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What makes smart cities “Smart”?

Invited talk, National University of Ireland in Galway

What makes smart cities “Smart”?

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What makes smart cities “Smart”?
1
Payam Barnaghi
Institute for Communication Systems (ICS)/
5G Innovation Centre
University of Surrey
Guildford, United Kingdom
November 5, Galway, Ireland
Desire for innovation
2
Driverless Car of the Future (1957)
Image: Courtesy of http://paleofuture.com
“A hundred years hence people will be so
avid of every moment of life, life will be so
full of busy delight, that time-saving
inventions will be at a huge premium…”
“…It is not because we shall be hurried in
nerve-shattering anxiety, but because we
shall value at its true worth the refining and
restful influence of leisure, that we shall be
impatient of the minor tasks of every day….”
The March 26, 1906, New Zealand Star :
Source: http://paleofuture.com
4P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology
(IET), I. Borthwick (editor), March 2015.
Apollo 11 Command Module (1965) had
64 kilobytes of memory
operated at 0.043MHz.
An iPhone 5s has a CPU running at speeds
of up to 1.3GHz
and has 512MB to 1GB of memory
Cray-1 (1975) produced 80 million Floating
point operations per second (FLOPS)
10 years later, Cray-2 produced 1.9G FLOPS
An iPhone 5s produces 76.8 GFLOPS – nearly
a thousand times more
Cray-2 used 200-kilowatt power
Source: Nick T., PhoneArena.com, 2014
Computing Power
6
−Smaller size
−More Powerful
−More memory and more storage
−"Moore's law" over the history of computing, the
number of transistors in a dense integrated circuit
has doubled approximately every two years.

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What makes smart cities “Smart”?

