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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

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Agenda

• Internet of Things

• Broad Data:

– Big Data

– User-generated Data

– Linked Data

• Urban analytics

• Smart Cities & Open Government

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Internet of Things (IoT) Promise

• There will be around 25 billion devices connected to the Internet by 2015, 50 billion by 2020

– A dynamic and universal network where billions of identifiable “things” (e.g. devices, people, applications, etc.) communicate with one another anytime anywhere; things become context-aware, are able to configure themselves and exchange information, and show “intelligence/cognitive” behaviour

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Internet of Things: Challenges

1. To process huge amounts of data supplied by “connected things” and to offer services as response

2. To research in new methods and mechanisms to find, retrieve, and transmit data dynamically – Discovery of sensor data — both in time and space

– Communication of sensor data: complex queries (synchronous), publish/subscribe (asynchronous)

– Processing of great variety of sensor data streams: correlation, aggregation and filtering

3. Ethical and social dimension: to keep the balance between personalization, privacy and security

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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

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

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Personal data: SmartWatch & Health-promoting Data Devices

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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

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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!

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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

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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/

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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

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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

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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

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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/

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Data Understanding through Linked Statistics & Visualizations

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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/

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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

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Persuasive Interfaces to Promote Positive Behaviour Change

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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

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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]

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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

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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

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• 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

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• 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

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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

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IES Cities Player

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Bristol’s Democratree App

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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

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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

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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