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Data Technology and Smart Cities - Guest lecture Sustainable Facility Management

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Introduction Guest lecture in the Sustainable Facility Management about use cases and options of using Smart City Data Technology in facility management

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Data Technology and Smart Cities - Guest lecture Sustainable Facility Management

  1. 1. (Data) Technology and Smart Cities Guest lecture in AAR4821 – Sustainable Facilities Management Dirk Ahlers Department of Computer Science, NTNU https://www.ntnu.edu/employees/dirk.ahlers https://www.ntnu.edu/smartcities
  2. 2. 2 What is a Smart City? • Technology? • ICT? • Data? • Smarter Planning? • Smarter Operation? • Smarter Organization? • Smarter People?
  3. 3. 3 Goal and Overview • Sustainability • ‘Green’ buildings • Climate mitigation and adaptation • (Urban Planning) ICT & Technology Facility Management & Real Estate Development • ICT as an enabler • Strategy & Smartness & Data • Urban (big) data • Integration into urban networks • Data-driven society
  4. 4. 4 Scales of study Smart Home Smart Building Smart Campus Smart Neighbor- hood Smart City Smart Nation Building Systems Building Integration, Cohesion Infrastructure, Networks, Community City Systems, Distribution, Organization, Politics, Citizens Large-scale integration of everything, government, ??? Private systems
  5. 5. 5 [http://trondheim2030.no/2015/11/09/ny-tredimensjonal-modell-av-trondheim-gjor-det- enklere-a-planlegge-framtidas-by/]
  6. 6. 6 Building Lifecycle Planning & Design Construction Operation Teardown • Here: focus on planning, maintenance and operations • Use, operation, maintenance, repair, cleaning, logistics, modernisation, adaptation, transformation
  7. 7. 7 ICT and Data • Information Systems and Data Architecture – Measurement, analysis, monitoring, control, information – Integration/coordination a main challenge • Link between stakeholders, Smart City, municipal systems – Open and empowering systems, sensor- and data-driven [cf. previous talk, Judith Borsboom-van Beurden, Sustainable cities - Smart cities, TNO report] Security/Privacy Immense complexity hides in this layer alone!
  8. 8. 8 What we do • Smart City development and research projects • Interdisciplinary, spanning multiple departments and faculties • Labs, workshops, equipment, testbeds, networks, living labs • Support for projects, prototyping, students, theses, … • Work in the context of – Smart Cities – Urban Computing – Mobility Analysis – Smart Mobility – Visual Analytics – Campus Analytics
  9. 9. 9 Examples • Campus mobility • Outdoor/indoor air quality • Smart Hospitals (EBIM), logistics and optimization • Labs/workshops/makerspaces • Trondheim Kunnskapsaksen • ZEB • ZEN • Smart Grids, Smart Metering, Smart Charging, energy use • Internet of Things/Everything • Big Data and Machine Learning • Smart Parking, MaaS, smart transport planning, green transportation • Water, waste, utilities, building stock measurements
  10. 10. 10 Campus Guide: MazeMap • Common project between Trådløse Trondheim A/S, Information department NTNU, Studieavdelingen, NTNU IT, NTNU Videre and IME NTNU • Cisco WLAN-positioning on Campus and in the City [http://mazemap.com/]
  11. 11. 11 Wireless Trondheim / Mazemap Living Lab • Range of applications on WiFi network – WLAN indoor coverage on campus • ‘Campus Analytics’ – Mobility data with high spatial and temporal resolution – Passive location sensing – Device positions as proxy for people’s locations – Abstraction and processing layers • Data cleaning/preprocessing • Movement Extraction • Building-graph extraction • Visualization
  12. 12. 12 Campus as Living Lab • Applications – Awareness of building use – Learning and improving routes on campus – Service locations – Bottlenecks – Connection to larger mobility – Sustainable campus • Self-contained campus is functionally closed – But: System Boundaries • Scale up to smart city infrastructures • Member of ENoLL – European Network of Living Labs
  13. 13. 13 Data Set Campus Analytics • 1800 access points over 350000 sqm • Tracking based on probe requests • Data contains anonymized ID, timestamp, coordinates, accuracy, derived hierarchy – e.g. Gloshaugen > IT-Vest > 1. etasje, Gloshaugen > Sentralbygg II > 13. etasje • 43000 devices per day, 3.2 million positions • Lots of data cleaning necessary [Visualizing a City Within a City — Mapping Mobility Within a University Campus. Dirk Ahlers, Kristoffer Aulie, Jeppe Eriksen, and John Krogstie. Conference on Big Data and Analytics for Smart Cities. 2016.]
