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Internet of Things and Data Analytics for Smart Cities and eHealth

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Invited talk, University of York, November 2016

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Internet of Things and Data Analytics for Smart Cities and eHealth

  1. 1. Internet of Things and Data Analytics for Smart Cities and eHealth 1 Payam Barnaghi Institute for Communication Systems (ICS)/ 5G Innovation Centre University of Surrey Guildford, United Kingdom University of York, November 2016
  2. 2. “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
  3. 3. 3 IBM Mainframe 360, source Wikipedia
  4. 4. 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 image source: http://blog.opower.com/
  5. 5. Computing Power 5 −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.
  6. 6. Smaller in size but larger in scale 6
  7. 7. The old Internet timeline 7Source: Internet Society
  8. 8. Connectivity and information exchange was (and is) the main motivation behind the Internet; but Content and Services are now the key elements; and all started growing rapidly by the introduction of the World Wide Web (and linked information and search and discovery services). 8
  9. 9. Early days of the web 9
  10. 10. Search on the Internet/Web in the early days 10
  11. 11. Source: Intel, 2012
  12. 12. Source: http://www.techspartan.co.uk
  13. 13. 13P. 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.
  14. 14. 14 Sensor devices are becoming widely available - Programmable devices - Off-the-shelf gadgets/tools
  15. 15. 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, M2M, 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…
  16. 16. 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! 16
  17. 17. Speed of light? 17 Image source: The Brain with David Eagleman, BBC
  18. 18. Device/Data interoperability 18 The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
  19. 19. WoT/IoT WSN WSN WSN WSN WSN Network-enabled Devices Semantically annotate data 19 Gateway CoAP HTTP CoAP CoAP HTTP 6LowPAN Semantically annotate data http://mynet1/snodeA23/readTemp? WSN MQTT MQTT Gateway Gateway
  20. 20. 20 Some good existing models: W3C SSN Ontology Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn M. Compton, P. Barnaghi, L. Bermudez, et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
  21. 21. IoT-lite ontology 21
  22. 22. Spatial Data on the Web WG https://www.w3.org/2015/spatial/charter
  23. 23. 23
  24. 24. Hyper/CAT 24 Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html - Servers provide catalogues of resources to clients. - A catalogue is an array of URIs. - Each resource in the catalogue is annotated with metadata (RDF-like triples).
  25. 25. FIWARE IoT Discovery Generic Enabler 25http://catalogue.fiware.org/enablers/iot-discovery/documentation
  26. 26. New Generation of Search Engines 26 P. Barnaghi, A. Sheth, “On Searching the Internet of Things: Requirements and Challenges”, to appear, IEEE Intelligent Systems, 2016.
  27. 27. On Searching the Internet of Things 27 P. Barnaghi, A. Sheth, “On Searching the Internet of Things: Requirements and Challenges”, to appear, IEEE Intelligent Systems, 2016.
  28. 28. A discovery engine for the IoT 28A. HosseiniTabatabaie, P. Barnaghi, C. Wang, L. Dong, R. Tafazolli, "Method and Apparatus for Scalable Data Discovery in IoT Systems”, US Patents, CNV12174, May 2014. Let’s assume that attribute x has an alphabet Ax ={ax1,…,axs}. Query for a data item (q) that is described with attributes x, y and z, is then represented as q={x=axk & y=ayl & z=azm} The average ratio of matching processes that are required to resolve this query at n:
  29. 29. A GMM model for indexing 29 Average Success rates First attempt: 92.3% (min) At first DS: 92.5 % (min) At first DSL2 : 98.5 % (min) Number of attempts Percentageofthetotalqueries A. HosseiniTabatabaie, P. Barnaghi, C. Wang, L. Dong, R. Tafazolli, "Method and Apparatus for Scalable Data Discovery in IoT Systems”, US Patents, CNV12174, May 2014.
  30. 30. Indexing spatial data with multiple attributes 30 Fathy Y., Barnaghi P., Tafazolli R., “Distributed in-network indexing mechanism for the Internet of Things (IoT)”, submitted to IEEE ICC 2017. Fathy Y., Barnaghi P., Enshaeifar S., Tafazolli R., "A Distributed In-network Indexing Mechanism for the Internet of Things", IEEE World Forum on IoT, 2016.
  31. 31. Adaptive Clustering 31D. Puschmann, P. Barnaghi, R.Tafazolli, "Adaptive Clustering for Dynamic IoT Data Stream", IEEE Internet of Things Journal, 2016.
  32. 32. Adaptive clustering 32D. Puschmann, P. Barnaghi, R. Tafazolli, "Marginal Distribution Clustering of Multi-variate Streaming IoT Data", IEEE World Forum on IoT, Dec. 2016.
  33. 33. Dynamic clusters 33D. Puschmann, P. Barnaghi, R. Tafazolli, "Marginal Distribution Clustering of Multi-variate Streaming IoT Data", IEEE World Forum on IoT, Dec. 2016.
