationintroduction to Internet of things presentation
introduction to Internet of things presentation
Kavitabani1
 
Ioe module 1
Ioe module 1Ioe module 1
Ioe module 1
nikshaikh786
 
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-gInternet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
Mohan Kumar G
 
Internet of Things
Internet of ThingsInternet of Things
Internet of Things
Mphasis
 
IoT : Research, Development, Challenges
IoT: Research, Development, ChallengesIoT: Research, Development, Challenges
IoT : Research, Development, Challenges
baddi youssef
 
Internet of-thing
Internet of-thingInternet of-thing
Internet of-thing
Rishab garg
 
L1-Intro-IoT.pptx
L1-Intro-IoT.pptxL1-Intro-IoT.pptx
L1-Intro-IoT.pptx
jayakumar703719
 
What is IoT | The Structure of IoT | Future of IoT
What is IoT | The Structure of IoT | Future of IoTWhat is IoT | The Structure of IoT | Future of IoT
What is IoT | The Structure of IoT | Future of IoT
International Institute of Information Technology (I²IT)
 
IOT- information Technology
IOT- information Technology IOT- information Technology
IOT- information Technology
khushi pokharna
 

Similar to Internet de las Cosas: del Concepto a la Realidad (20)

Internet of Things (IoT) - IK
Internet of Things (IoT) - IKInternet of Things (IoT) - IK
Internet of Things (IoT) - IK
 
IOT
IOTIOT
IOT
 
The-Internet-Of-Things-4th-Industrial-Revolution.pptx
The-Internet-Of-Things-4th-Industrial-Revolution.pptxThe-Internet-Of-Things-4th-Industrial-Revolution.pptx
The-Internet-Of-Things-4th-Industrial-Revolution.pptx
 
IoT.pptx
IoT.pptxIoT.pptx
IoT.pptx
 
IoT
IoT  IoT
IoT
 
IoT and Big Data.pptx
IoT and Big Data.pptxIoT and Big Data.pptx
IoT and Big Data.pptx
 
IoT and Big Data.pptx
IoT and Big Data.pptxIoT and Big Data.pptx
IoT and Big Data.pptx
 
IoT and Big Data.pptx
IoT and Big Data.pptxIoT and Big Data.pptx
IoT and Big Data.pptx
 
The internet of things
The internet of thingsThe internet of things
The internet of things
 
IoT Intro.pptx
IoT Intro.pptxIoT Intro.pptx
IoT Intro.pptx
 
Iot amey p naik
Iot amey p naikIot amey p naik
Iot amey p naik
 
introduction to Internet of things presentation
introduction to Internet of things presentationintroduction to Internet of things presentation
introduction to Internet of things presentation
 
Ioe module 1
Ioe module 1Ioe module 1
Ioe module 1
 
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-gInternet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
Internet-of-things- (IOT) - a-seminar - ppt - by- mohan-kumar-g
 
Internet of Things
Internet of ThingsInternet of Things
Internet of Things
 
IoT : Research, Development, Challenges
IoT: Research, Development, ChallengesIoT: Research, Development, Challenges
IoT : Research, Development, Challenges
 
Internet of-thing
Internet of-thingInternet of-thing
Internet of-thing
 
L1-Intro-IoT.pptx
L1-Intro-IoT.pptxL1-Intro-IoT.pptx
L1-Intro-IoT.pptx
 
What is IoT | The Structure of IoT | Future of IoT
What is IoT | The Structure of IoT | Future of IoTWhat is IoT | The Structure of IoT | Future of IoT
What is IoT | The Structure of IoT | Future of IoT
 
IOT- information Technology
IOT- information Technology IOT- information Technology
IOT- information Technology
 

More from Diego López-de-Ipiña González-de-Artaza

Democratizing co-production of thematic co-explorations for Citizen Observato...
Democratizing co-production of thematic co-explorations for Citizen Observato...Democratizing co-production of thematic co-explorations for Citizen Observato...
Democratizing co-production of thematic co-explorations for Citizen Observato...
Diego López-de-Ipiña González-de-Artaza
 
Digital Twin aiding more effective Digital Maintenance
Digital Twin aiding more effective Digital MaintenanceDigital Twin aiding more effective Digital Maintenance
Digital Twin aiding more effective Digital Maintenance
Diego López-de-Ipiña González-de-Artaza
 
