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

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
What is a Smart City?
• Technology?
• ICT?
• Data?
• Smarter Planning?
• Smarter Operation?
• Smarter Organization?
• Smarter People?

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
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
[http://trondheim2030.no/2015/11/09/ny-tredimensjonal-modell-av-trondheim-gjor-det-
enklere-a-planlegge-framtidas-by/]

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
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
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
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
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
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
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
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

15. 15 15

16. 16 16

17. 17
Building matrix

18. 18
Building trajectories

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
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
CTT – Carbon Track and Trace: IoT emission sensor network
https://www.ntnu.edu/ad/ctt

22. 22

23. 23
[Johan van der Zwart, Healthcare architecture research]

24. 24
[Johan van der Zwart, Healthcare architecture research]

25. 25
Labs/Workshops
• Smart City Infrastructure Lab
• Makerspace IDI
• IoT Rooftop Lab (NINOT)
• Woodworking workshop
• Metalworking workshop
• Living Labs

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
SCIL Makerspace

28. 28
Trondheim Bylab (City Lab)
[https://www.trondheim.kommune.no/content/1117755665/Trondheim-bylab]

29. 29

30. 30 [https://climathon.climate-kic.org/trondheim][https://climathon.climate-kic.org/trondheim]

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

33. 33
[https://www.sidewalklabs.com/]

34. 34 [http://lysgarden.no/]

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
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. 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
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

39. 39