  • 1. What makes smart cities “Smart”? 1 Payam Barnaghi Institute for Communication Systems (ICS)/ 5G Innovation Centre University of Surrey Guildford, United Kingdom November 5, Galway, Ireland
  • 2. Desire for innovation 2 Driverless Car of the Future (1957) Image: Courtesy of http://paleofuture.com
  • 3. “A hundred years hence people will be so avid of every moment of life, life will be so full of busy delight, that time-saving inventions will be at a huge premium…” “…It is not because we shall be hurried in nerve-shattering anxiety, but because we shall value at its true worth the refining and restful influence of leisure, that we shall be impatient of the minor tasks of every day….” The March 26, 1906, New Zealand Star : Source: http://paleofuture.com
  • 4. 4P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology (IET), I. Borthwick (editor), March 2015.
  • 5. Apollo 11 Command Module (1965) had 64 kilobytes of memory operated at 0.043MHz. An iPhone 5s has a CPU running at speeds of up to 1.3GHz and has 512MB to 1GB of memory Cray-1 (1975) produced 80 million Floating point operations per second (FLOPS) 10 years later, Cray-2 produced 1.9G FLOPS An iPhone 5s produces 76.8 GFLOPS – nearly a thousand times more Cray-2 used 200-kilowatt power Source: Nick T., PhoneArena.com, 2014
  • 6. Computing Power 6 −Smaller size −More Powerful −More memory and more storage −"Moore's law" over the history of computing, the number of transistors in a dense integrated circuit has doubled approximately every two years.
  • 7. Internet of Things: The story so far RFID based solutions Wireless Sensor and Actuator networks , solutions for communication technologies, energy efficiency, routing, … Smart Devices/ Web-enabled Apps/Services, initial products, vertical applications, early concepts and demos, … Motion sensor Motion sensor ECG sensor Physical-Cyber-Social Systems, Linked-data, semantics, More products, more heterogeneity, solutions for control and monitoring, … Future: Cloud, Big (IoT) Data Analytics, Interoperability, Enhanced Cellular/Wireless Com. for IoT, Real-world operational use-cases and Industry and B2B services/applications, more Standards… P. Barnaghi, A. Sheth, "Internet of Things: the story so far", IEEE IoT Newsletter, September 2014. 7
  • 8. Smart City “A smart city uses digital technologies or information and communication technologies (ICT) to enhance quality and performance of urban services, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens.” [Wikipedia] 8 Is this a good definition?
  • 9. Cities of the future 9 http://www.globalnerdy.com/2007/08/28/home-electronics-of-the-future-as-predicted-28-years-ago/
  • 11. Source: The dailymail, http://helenography.net/, http://edwud.com/
  • 12. What are smart cities? 12 “An ecosystem of systems enabled by the Internet of Things and information communication technologies.” “People, resources, and information coming together, operating in an ad-hoc and/or coordinated way to improve city operations and everyday activities.”
  • 13. Source: Frost and Sullivan via http://raconteur.net/
  • 14. What does makes smart cities “smart”?
  • 15. Smart Citizens (more informed and more in control) Smart Governance (better services and informed decisions) Smart Environment Providing more equality and wider reach Context-aware and situation-aware services Cost efficacy and supporting innovation What does makes smart cities “smart”?
  • 16. How do cities get smarter?
  • 17. How do cities get smarter? 17 Continuous (near-) real-time sensing/monitoring and data collection Linked/integrated data and linked/integrated services Real-time intelligence and actionable-information for different situations/services Smart interaction and actuation Creating awareness and effective participation
  • 18. How can technology help to make cities smarter?
  • 19. The role of data 19 Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data driven systems for building, community and city-scale applications, http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
  • 20. 20 “Each single data item can be important.” “Relying merely on data from sources that are unevenly distributed, without considering background information or social context, can lead to imbalanced interpretations and decisions.” ?
  • 22. Data- Challenges − Multi-modal and heterogeneous − Noisy and incomplete − Time and location dependent − Dynamic and varies in quality − Crowed sourced data can be unreliable − Requires (near-) real-time analysis − Privacy and security are important issues − Data can be biased- we need to know our data! 22
  • 23. Smart data collection − Smart data collection − Intelligent data pProcessing (selective attention and information-extraction) − Region Beta Paradox 23 (image source: KRISTEN NICOLE, siliconangle.com)
  • 24. 24 “The ultimate goal is transforming the raw data to insights and actionable information and/or creating effective representation forms for machines and also human users, and providing automated services.” This usually requires data from multiple sources, (near-) real time analytics and visualisation and/or semantic representations.
  • 25. 25 “Data will come from various source and from different platforms and various systems.” This requires an ecosystem of IoT systems with several backend support components (e.g. pub/sub, storage, discovery, and access services). Semantic interoperability is also a key requirement.
  • 26. Device/Data interoperability 26 The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
  • 27. IoT environments are usually dynamic and (near-) real- time 27 Off-line Data analytics Data analytics in dynamic environments Image sources: ABC Australia and 2dolphins.com
  • 28. What type of problems we expect to solve using the IoT and data analytics solutions?
  • 29. 29Source LAT Times, http://documents.latimes.com/la-2013/ A smart City example Future cities: A view from 1998
  • 32. 32
  • 33. Applications and potentials − Analysis of thousands of traffic, pollution, weather, congestion, public transport, waste and event sensory data to provide better transport and city management. − Converting smart meter readings to information that can help prediction and balance of power consumption in a city. − Monitoring elderly homes, personal and public healthcare applications. − Event and incident analysis and prediction using (near) real- time data collected by citizen and device sensors. − Turning social media data (e.g.Tweets) related to city issues into event and sentiment analysis. − Any many more… 33
  • 34. EU FP7 CityPulse Project 34
  • 35. Big (IoT) Data Analytics . . . Real World Data Smart City Framework Smart City Scenarios
  • 36. Designing for real world problems
  • 37. 101 Smart City scenarios 37http://www.ict-citypulse.eu/scenarios/ Dr Mirko Presser Alexandra Institute Denmark
  • 41. Creating Patterns- Adaptive sensor SAX 41 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data”, IEEE Sensors Journal, 2013.
  • 42. Data abstraction 42 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
  • 43. Adaptable and dynamic learning methods http://kat.ee.surrey.ac.uk/
  • 47. City event extraction from social streams 47 Tweets from a city POS Tagging Hybrid NER+ Event term extraction GeohashingGeohashing Temporal Estimation Temporal Estimation Impact Assessment Impact Assessment Event Aggregation Event AggregationOSM LocationsOSM Locations SCRIBE ontologySCRIBE ontology 511.org hierarchy511.org hierarchy City Event ExtractionCity Event Annotation P. Anantharam, P. Barnaghi, K. Thirunarayan, A.P. Sheth, "Extracting City Traffic Events from Social Streams", ACM Trans. on Intelligent Systems and Technology, 2015. Collaboration with Kno.e.sis, Wright State University
  • 48. Geohashing 48 0.6 miles Max-lat Min-lat Min-long Max-long 0.38 miles 37.7545166015625, -122.40966796875 37.7490234375, -122.40966796875 37.7545166015625, -122.420654296875 37.7490234375, -122.420654296875 4 37.74933, -122.4106711 Hierarchical spatial structure of geohash for representing locations with variable precision. Here the location string is 5H34 0 1 2 3 4 5 6 7 8 9 B C D E F G H I J K L 0 1 7 2 3 4 5 6 8 9 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8
  • 49. Social media analysis 49 City Infrastructure Tweets from a city P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, ACM Transactions on TICS, 2014.
  • 50. Social media analysis (deep learning – under construction) 50 http://iot.ee.surrey.ac.uk/citypulse-social/
  • 51. Accumulated and connected knowledge? 51 Image courtesy: IEEE Spectrum
  • 54. Users in control or losing control? 54 Image source: Julian Walker, Flicker
  • 55. Avoiding failures 55 Source: IEEE Spectrum, Lessons From a Decade of IT Failures
  • 56. Things to avoid: Over-complexifying, Under-delivering 56 Source: IEEE Spectrum, Lessons From a Decade of IT Failures
  • 57. Data Analytics solutions for smart cities − Great opportunities and many applications; − Enhanced and (near-) real-time insights; − Supporting more automated decision making and in-depth analysis of events and occurrences by combining various sources of data; − Providing more and better information to citizens; − … 57
  • 58. However… − We need to know our data and its context (density, quality, reliability, …) − Open Data (there needs to be more real-time data) − Complementary data − Citizens in control − Transparency and data management issues (privacy, security, trust, …) − Reliability and dependability of the systems 58
  • 59. In conclusion −Smart cities are made of informed citizens, smart environments and informed and intelligent decision making and governance. −Smart cities should promote innovation, equality and wider reach of services to all citizens. −IoT plays a key role in making cities smarter; openness of data and interconnection and interoperability between different data sources and services is a key requirement. −Technology alone won’t make cities smart. 59
  • 60. IET sector briefing report 60 Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
  • 62. Other challenges and topics that I didn't talk about Security Privacy Trust, resilience and reliability Noise and incomplete data Cloud and distributed computing Networks, test-beds and mobility Mobile computing Applications and use-case scenarios 62