Humanized Computing: the path towards higher collaboration and reciprocal lea...
Humanized Computing: the path towards higher collaboration and reciprocal lea...Humanized Computing: the path towards higher collaboration and reciprocal lea...
Humanized Computing: the path towards higher collaboration and reciprocal lea...
Diego López-de-Ipiña González-de-Artaza
 
Generative AI How It's Changing Our World and What It Means for You_final.pdf
Generative AI How It's Changing Our World and What It Means for You_final.pdfGenerative AI How It's Changing Our World and What It Means for You_final.pdf
Generative AI How It's Changing Our World and What It Means for You_final.pdf
Diego López-de-Ipiña González-de-Artaza
 
Democratizing Co-Production Of Sustainable Public Services
Democratizing Co-Production Of Sustainable Public Services Democratizing Co-Production Of Sustainable Public Services
Democratizing Co-Production Of Sustainable Public Services
Diego López-de-Ipiña González-de-Artaza
 
Ontological Infrastructure for Interoperable Research Information Systems: HE...
Ontological Infrastructure for Interoperable Research Information Systems: HE...Ontological Infrastructure for Interoperable Research Information Systems: HE...
Ontological Infrastructure for Interoperable Research Information Systems: HE...
Diego López-de-Ipiña González-de-Artaza
 
Fostering multi-stakeholder collaboration through co-production and rewarding
Fostering multi-stakeholder collaboration through co-production and rewarding Fostering multi-stakeholder collaboration through co-production and rewarding
Fostering multi-stakeholder collaboration through co-production and rewarding
Diego López-de-Ipiña González-de-Artaza
 
A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...
A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...
A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...
Diego López-de-Ipiña González-de-Artaza
 
A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...
A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...
A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...
Diego López-de-Ipiña González-de-Artaza
 
PrácticaParticipación-INTERLINK-realizingcoproduction_final.pdf
PrácticaParticipación-INTERLINK-realizingcoproduction_final.pdfPrácticaParticipación-INTERLINK-realizingcoproduction_final.pdf
PrácticaParticipación-INTERLINK-realizingcoproduction_final.pdf
Diego López-de-Ipiña González-de-Artaza
 
INTERLINK: Engaged Research through co-production
INTERLINK: Engaged Research through co-production INTERLINK: Engaged Research through co-production
INTERLINK: Engaged Research through co-production
Diego López-de-Ipiña González-de-Artaza
 
Boosting data-driven innovation in Europe with the support of DIHs
Boosting data-driven innovation in Europe with the support of DIHs Boosting data-driven innovation in Europe with the support of DIHs
Boosting data-driven innovation in Europe with the support of DIHs
Diego López-de-Ipiña González-de-Artaza
 
Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...
Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...
Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...
Diego López-de-Ipiña González-de-Artaza
 
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...
Diego López-de-Ipiña González-de-Artaza
 
Role of Data Incubators shaping European Data Spaces: EDI & REACH cases
Role of Data Incubators shaping European Data Spaces: EDI & REACH casesRole of Data Incubators shaping European Data Spaces: EDI & REACH cases
Role of Data Incubators shaping European Data Spaces: EDI & REACH cases
Diego López-de-Ipiña González-de-Artaza
 
Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...
Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...
Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...
Diego López-de-Ipiña González-de-Artaza
 
ROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios Estándar
ROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios EstándarROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios Estándar
ROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios Estándar
Diego López-de-Ipiña González-de-Artaza
 
Introduction to FAIR Data and Research Objects
Introduction to FAIR Data and Research ObjectsIntroduction to FAIR Data and Research Objects
Introduction to FAIR Data and Research Objects
Diego López-de-Ipiña González-de-Artaza
 
Introducción a Linked Open Data (espacios enlazados y enlazables)
Introducción a Linked Open Data (espacios enlazados y enlazables)Introducción a Linked Open Data (espacios enlazados y enlazables)
Introducción a Linked Open Data (espacios enlazados y enlazables)
Diego López-de-Ipiña González-de-Artaza
 
Red Ontologías Hércules – ROH
Red Ontologías Hércules – ROHRed Ontologías Hércules – ROH
Red Ontologías Hércules – ROH
Diego López-de-Ipiña González-de-Artaza
 

More from Diego López-de-Ipiña González-de-Artaza (20)

Democratizing co-production of thematic co-explorations for Citizen Observato...
Democratizing co-production of thematic co-explorations for Citizen Observato...Democratizing co-production of thematic co-explorations for Citizen Observato...
Democratizing co-production of thematic co-explorations for Citizen Observato...
 
