Internet of Things, Web of Data & Citizen Participation as Enablers of Smart Cities Internet of Things Broad Data: Big Data User-generated Data Linked Data Urban analytics Smart Cities Open Government

1. 1
Internet of Things, Web of Data & Citizen
Participation as Enablers of Smart Cities
4 February 2015, 9:00-12:30,
Universidad de Jaén - Edificio Central
Mesa Redonda: Infraestructuras de Datos Espaciales, Retos y Tendencias (04/02/2015)
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es

2. 2
Agenda
• Internet of Things
• Broad Data:
– Big Data
– User-generated Data
– Linked Data
• Urban analytics
• Smart Cities & Open Government

3. 3
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

4. 4
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

5. 5
IoT Enabling Technologies
• Low-cost embedded computing and communication
platforms, e.g. Arduino or Rapsberry PI
• Wide availability of low-cost sensors and sensor networks
• Cloud-based Sensor Data Management Frameworks:
Xively, Sense.se
 Democratization of Internet-connected Physical Objects

6. 6
IoT impulse: Smart Cities, consumer
objects, mobile sensing, smart metering

7. 7
Personal data: SmartWatch & Health-
promoting Data Devices

8. 8
Human-mediated Mobile Sensing
• The combination of varied data sources such as Humans,
SmartPhones and sensors gives place to Mobile
Sensing/Participatory Sensing & CrowdSensing  Broad Data

9. 9
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!

10. 10
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

11. 11
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/

12. 12
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

13. 13
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

14. 14
Actionable Knowledge from
Sensorial Data
• Don’t care about the 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

15. 15
Towards Actionable Knowledge:
Converting to and Visualizing Open Data
• labman: data management system for research organizations which
enables to correlate researchers, publications, projects, funding, news …
– http://www.morelab.deusto.es
• euro e-lecciones, social data mining in Twitter to visualize trends for the
last European elections
– http://apps.morelab.deusto.es/eu_elections
• teseo, conversion and visualization of the distribution by genre and topics
of PhD dissertations in Spain. These data was extracted from site
https://www.educacion.gob.es/teseo/irGestionarConsulta.do
– http://apps.morelab.deusto.es/teseo
• intellidata, bank transaction analysis in different streets and
neighborhoods in Madrid and Barcelona
– http://apps.morelab.deusto.es/intellidata/

16. 16
Data Understanding through Linked
Statistics & Visualizations

17. 17
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/

18. 18
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

19. 19
Persuasive Interfaces to Promote Positive
Behaviour Change

20. 20
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 in cities
• Citizens’ participation through apps in their
smartphones

21. 21
Society Urbanisation
• 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]

22. 22
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

23. 23
The need for Participative Cities
• Not enough with the traditional resource efficiency
approach of Smart City initiatives
• “City appeal” will be key to attract and retain citizens, companies and
tourists
• Only possible by user-driven and centric innovation:
– The citizen should be heard, EMPOWERED!
» Urban apps to enhance the experience and interactions of the citizen, by
taking advantage of the city infrastructure
– The information generated by cities and citizens must be linked and
processed
» How do we correlate, link and exploit such humongous data for all
stakeholders’ benefit?
• We should start talking about Big (Linked) Data

24. 24
• 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 high quality
and trustable data
• BUT, how can we know if a given user and,
consequently, the data generated by him/her can
be trusted?
– W3C has created the PROV Data Model, for provenance
interchange
User-provided Data

25. 25
• 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

26. 26
IES Cities Project
• The IES Cities project promotes user-centric
mobile micro-services that exploit open data
and generate user-supplied data
– Hypothesis: Users may help on improving, extending
and enriching the open data in which micro-services
are based
• Its platform aims to:
– Enable user supplied data to complement, enrich and
enhance existing datasets about a city
– Facilitate the generation of citizen-centric apps that
exploit urban data in different domains

27. 27
IES Cities Player

28. 28
Bristol’s Democratree App

29. 29
Urban Intelligence / Analytics
• Broad Data aggregates data from heterogeneous sources:
– Open Government Data repositories
– 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

30. 30
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 data captured on these smart cities deployment
Smarter Cities= Internet of Things + Linked Data + citizen
participation through Smartphones + Urban Analytics

31. 31
Internet of Things, Web of Data & Citizen
Participation as Enablers of Smart Cities
4 February 2015, 9:00-12:30,
Universidad de Jaén - Edificio Central
Mesa Redonda: Infraestructuras de Datos Espaciales, Retos y Tendencias (04/02/2015)
Dr. Diego López-de-Ipiña González-de-Artaza
dipina@deusto.es
http://paginaspersonales.deusto.es/dipina
http://www.morelab.deusto.es