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Towards Smarter Inclusive Cities: Internet of Things, Web of Data & Citizen Participation as Enablers


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Smarter Cities = IoT+ Web of Data + User participation + Urban Analytics

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Towards Smarter Inclusive Cities: Internet of Things, Web of Data & Citizen Participation as Enablers

  1. 1. 1 Towards Smarter Inclusive Cities: Internet of Things, Web of Data & Citizen Participation as Enablers 10.30 - 11.30, 16 September 2015, Lounge of H21 (21st floor) University of Halmstad, Sweden Dr. Diego López-de-Ipiña González-de-Artaza
  2. 2. 2 Agenda • DeustoTech-INTERNET unit – MORElab research group – Research lines and active projects • Research areas – Topics tackled – Concept: Smarter Cities = IoT + Web of Data + User participation + Urban Analytics • Key European active projects • Discussion time
  3. 3. 3 DeustoTech – Deusto Institute of Technology • Associated to Faculty of Engineering, it belongs to Fundación Deusto • 150 people divided in 7 research units – We represent DeustoTech- INTERNET, a.k.a. MORElab – envisioning future internet research group •
  4. 4. 4 DeustoTech-INTERNET • Motto: “User-centred Intelligent Services for Anything, Anywhere at Anytime” • Areas of research: – Context-aware Mobile Computing for Enhanced User- Environment Interaction – Semantic Middleware for Embedded Wirelessly-connected Devices – Smart Environments of Augmented Internet-connected Objects – Ambient Assisted Living (AAL): adaptive accessible interfaces and social robotics. – Future Internet: Internet of Services, Internet/Web of Things and Web of Data
  5. 5. 5 DeustoTech-INTERNET Unit • Principal researcher: – Dr. Diego López-de-Ipiña, • It comprises ( unit/deustotech-internet/): – 5 lecturers (4 PhD holders) – 3 PostDoc – 3 Research Assistants – 2 Research Interns – 7 PhD grant holders • 20 people
  6. 6. 6 What do we actually do? • Remote Labs & Internet-connected Objects: – GO-LAB – federation of remote labs to enable cross-organisation remote experiments – WebLab-Deusto – open platform to ease the deployment of remote labs • Enabling Smart Assistive Environments: – SONOPA – activity-aware social networks to promote social interaction among elderlies – FRASEware – enhancing activity recognition by mixing knowledge- and data-driven approaches obtaining dynamic and personalized models – City4Age – city-wide support for elderly-friendly urban services promoting active and healthy ageing  from close/controlled (homes) to open environments (cities)
  7. 7. 7 What do we actually do? • Social Data Mining & CrowdSourcing: – MOVESMART– enhancing routing algorithms for Electric Vehicles taking into account user generated data • Linked Data Prosuming, Visualizations & Apps: – IES Cities – urban app ecosystems based on council and government open data where users prosume data – WeLive– enabling a holistic LinkedData-based Open Services platform to enable co-creation and open innovation of urban services for open government • Semantic Embedded Middleware: – Sustainable IoT – persuasive interfaces and cooperation among smart connected objects to foster sustainability
  8. 8. 8 Agenda • DeustoTech-INTERNET unit – MORElab research group – Research lines and active projects • Research areas – Topics tackled – Concept: Smarter Cities = IoT + Web of Data + User participation + Urban Analytics • Key European active projects • Discussion time
  9. 9. 9 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,  Democratization of Internet-connected Physical Objects
  10. 10. 10 IoT impulse: Smart Cities, consumer objects, mobile sensing, smart metering
  11. 11. 11 Personal data: SmartWatch & Health- promoting Data Devices
  12. 12. 12 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
  13. 13. 13 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:
  14. 14. 14 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
  15. 15. 15 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
  16. 16. 16 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
  17. 17. 17 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 … – • euro e-lecciones, social data mining in Twitter to visualize trends for the last European elections – • teseo, conversion and visualization of the distribution by genre and topics of PhD dissertations in Spain. These data was extracted from site – • intellidata, bank transaction analysis in different streets and neighborhoods in Madrid and Barcelona –
  18. 18. 18 Data Understanding through Linked Statistics & Visualizations
  19. 19. 19 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 •
  20. 20. 20 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
  21. 21. 21 Persuasive Interfaces to Promote Positive Behaviour Change
  22. 22. 22 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
  23. 23. 23 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.]
  24. 24. 24 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
  25. 25. 25 Why Smarter Inclusive Cities? • Not enough with the traditional resource efficiency approach of Smart City initiatives • “City appeal and dynamicity” 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
  26. 26. 26 • 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
  27. 27. 27 • 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
  28. 28. 28 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
  29. 29. 29 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 + Linked Data + citizen participation through Smartphones + Urban Analytics
  30. 30. 30 Agenda • DeustoTech-INTERNET unit – MORElab research group – Research lines and active projects • Research areas – Topics tackled – Concept: Smarter Cities = IoT + Web of Data + User participation + Urban Analytics • Key European active projects • Discussion time
  31. 31. 31 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 European CIP project with 2013-2016
