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Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Participation

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Smarter Cities pillars: Internet of Things, Web of Data, Crowdsourcing
Interdependence analysis: Society ageing and Societal urbanisation
Enablement of Smarter Inclusive Cities

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Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Participation

  1. 1. 1 Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Participation UCLM, Ciudad Real, 4 de Noviembre de 2015, 11:45-12:30 Dr. Diego López-de-Ipiña González-de-Artaza dipina@deusto.es http://paginaspersonales.deusto.es/dipina http://www.morelab.deusto.es
  2. 2. 2 Agenda • Smarter Cities pillars: – Internet of Things – Web of Data – Crowdsourcing • Interdependence analysis: – Society ageing – Societal urbanisation • Enablement of Smarter Inclusive Cities
  3. 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. 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. 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. 6 IoT impulse: Smart Cities, consumer objects, mobile sensing, smart metering
  7. 7. 7 Personal data: SmartWatch & Health- promoting Data Devices
  8. 8. 8 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
  9. 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. 10 CrowdSensing • Individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest
  11. 11. 11 Personal Data • Defined as "any information relating to an identified or identifiable natural person ("data subject")”
  12. 12. 12 Social Open Innovation • Novel solution to a social problem that is more effective, efficient, sustainable, or just than current solutions. – New ideas (products, services and models) that simultaneously meet social needs and create new social relationships
  13. 13. 13 CAPS: Collective-awareness Platforms for Sustainability and Social Innovation • Aims at designing and piloting online platforms creating awareness of sustainability problems and offering collaborative solutions based on networks (of people, of ideas, of sensors), enabling new forms of social innovation. • Examples: – Open Democracy, Open Policy Making – Collaborative/Shared Economy – Collaborative making  co-creation
  14. 14. 14 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/
  15. 15. 15 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
  16. 16. 16 Linked Data Life Cycle • Linked Data must go through several stages (several iterations on Linkage) before are ready for exploitation:
  17. 17. 17 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
  18. 18. 18 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
  19. 19. 19 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
  20. 20. 20 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/
  21. 21. 21 Data Understanding through Linked Statistics & Visualizations
  22. 22. 22 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/
  23. 23. 23 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
  24. 24. 24 Persuasive Interfaces to Promote Positive Behaviour Change GreenSoul, H2020 project 2016-2018, EE11
  25. 25. 25 What is 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 – Opportunity to encounter insights in new and emerging data streams and contents and to answer previously considered beyond the scope questions • Enabled by Open Source frameworks such as Hadoop and Spark
  26. 26. 26 Features of Big Data • The structure (or lack thereof) and size of Big Data that makes it so unique • Represents both significant information and the way this information is analyzed – "Big Data" represents a noun – "the data" - and a verb – "combing the data to find value.“ • Interpretation of Big Data can bring about insights which might not be immediately visible or which would be impossible to find using traditional methods.
  27. 27. 27 Why Big Data? • We're generating more content than ever before, but in many cases it leads to more questions and fewer answers. – What is happening in the atmosphere? – Which candidate do voters prefer? – Which movies, books, and TV shows are going to satiate the public's appetite? – Which trends are coming down the road? • Technology can drive the business: – Finding "competitive advantages," getting "data on the board's agenda" and driving "innovative products and startups.“ • http://econsultancy.com/es/blog/63365-three-reasons-why-big-data-is- awesome
  28. 28. 28 From 3Vs to 4Vs of Big Data
  29. 29. 29 Big Data Storage Needs
  30. 30. 30 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
  31. 31. 31 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.]
  32. 32. 32 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 – Or go for big companies’ “smart city in a box” solutions
  33. 33. 33 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
  34. 34. 34 Smart Diamond of Smart Cities
  35. 35. 35 Smart City Applications • sadfafd
  36. 36. 36 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
  37. 37. 37 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]
  38. 38. 38 Silver Economy
  39. 39. 39 The need for Participative 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
  40. 40. 40 • 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
  41. 41. 41 • 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
  42. 42. 42 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
  43. 43. 43 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
  44. 44. 44 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
  45. 45. 45 Analytics in the Smart City: Data- driven decision making
  46. 46. 46 Priority Areas EIP on Smart Cities & Communities • dfad
  47. 47. 47 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
  48. 48. 48 From Open Data to Open Knowledge
  49. 49. 49 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 2013-2016, Zaragoza & Majadahonda involved http://iescities.eu
  50. 50. 50 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
  51. 51. 51 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.
  52. 52. 52 IES Cities Player
  53. 53. 53 Bristol’s Democratree App
  54. 54. 54 Zaragoza’s Your Opinion Matters
  55. 55. 55 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 project 2015-2017, Bilbao council involved http://welive.eu
  56. 56. 56 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
  57. 57. 57 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
  58. 58. 58 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.
  59. 59. 59 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 Vision/Architecture
  60. 60. 60 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, Madrid is involved
  61. 61. 61 SIMPATICO • Addresses the need to offer a more efficient and more effective experience to companies and citizens in their daily interaction with Public Administration (PA) – Providing a personalized delivery of e- services based on advanced cognitive system technologies and by promoting an active engagement of people for the continuous improvement of the interaction with these services. H2020 project 2016-2018, EURO6, Xunta Galicia is involved
  62. 62. 62 PA traditional e-services vs. SIMPATICO approach
  63. 63. 63 I have a dream … the citizen- empowered inclusive City • Smart Cities must ensure social equity, economic viability and environmental sustainability, enabled by: – IoT: Smart Objects, e.g. enabling technology for inclusive cities which allows to collect data, e.g. people transiting through a given area – Web of Data: Open Data from a given council should be linked to real- time data gathered by sensor data (physical) and prosumed data by users (virtual sensors)  BROAD DATA – Citizen participation: smartphones running Location-aware Open Data apps which recommend to surrounding citizens and visitors according to their profile and capabilities • User-conscious apps should adapt to the capabilities of different users, their devices and current context
  64. 64. 64 I have a dream … the citizen- empowered inclusive City
  65. 65. 65 Enabling Smarter Cities through Internet of Things, Web of Data & Citizen Participation UCLM, Ciudad Real, 4 de Noviembre de 2015, 11:45-12:30 Dr. Diego López-de-Ipiña González-de-Artaza dipina@deusto.es http://paginaspersonales.deusto.es/dipina http://www.morelab.deusto.es
  66. 66. 66 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
  67. 67. 67 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

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