Digital Twin aiding more effective Digital Maintenance
Digital Twin aiding more effective Digital MaintenanceDigital Twin aiding more effective Digital Maintenance
Digital Twin aiding more effective Digital Maintenance
 
Humanized Computing: the path towards higher collaboration and reciprocal lea...
Humanized Computing: the path towards higher collaboration and reciprocal lea...Humanized Computing: the path towards higher collaboration and reciprocal lea...
Humanized Computing: the path towards higher collaboration and reciprocal lea...
 
Generative AI How It's Changing Our World and What It Means for You_final.pdf
Generative AI How It's Changing Our World and What It Means for You_final.pdfGenerative AI How It's Changing Our World and What It Means for You_final.pdf
Generative AI How It's Changing Our World and What It Means for You_final.pdf
 
Democratizing Co-Production Of Sustainable Public Services
Democratizing Co-Production Of Sustainable Public Services Democratizing Co-Production Of Sustainable Public Services
Democratizing Co-Production Of Sustainable Public Services
 
Ontological Infrastructure for Interoperable Research Information Systems: HE...
Ontological Infrastructure for Interoperable Research Information Systems: HE...Ontological Infrastructure for Interoperable Research Information Systems: HE...
Ontological Infrastructure for Interoperable Research Information Systems: HE...
 
Fostering multi-stakeholder collaboration through co-production and rewarding
Fostering multi-stakeholder collaboration through co-production and rewarding Fostering multi-stakeholder collaboration through co-production and rewarding
Fostering multi-stakeholder collaboration through co-production and rewarding
 
A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...
A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...
A Collaborative Environment to Boost Sustainable Engaged Research & Co-Produc...
 
A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...
A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...
A Collaborative Environment to Boost Co-Production of Sustainable Public Serv...
 
PrácticaParticipación-INTERLINK-realizingcoproduction_final.pdf
PrácticaParticipación-INTERLINK-realizingcoproduction_final.pdfPrácticaParticipación-INTERLINK-realizingcoproduction_final.pdf
PrácticaParticipación-INTERLINK-realizingcoproduction_final.pdf
 
INTERLINK: Engaged Research through co-production
INTERLINK: Engaged Research through co-production INTERLINK: Engaged Research through co-production
INTERLINK: Engaged Research through co-production
 
Boosting data-driven innovation in Europe with the support of DIHs
Boosting data-driven innovation in Europe with the support of DIHs Boosting data-driven innovation in Europe with the support of DIHs
Boosting data-driven innovation in Europe with the support of DIHs
 
Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...
Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...
Social Coin: Blockchain-mediated incentivization of citizens for sustainable ...
 
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...
Human-centric Collaborative Services : IoT, Broad Data, Crowdsourcing, Engage...
 
Role of Data Incubators shaping European Data Spaces: EDI & REACH cases
Role of Data Incubators shaping European Data Spaces: EDI & REACH casesRole of Data Incubators shaping European Data Spaces: EDI & REACH cases
Role of Data Incubators shaping European Data Spaces: EDI & REACH cases
 
Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...
Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...
Transiting to SMART COMMUNITIES by fostering Collaboration & CO-CREATION for ...
 
ROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios Estándar
ROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios EstándarROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios Estándar
ROH: Proceso de Ingeniería Ontológica & Uso y Extensión de Vocabularios Estándar
 
Introduction to FAIR Data and Research Objects
Introduction to FAIR Data and Research ObjectsIntroduction to FAIR Data and Research Objects
Introduction to FAIR Data and Research Objects
 
Introducción a Linked Open Data (espacios enlazados y enlazables)
Introducción a Linked Open Data (espacios enlazados y enlazables)Introducción a Linked Open Data (espacios enlazados y enlazables)
Introducción a Linked Open Data (espacios enlazados y enlazables)
 
Red Ontologías Hércules – ROH
Red Ontologías Hércules – ROHRed Ontologías Hércules – ROH
Red Ontologías Hércules – ROH
 

Recently uploaded

Zaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdfZaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdf
AmandaCheung15
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
Jimmy Lai
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
SAI KAILASH R
 
Uncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in LibrariesUncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in Libraries
Brian Pichman
 
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision MakingConnector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
DianaGray10
 
Step-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From ScratchStep-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From Scratch
softsuave
 
How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...
DianaGray10
 
Gen AI: Privacy Risks of Large Language Models (LLMs)
Gen AI: Privacy Risks of Large Language Models (LLMs)Gen AI: Privacy Risks of Large Language Models (LLMs)
Gen AI: Privacy Risks of Large Language Models (LLMs)
Debmalya Biswas
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
ldtexsolbl
 