  34. 34. Dynamic clusters - multivariate data 34D. Puschmann, P. Barnaghi, R. Tafazolli, "Marginal Distribution Clustering of Multi-variate Streaming IoT Data", IEEE World Forum on IoT, Dec. 2016.
  35. 35. Creating Patterns- Adaptive sensor SAX 35 F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data”, IEEE Sensors Journal, 2013.
  36. 36. From SAX patterns to events/occurrences 36 F. Ganz, P. Barnaghi, F. Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal, 2014.
  37. 37. Learning ontology from sensory data 37
  38. 38. Patterns and Segmentation of Time-series data 38 A. Gonzalez-Vidal, P. Barnaghi, A. F. Skarmeta, BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation, Submitted to IEEE TKDE, 2016.
  39. 39. KAT- Knowledge Acquisition Toolkit F. Ganz, D. Puschmann, P. Barnaghi, F. Carrez, "A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things", IEEE Internet of Things Journal, 2015. 39 https://github.com/CityPulse/Knowledge-Acquisition-Toolkit-2.0 http://kat.ee.surrey.ac.uk
  40. 40. KAT V.2.0 40
  41. 41. IoT data 41
  42. 42. Analysing social streams 42Collaboration with Wright State University:
  43. 43. City event extraction from social streams 43 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.
  44. 44. CRF formalisation – for annotation 44 A General CRF Model
  45. 45. Extracted events and the ground truth 45Open source software: https://osf.io/b4q2t/
  46. 46. Extracting city events 46 City Infrastructure Yes it is police @hasselager … there directing traffic CRF- based NER Tagging Multi-view Event Extraction Loc. Est. = “hasselager, aarhus” Loc. Est. = “hasselager, aarhus” Temp. Est. = “2015-2-19 21:07:17” Temp. Est. = “2015-2-19 21:07:17” Level = 2Level = 2 Event = TrafficEvent = Traffic OSM Loc. OSM Loc. CrimeCrimeTransp.Transp. City Event Extraction CNN POS+NER Event term extraction CulturalCultural SocialSocial Enviro.Enviro. SportSport HealthHealth DataData Transp.Transp. Yes <O> it <O> is <O> police <B-CRIME> @hasselager <B-LOCATION>… <O> there <O> directing <O> traffic <B-TRAFFIC> Yes <S-NP/O> it <S-NP/O> is <S-VP/O> police <S-NP/O> @hasselager <S-LOC> ... <O/O> there <S-NP/O> directing <S-VP/O> traffic <S-NP/O> Nazli FarajiDavar, Payam Barnaghi, "A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams", submitted to ACM Transactions on Intelligent Systems and Technology (TIST), Nov. 2015.
  47. 47. Extracting city events 47 http://iot.ee.surrey.ac.uk/citypulse-social/ Nazli FarajiDavar, Payam Barnaghi, "A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams", submitted to ACM Transactions on Intelligent Systems and Technology (TIST), Nov. 2015.
  48. 48. Cities of the future 48 http://www.globalnerdy.com/2007/08/28/home-electronics-of-the-future-as-predicted-28-years-ago/
  49. 49. 49 Source: BBC News
  50. 50. Source: The dailymail, http://helenography.net/, http://edwud.com/
  51. 51. What are smart cities? 51 “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.”
  52. 52. What does makes smart cities “smart”?
  53. 53. 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”?
  54. 54. How do cities get smarter?
  55. 55. How do cities get smarter? 55 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
  56. 56. How can technology help to make cities smarter?
  57. 57. The role of data 57 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
  58. 58. 58 “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.” ?
  59. 59. 59 “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.
  60. 60. IoT environments are usually dynamic and (near-) real- time 60 Off-line Data analytics Data analytics in dynamic environments Image sources: ABC Australia and 2dolphins.com
  61. 61. What type of problems we expect to solve using the IoT and data analytics solutions?
  62. 62. 62Source LAT Times, http://documents.latimes.com/la-2013/ A smart City example Future cities: A view from 1998
  63. 63. 63 Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/ Source: wikipedia Back to the Future: 2013
  64. 64. Common problems 64 Guildford, Surrey
  65. 65. 65
  66. 66. 101 Smart City scenarios 66http://www.ict-citypulse.eu/scenarios/ Dr Mirko Presser Alexandra Institute Denmark
  67. 67. Live data 67
  68. 68. 68 Event Visualisation
  69. 69. CityPulse demo 69
  70. 70. Users in control or losing control? 70 Image source: Julian Walker, Flicker
  71. 71. 71 http://www.ict-citypulse.eu/ https://github.com/CityPulse
  72. 72. eHealth 72 Ramesh Jain, Micro reports and Situation Recognition at social machines workshop, 2016.
  73. 73. 73 Ramesh Jain, Micro reports and Situation Recognition at social machines workshop, 2016.
