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Smart Cities and Data Analytics: Challenges and Opportunities

Workshop on Smart City: Applications and Services, Budva, Montenegro, October 2015.

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Smart Cities and Data Analytics: Challenges and Opportunities

  1. 1. Smart Cities and Data Analytics: Challenges and Opportunities 1 Payam Barnaghi Institute for Communication Systems (ICS)/ 5G Innovation Centre University of Surrey Guildford, United Kingdom Workshop on Smart City: Applications and Services Budva, Montenegro October 2015
  2. 2. 2 IBM Mainframe 360, source Wikipedia
  3. 3. 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.,, 2014
  4. 4. Computing Power 4 −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.
  5. 5. Cyber-Physical-Social Data 5P. 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.
  6. 6. 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. 6
  7. 7. 7 “Each single data item is 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.” ?
  8. 8. 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! 8
  9. 9. Data Lifecycle 9 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,
  10. 10. 10 “The ultimate goal is transforming the raw data to insights and actionable knowledge and/or creating effective representation forms for machines and also human users and creating automation.” This usually requires data from multiple sources, (near-) real time analytics and visualisation and/or semantic representations.
  11. 11. 11 “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.
  12. 12. Device/Data interoperability 12 The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
  13. 13. Search on the Internet/Web in the early days 1313
  14. 14. Accessing IoT data 14 “ The internet/web norm (for now) is often to use an interface to search for the data; the search engines are usually information locators – return the link to the information; IoT data access is more opportunistic and context aware”. The IoT requires context-aware and opportunistic push mechanism, dynamic device/resource associations and (software-defined) data routing networks.
  15. 15. IoT environments are usually dynamic and (near-) real- time 15 Off-line Data analytics Data analytics in dynamic environments Image sources: ABC Australia and
  16. 16. What type of problems we expect to solve using the IoT and data analytics solutions?
  17. 17. 17Source LAT Times, A smart City example Future cities: A view from 1998
  18. 18. 18 Source:[default]/0/ Source: wikipedia Back to the Future: 2013
  19. 19. Common problems 19 Source: & Guildford, Surrey
  20. 20. 20
  21. 21. 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… 21
  22. 22. EU FP7 CityPulse Project 22
  23. 23. 23 CityPulse Consortium Industrial SIE (Austria, Romania), ERIC SME AI, Higher Education UNIS, NUIG, UASO, WSU City BR, AA Partners: Duration: 36 months (2014-2017)
  24. 24. 24
  25. 25. Designing for real world problems
  26. 26. 101 Smart City scenarios 26 Dr Mirko Presser Alexandra Institute Denmark
  27. 27. 27 Data Visualisation
  28. 28. 28 Event Visualisation
  29. 29. CityPulse demo 29
  30. 30. Data abstraction 30 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.
  31. 31. Adaptable and dynamic learning methods
  32. 32. Correlation analysis 32
  33. 33. Analysing social streams 33 With
  34. 34. City event extraction from social streams 34 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 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
  35. 35. Geohashing 35 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
  36. 36. Social media analysis 36 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.
  37. 37. Social media analysis (deep learning – under construction) 37
  38. 38. Accumulated and connected knowledge? 38 Image courtesy: IEEE Spectrum
  39. 39. Reference Datasets 39
  40. 40. Importance of Complementary Data 40
  41. 41. Users in control or losing control? 41 Image source: Julian Walker, Flicker
  42. 42. Data Analytics solutions for IoT data − 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; − … 42
  43. 43. 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 43
  44. 44. In conclusion − IoT data analytics is different from common big data analytics. − Data collection in the IoT comes at the cost of bandwidth, network, energy and other resources. − Data collection, delivery and processing is also depended on multiple layers of the network. − We need more resource-aware data analytics methods and cross-layer optimisations. − The solutions should work across different systems and multiple platforms (Ecosystem of systems). − Data sources are more than physical (sensory) observation. − The IoT requires integration and processing of physical-cyber-social data. − The extracted insights and information should be converted to a feedback and/or actionable information. 44
  45. 45. IET sector briefing report 45 Available at:
  46. 46. CityPulse stakeholder report 46
  47. 47. 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 47
  48. 48. Q&A − Thank you. @pbarnaghi