Use Cases & Benefits of RPA in Manufacturing in 2024.pptx
Use Cases & Benefits of RPA in Manufacturing in 2024.pptxUse Cases & Benefits of RPA in Manufacturing in 2024.pptx
Use Cases & Benefits of RPA in Manufacturing in 2024.pptx
SynapseIndia
 
Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17
Bhajan Mehta
 
Feature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptxFeature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptx
ssuser1915fe1
 
Tailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer InsightsTailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer Insights
SynapseIndia
 
Vulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive OverviewVulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive Overview
Steven Carlson
 
kk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdfkk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdf
KIRAN KV
 
Opencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of MünsterOpencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of Münster
Matthias Neugebauer
 
The History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal EmbeddingsThe History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal Embeddings
Zilliz
 
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Zilliz
 
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
maigasapphire
 
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdfAcumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
BrainSell Technologies
 

Recently uploaded (20)

Zaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdfZaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdf
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
 
Uncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in LibrariesUncharted Together- Navigating AI's New Frontiers in Libraries
Uncharted Together- Navigating AI's New Frontiers in Libraries
 
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision MakingConnector Corner: Leveraging Snowflake Integration for Smarter Decision Making
Connector Corner: Leveraging Snowflake Integration for Smarter Decision Making
 
Step-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From ScratchStep-By-Step Process to Develop a Mobile App From Scratch
Step-By-Step Process to Develop a Mobile App From Scratch
 
How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...How UiPath Discovery Suite supports identification of Agentic Process Automat...
How UiPath Discovery Suite supports identification of Agentic Process Automat...
 
Gen AI: Privacy Risks of Large Language Models (LLMs)
Gen AI: Privacy Risks of Large Language Models (LLMs)Gen AI: Privacy Risks of Large Language Models (LLMs)
Gen AI: Privacy Risks of Large Language Models (LLMs)
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
 
Use Cases & Benefits of RPA in Manufacturing in 2024.pptx
Use Cases & Benefits of RPA in Manufacturing in 2024.pptxUse Cases & Benefits of RPA in Manufacturing in 2024.pptx
Use Cases & Benefits of RPA in Manufacturing in 2024.pptx
 
Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17
 
Feature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptxFeature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptx
 
Tailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer InsightsTailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer Insights
 
Vulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive OverviewVulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive Overview
 
kk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdfkk vathada _digital transformation frameworks_2024.pdf
kk vathada _digital transformation frameworks_2024.pdf
 
Opencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of MünsterOpencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of Münster
 
The History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal EmbeddingsThe History of Embeddings & Multimodal Embeddings
The History of Embeddings & Multimodal Embeddings
 
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...
 
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...
 
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdfAcumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
Acumatica vs. Sage Intacct vs. NetSuite _ NOW CFO.pdf
 