  74. 74. Medical/Health Data − The average person is likely to generate more than one million gigabytes of health-related data in their lifetime. This is equivalent to 300 million books. − Medical data is expected to double every 73 days by 2020. − 80% of health data is invisible to current systems because it’s unstructured. − Less than 50% of medical decisions meet evidence-based standards. (source: The rand corporation) 74Source: IBM Research
  75. 75. Unstructured data! Heterogeneity, multi-modality and volume are among the key issues. Often natural language! We need interoperable and machine-interpretable solutions… 75
  76. 76. Medical/Health decision making − One in five diagnoses are incorrect or incomplete and nearly 1.5 million medication errors are made in the US every year. − Medical journals publish new treatments and discoveries every day. − The amount of medical information available is doubling every five years and much of this data is unstructured - often in natural language. − 81 percent of physicians report that they spend five hours per month or less reading journals. 76Source: IBM Research
  77. 77. Medical/Health data in decision making − Patient histories can give clues. − Electronic medical record data provide lots of information. − Current observation and measurement data and fast analysis of the data can help (combined with other data/medical records). − This needs fast/accurate/secure data: − Collection/retrieval − Communication − Sharing/Integration − Processing/Analysis − Visualisation/presentation 77
  78. 78. IBM Watson 78 Watson can process the patient data to find relevant facts about family history, current medications and other existing conditions. It can combines this information with current findings from tests and instruments and then examines all available data sources to form hypotheses and test them. Watson can also incorporate treatment guidelines, electronic medical record data, doctor's and nurse's notes, research, clinical studies, journal articles, and patient information into the data available for analysis. Source: IBM Watson can read 40 million documents in 15 seconds.
  79. 79. Sensely 79 Source: http://sense.ly/
  80. 80. Healthcare data analytics- Symptom management 80N. Papachristou, C. Miaskowski, P. Barnaghi, R. Maguire, N. Farajidavar, B. Cooper and X. Hu, "Comparing Machine Learning Clustering with Latent Class Analysis on Cancer Symptoms’ Data", IEEE-NIH 2016, Nov. 2016.
  81. 81. Technology Integrated Health Management (TIHM) − An Internet of Things testbed to support dementia patients and their carers/doctors. − For patients with early to mild dementia − Remote and technology assisted care, monitoring and alert. 81
  82. 82. Innovation Partners Nine companies with 25+ devices and services, including monitors, sensors, apps, hubs, virtual assistants, location devices and wearables
  83. 83. The Health Challenge: Dementia  16,801 people with dementia in Surrey – set to rise to 19,000 by 2020 (estimated) - nationally 850,000 - estimated 1m by 2025 (Alzheimer’s Society)  Estimated to cost £26bn p/a in the UK (Alzheimer’s Society): health and social care (NHS and private) + unpaid care  Devices in the IoT will provide actionable data on agitation, mood, sleep, appetite, weight loss, anxiety and wandering – all have a big impact on quality of life and wellbeing
  84. 84. The Health Challenge: Falls  Surrey spends £10m a year on fracture care – with 95% of hip fractures caused by falls  People with dementia suffer significantly higher fall rates that cause injury – with falls the most common cause of injury- related deaths in the over-75s  Devices in the IoT will monitor location, activity and incident, supporting health/care staff and carers, enabling early intervention
  85. 85. The Health Challenge: Carers  5.4m carers supporting ill, older or disabled family members, friends and partners in England - expected to rise by 40% over the next 20 years.  Value of such informal care estimated at £120bn a year – but carer ‘burnout’ a key reason why loved ones require admission to a care/nursing home.  Devices in the IoT will support carers in their caring asks – and support their own health and wellbeing.
  86. 86.  Infrastructure  Interoperability, integration  Security  Data governance  Scalability Technical Challenge
  87. 87. Device/Data interoperability 87
  88. 88. FIHR4TIHM 88
  89. 89. Gateway Gatewa y Data Analytics Engine IoT Test Bed Cloud External NHS, GP IT systems Possible links to Other Test Beds HyperCat Gateway HyperCat HyperCat HyperCat Data-driven and patient centered Healthcare Applications
  90. 90.  Extend into homes – year 1 via two CCG areas, rolling out across four more CCGs in year 2  Reach 350 homes – with a control group of 350 – via dementia register  Focus on most effective product combinations – with potential for more via an open call Roll Out NE Hants & Farnham Living Lab Guildford & Waverley Rest of Surrey And beyond…
  91. 91. In Conclusion − Lots of opportunities and in various application domains; − 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; − Citizens in control; − Transparency and data management issues (privacy, security, trust, …); − Reliability and dependability of the systems. 92
  92. 92. Accumulated and connected knowledge? 93 Image courtesy: IEEE Spectrum
  93. 93. 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 94
  94. 94. Q&A − Thank you. http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/ @pbarnaghi p.barnaghi@surrey.ac.uk

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