  32. 32. 32 IES Cities Stakeholders • Citizens: – Users collaborate in the definition of the digital entity of the city. – Citizen produce and consumes contents (super-prosumer concept). • SMEs: – IES Cities will allow the creation of services benefiting the local businesses. • ICT-developing companies: – The platform will enable the chance to create new apps and services based on user needs, bringing new possibilities and added value. • Public administration: – The interaction with the users will enable them to improve and foster the use of their deployed sensors in urban areas and open databases
  33. 33. 33 IES Cities Objectives • To create a new open-platform adapting the technologies and over taking the knowledge from previous initiatives. • To validate and test a set of predefined urban apps across the cities. • To validate, analyse and retrieve technical feedback from the different pilots in order to detect and solve the major incidences of the technical solutions used in the cities. • To adequately achieve engagement of users in the pilots and measure their acceptability during the validations. • To maximize the impact of the project through adequate dissemination activities and publication of solutions upon a Dual-license model. 33
  34. 34. 34 IES Cities Player
  35. 35. 35 Bristol’s Democratree App
  36. 36. 36 Zaragoza’s Your Opinion Matters
  37. 37. 37 What´s WeLive (I) A novel We-Government ecosystem of tools (Live) that is easily deployable in different PA and which promotes co- innovation and co-creation of personalised public services through public-private partnerships and the empowerment of all stakeholders to actively take part in the value-chain of a municipality or a territory Open Data Open Services Open Innovation H2020 INSO1 project 2015- 2017, Bilbao council involved
  38. 38. 38 What´s WeLive (II) Stakeholder Collaboration + Public-private Partnership  IDEAS >> APPLICATIONS >> MARKETPLACE WeLive offers tools to transform the needs into ideas Tools to select the best Ideas and create the B. Blocks A way to compose the Building Blocks into mass market Applications which can be exploited through the marketplace
  39. 39. 39 WeLive proposes… Transform the current e-government approach into… WeLive Open and Collaborative Government Solution = We- government + t-government + I-government + m-government We- All stakeholders are treated as peers and prosumers t- Providing Technology tools to create public value l- To do more with less by involving other players and the PA as orchestrator m- Utilisation of mobile tech. for public services delivery
  40. 40. 40 Key Area WeLive Innovation and added value Open Data WeLive will provide an Open Data Toolset which will enable to handle the whole life cycle of what is starting to be termed as Broad Data, i.e. a combination of Open Data, Social Data, Big Data and private data. • Open Data Toolset will provide tools to capture, transform, adapt, link, store, publish and search for data which may be consumed by innovative public service apps. Open Services Open Services Framework centred on two key abstractions, namely building blocks and app templates. • Factorize the capabilities offered by a city or its stakeholders as a set of building blocks which can be easily combined with each other to give place to composite services. • Exemplary service templates composed of several building blocks so that stakeholders can personalize them and turn them into new public service app instances. Open Innovation Tackle the whole innovation process phases: a) conceptualization, b) voting and selection, c) funding, d) development and e) promotion and f) exploitation. • WeLive will focus on how to pass from innovation to adoption, by democratizing the creation process and fostering public-private partnership that will jointly exploit the outcomes of the innovation process. User- centric services Personalization of public service apps based on user profile and context. • A key element, named Citizen Data Vault, will represent a single sign-on point for a user • Decision Engine will enable stakeholders to retrieve statistics about the usage and app consumption and demand patterns of the different stakeholder groups. • Visual Composer, a tool to enable every stakeholder, even citizens, to visually compose their own services will be offered.
  41. 41. 41 WeLive Marketplace (Java EE) WeLive Player Citizen Data Vault (PubSubHubBub) Decision Engine (JBoss Drools 6) Open Innovation Area (Java EE) Propose Building blocks Get profile Update data Building blocks Data Mashup Publish new Building blocks Idea generation from citizen Get Public Service App Use existing Building Blocks Idea Generation Idea evaluation and selection Idea refinement Idea implementation NEED Develop building blocks/open service from scratch Visual composer (HTML5/CSS3) WeLive Framework
  42. 42. 42 City4Age: Elderly-friendly City services for active and healthy ageing • Aims to act as a bridge between the European Innovation Partnerships (EIP) on Smart Cities and Communities & Active and Healthy Ageing (EIP AHA) • Demonstrate that Cities play a pivotal role in the unobtrusive collection of “more data”and with “increased frequency” for comprehending individual behaviours and improving the early detection of risks H2020 project 2016- 2018, PHC 21
  43. 43. 43 I have a dream … the citizen- empowered inclusive City
  44. 44. 44 Agenda • DeustoTech-INTERNET unit – MORElab research group – Research lines and active projects • Research areas – Topics tackled – Concept: Smarter Cities = IoT + Web of Data + User participation + Urban Analytics • Key European active projects • Discussion time
  45. 45. 45 Towards Smarter Inclusive Cities: Internet of Things, Web of Data & Citizen Participation as Enablers 10.30 - 11.30, 16 September 2015, Lounge of H21 (21st floor) University of Halmstad, Sweden Dr. Diego López-de-Ipiña González-de-Artaza