Internet de las Cosas: del Concepto a la Realidad

  • 1. 1 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
  • 2. 2 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
  • 3. 3 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
  • 4. 4 ¿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/
  • 5. 5 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
  • 6. 6 Visión de la Internet del Futuro
  • 7. 7 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
  • 8. 8 Arquitectura de la Internet del Futuro
  • 9. 9 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
  • 11. 11
  • 12. 12 Internet de las Cosas … conectando información, gente y cosas
  • 13. 13 Evolución hacia IoT • Desde la Web a la Web Social hacia IoT
  • 14. 14 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
  • 15. 15 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
  • 16. 16 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
  • 17. 17 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
  • 21. 21 IoT = Sensors + Connectivity + Processing for People
  • 22. 22 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
  • 23. 23 Internet de las Cosas: mucho más que cosas inteligentes
  • 24. 24 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
  • 25. 25 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
  • 26. 26 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.
  • 29. 29 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
  • 30. 30 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 $1 “
  • 31. 31 Tipos de Internet de las Cosas • Al menos dos sabores: – Consumer IoT (CIoT): orientada a consumidores – Industrial IoT (IIoT) • Industria 4.0
  • 32. 32 Consumer Internet of Things (CIoT) • The Consumer Internet of Things (CIoT) represents the class of consumer-oriented applications where: – Devices are consumer devices, such as smart appliances, e.g. refrigerator, washer, dryer, personal gadgets such as, fitness sensors, Google Glasses, etc. – Data volumes and rates are relatively low – Applications are not mission or safety critical, e.g., the failure of fitness gadget will make you, at worse, upset, but won’t cause any harm – CIoT applications tend to be “consumer-centric”
  • 34. 34 IoT impulse: Smart Cities, consumer objects, mobile sensing, smart metering
  • 35. 35 Personal data: SmartWatch & Health- promoting Data Devices
  • 36. 36 Quantified Self & Life Logging • Quantified self is self-knowledge through self-tracking with technology – Movement to incorporate technology into data acquisition on aspects of a person's daily life in terms of inputs (e.g. food consumed, quality of surrounding air), states (e.g. mood, arousal, blood oxygen levels), and performance (mental and physical) • Self-monitoring and self-sensing through wearable sensors (EEG, ECG, video, etc.) and wearable computing  lifelogging • Application areas: – Health and wellness improvement – Improve personal or professional productivity • Products and companies: – Apple Watch, Fitbit tracker, Jawbone UP, Pebble, Withings scale
  • 38. 38 Google Glass • Su misión es producir un ubiquitous computer de venta masiva – Lanzadas para los desarrolladores de Google I/O por 1500$ en el año 2013 • Muestra información disponible sin utilizar las manos, accede a Internet mediante órdenes de voz, de manera comparable a Google Now
  • 39. 39 • Google Home – Features • Amazon Echo – Alexa API Audible Computing • Apple AirPods – Comparison
  • 40. 40 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/
  • 41. 41 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”
  • 42. 42 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
  • 43. 43 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.
  • 45. 45 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".
  • 47. 47 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
  • 48. 48 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
  • 49. 49 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
  • 50. 50 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):
  • 54. 54 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
  • 55. 55 What do IoT apps do? (II)
  • 61. 61 De la Granja Digital a la Mesa
  • 63. 63 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)
  • 64. 64 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.
  • 65. 65 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
  • 66. 66 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
  • 67. 67 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:
  • 71. 71 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
  • 72. 72 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.
  • 73. 73 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)
  • 75. 75 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
  • 76. 76 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
  • 78. 78 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. 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
  • 80. 80 NFC in Use • Get info from posters, make payments, exchange connections
  • 81. 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)
  • 82. 82 • 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
  • 83. 83 iBeacon – a class of BLE devices that broadcast their identifier to nearby portable electronic devices (I)
  • 84. 84 iBeacon – a class of BLE devices that broadcast their identifier to nearby portable electronic devices (II)
  • 86. 86 Short range communication: NFC, QR and iBeacons
  • 87. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  • 98. 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,…
  • 100. 100 Ventajas y Retos de Cloud Computing
  • 102. 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. 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. 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. 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. 106 Fog Computing = IoT + Cloud Computing (II)
  • 107. 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. 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. 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. 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. 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. 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. 113 Linked Data Life Cycle • Linked Data must go through several stages (several iterations on Linkage) before are ready for exploitation:
  • 114. 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. 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. 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. 117 Data Understanding through Linked Statistics & Visualizations
  • 118. 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. 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. 120 Persuasive Interfaces to Promote Positive Behaviour Change GreenSoul, H2020 project 2016-2018, EE11
  • 121. 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. 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. 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. 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. 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. 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. 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. 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. 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. 130 Smart Diamond of Smart Cities
  • 132. 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. 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. 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. 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. 137 CrowdSensing • Individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest
  • 138. 138 Personal Data • Defined as "any information relating to an identified or identifiable natural person ("data subject")”
  • 139. 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. 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. 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. 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. 143 Analytics in the Smart City: Data- driven decision making
  • 144. 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. 145 From Open Data to Open Knowledge
  • 146. 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. 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. 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. 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) Source: Machina Research 2014
  • 150. 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. 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. 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. 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. 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.
  • 156. 156 Las 4 Vs de Big Data
  • 157. 157 IoT & Big Data • The more data that is created, the better understanding and wisdom people can obtain
  • 159. 159 Types of Analytics (II) • Predictive analysis enables you to move from sense and respond to predict and act
  • 162. 162 How does Big Data Analytics work? Source: Virtualisation and Validation of Smart City Data. Dr Sefki Kolozali. Dr Payam Barnaghi
  • 163. 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. 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. 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 Source: Machina Research 2014
  • 170. 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. 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. 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. 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. 174 Conclusión • Internet de las Cosas al Servicio de las Personas: – https://www.youtube.com/watch?v=Ge0q7jJuvbs
  • 175. 175 Conclusión • Internet de las Cosas al Servicio de las Personas: – https://www.youtube.com/watch?v=Ge0q7jJuvbs
  • 176. 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. 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. 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. 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. 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. 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