  14. 14. 14 14
  15. 15. 15 15
  16. 16. 16 16
  17. 17. 17 Building matrix
  18. 18. 18 Building trajectories
  19. 19. 19 Estimates are useful [A mobile service using anonymous location-based data: finding reading rooms. Shang Gao, John Krogstie, Trond Thingstad, Hoang Tran. International Journal of Information and Learning Technology, 32(1). 2015.]
  20. 20. 20 Applications • Awareness and planning support of building use • Real-time availability of rooms and facilities • Connection to larger mobility • Sustainable campus • Scaling out
  21. 21. 21 CTT – Carbon Track and Trace: IoT emission sensor network https://www.ntnu.edu/ad/ctt
  22. 22. 22
  23. 23. 23 [Johan van der Zwart, Healthcare architecture research]
  24. 24. 24 [Johan van der Zwart, Healthcare architecture research]
  25. 25. 25 Labs/Workshops • Smart City Infrastructure Lab • Makerspace IDI • IoT Rooftop Lab (NINOT) • Woodworking workshop • Metalworking workshop • Living Labs
  26. 26. 26 • LoRaWAN IoT network with gateway antennas and sensor devices in the city • IoT/Smart Home/Automation hardware • Drones, Arduinos, makerspace equipment • Measurement equipment • HCI support • Ongoing furnishing Equipment
  27. 27. 27 SCIL Makerspace
  28. 28. 28 Trondheim Bylab (City Lab) [https://www.trondheim.kommune.no/content/1117755665/Trondheim-bylab]
  29. 29. 29
  30. 30. 30 [https://climathon.climate-kic.org/trondheim][https://climathon.climate-kic.org/trondheim]
  31. 31. 31 Collected Inspiration and views to the future • Sidewalk Labs (Google/Alphabet) Toronto Neighborhood https://www.sidewalklabs.com/ • LinkNYC WiFi kiosks (SidewalkLabs/Intersection) https://www.intersection.com/ • Nest self-learning Thermostat and connected systems https://nest.com/ • Apple’s new campus ‘spaceship’ https://www.wired.com/2017/05/apple-park-new-silicon- valley-campus/ • Amazon Echo/Alexa assistant https://developer.amazon.com/alexa • Tesla cars and Powerwall battery https://www.tesla.com/powerwall • Mercedes F015 prototype ‘self-driving living room’ https://www.mercedes- benz.com/en/mercedes-benz/innovation/research-vehicle-f-015-luxury-in-motion/ • embr wave personal thermal device https://embrlabs.com/ • Please don’t do this ;-) https://sites.google.com/site/h2g2theguide/Index/g/704396 • IoT in buildings https://www.memoori.com/portfolio/internet-things-smart-commercial- buildings-2016-2021/ • Virtual Reality, 3D-Printing, e-bikes, sharing economy, … • Lots and lots more of useful, transformative, disruptive technology • Lysgården Trondheim http://lysgarden.no/ • Trondheim Technology https://teknologihovedstaden.no/ • Trondheim Bylab https://www.trondheim.kommune.no/content/1117755665/Trondheim- bylab
  32. 32. 32
  33. 33. 33 [https://www.sidewalklabs.com/]
  34. 34. 34 [http://lysgarden.no/]
  35. 35. Let’s talk! Contact search://Dirk Ahlers geo: 63°25‘10“N 10°24‘9“E @dirkahlers dirk.ahlers@ntnu.no https://www.ntnu.edu/smartcities
  36. 36. 36 I know, we can just wave a magic wand and make everything better. Except, of course, that making everything better by magic only makes things much, much worse. What we do, gentlemen, is dynamically refrain from using magic. -- Mustrum Ridcully, Unseen University Technological solutions do not operate in a vacuum. [Discworld The Ankh-Morpork Map for iPad: App Intro Video]
  37. 37. Physical Layer (Pilots) DataLayer Integration Layer Data, Analytics, Application-Driven Service and Data Interfaces Application Layer APIs Services (Dashboard, Apps, Website, Enterprise Integration) BER OSL TRD Companies, Developers CitizensCities UbiMobApplicationCore Other Stakeholders External Data External Data External Data External Data Component Overview External Data External Data External Data Internal Data APIs Semantic/LODlayer
  38. 38. 38 Interesting Data Issues • Heterogeneous data • Different spatial and temporal and conceptual granularity • Widely varying data quality • Applications not yet clear • Ownership/Privacy/Sharing/OpenData/lice nses • Integration very complex • Data and conceptual modeling • Not yet a standard for Smart City applications/data architecture/data lakes/… 38 Physical Layer (Pilots) BER OSL TRD External Data External Data External Data External Data External Data External Data External Data Internal Data
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