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Internet of Things and Data Analytics for Smart Cities and eHealth
1
Payam BarnaghiInstitute for Communication Systems (ICS)/5G Innovation Centre University of SurreyGuildford, United Kingdom
University of York, November 2016
“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
3IBM Mainframe 360, source Wikipedia
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 1.3GHzand has 512MB to 1GB of memoryCray-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, 2014image source: http://blog.opower.com/
Computing Power
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−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.
Smaller in size but larger in scale
6
The old Internet timeline
7Source: Internet Society
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
Early days of the web
9
Search on the Internet/Web in the early days
10
Source: Intel, 2012
Source: http://www.techspartan.co.uk
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
Sensor devices are becoming widely available
- Programmable devices- Off-the-shelf gadgets/tools
Internet of Things: The story so far
RFID based solutions
Wireless Sensor andActuator 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…
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
Speed of light?
17Image source: The Brain with David Eagleman, BBC
Device/Data interoperability
18The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
WoT/IoT
WSN
WSN
WSN
WSNWSN
Network-enabled Devices
Semantically annotate data
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GatewayCoAP
HTTP
CoAP
CoAP
HTTP
6LowPAN
Semantically annotate data
http://mynet1/snodeA23/readTemp?
WSNMQTT
MQTT
Gateway
Gateway
20
Some good existing models: W3C SSN Ontology
Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssnM. Compton, P. Barnaghi, L. Bermudez, et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
IoT-lite ontology
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Spatial Data on the Web WG
https://www.w3.org/2015/spatial/charter
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Hyper/CAT
24Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html
- Servers provide catalogues of resources toclients.
- A catalogue is an array of URIs.- Each resource in the catalogue is annotatedwith metadata (RDF-like triples).
FIWARE IoT Discovery Generic Enabler
25http://catalogue.fiware.org/enablers/iot-discovery/documentation
New Generation of Search Engines
26P. Barnaghi, A. Sheth, “On Searching the Internet of Things: Requirements and Challenges”, to appear, IEEE Intelligent Systems, 2016.
On Searching the Internet of Things
27P. Barnaghi, A. Sheth, “On Searching the Internet of Things: Requirements and Challenges”, to appear, IEEE Intelligent Systems, 2016.
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:
A GMM model for indexing
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Average Success ratesFirst attempt: 92.3% (min) At first DS: 92.5 % (min) At first DSL2 : 98.5 % (min)
Number of attempts
Perc
enta
ge o
f the
tota
l que
ries
0 10 20 30 40 50 6010-4
10-3
10-2
10-1
100
DSL2 capacity 1DSL2 capacity 2DSL2 capacity 3DSL2 capacity 4
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.
Indexing spatial data with multiple attributes
30Fathy 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.
Adaptive Clustering
31D. Puschmann, P. Barnaghi, R.Tafazolli, "Adaptive Clustering for Dynamic IoT Data Stream", IEEE Internet of Things Journal, 2016.
Adaptive clustering
32D. Puschmann, P. Barnaghi, R. Tafazolli, "Marginal Distribution Clustering of Multi-variate Streaming IoT Data", IEEE World Forum on IoT, Dec. 2016.
Dynamic clusters
33D. Puschmann, P. Barnaghi, R. Tafazolli, "Marginal Distribution Clustering of Multi-variate Streaming IoT Data", IEEE World Forum on IoT, Dec. 2016.
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.
Creating Patterns- Adaptive sensor SAX
35F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data”, IEEE Sensors Journal, 2013.
From SAX patterns to events/occurrences
36F. Ganz, P. Barnaghi, F. Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal, 2014.
Learning ontology from sensory data
37
Patterns and Segmentation of Time-series data
38A. Gonzalez-Vidal, P. Barnaghi, A. F. Skarmeta, BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation, Submitted to IEEE TKDE, 2016.
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
KAT V.2.0
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IoT data
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Analysing social streams
42Collaboration with Wright State University:
City event extraction from social streams
43
Tweets from a city POS Tagging
Hybrid NER+ Event term extraction
Geohashing
Temporal Estimation
Impact Assessment
Event Aggregatio
nOSM
LocationsSCRIBE
ontology
511.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.
CRF formalisation – for annotation
44
A General CRF Model
Extracted events and the ground truth
45Open source software: https://osf.io/b4q2t/
Extracting city events
46
City Infrastructure
Yes it is police @hasselager … there directing traffic
CRF-based NER
TaggingMulti-view Event
Extraction
Loc. Est. = “hasselager, aarhus”
Temp. Est. = “2015-2-19 21:07:17”
Level = 2
Event = Traffic
OSM Loc. CrimeTrans
p.
City Event Extraction
CNN POS+NE
R Event term
extraction
Cultural Social Enviro
. Sport Health
Data
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.
Extracting city events
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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.
Cities of the future
48http://www.globalnerdy.com/2007/08/28/home-electronics-of-the-future-as-predicted-28-years-ago/
49Source: BBC News
Source: The dailymail, http://helenography.net/, http://edwud.com/
What are smart cities?
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“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.”
What does makes smart cities “smart”?
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”?
How do cities get smarter?
How do cities get smarter?
55
Continuous (near-) real-time sensing/monitoringand data collection
Linked/integrated data and linked/integrated services
Real-time intelligence and actionable-informationfor different situations/services
Smart interaction and actuation
Creating awareness and effective participation
How can technology help to make cities smarter?
The role of data
57Source: 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
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“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.”?
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“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.
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
What type of problems we expect to solve using the IoT and data analytics solutions?
62Source LAT Times, http://documents.latimes.com/la-2013/
A smart City exampleFuture cities: A view from 1998
63Source:
http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia
Back to the Future: 2013
Common problems
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Guildford, Surrey
65
101 Smart City scenarios
66http://www.ict-citypulse.eu/scenarios/
Dr Mirko PresserAlexandra Institute
Denmark
Live data
67
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Event Visualisation
CityPulse demo
69
Users in control or losing control?
70
Image source: Julian Walker, Flicker
71
http://www.ict-citypulse.eu/
https://github.com/CityPulse
eHealth
72Ramesh Jain, Micro reports and Situation Recognition at social machines workshop, 2016.
73Ramesh Jain, Micro reports and Situation Recognition at social machines workshop, 2016.
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
Unstructured data!
Heterogeneity, multi-modality and volume are among the key issues.
Often natural language!
We need interoperable and machine-interpretable solutions…
75
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
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
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IBM Watson
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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.
Sensely
79Source: http://sense.ly/
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.
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
Innovation Partners Nine companies with 25+ devices and services, including monitors, sensors, apps, hubs, virtual assistants, location devices and wearables
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
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
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.
Infrastructure
Interoperability, integration
Security
Data governance
Scalability
Technical Challenge
Device/Data interoperability
87
FIHR4TIHM
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Health and Safety Monitoring and Alert
Privacy
Security
Tru
st
De
pe
nd
ab
ilit
y
Gateway
Gateway
Data Analytics Engine
IoT Test Bed Cloud
External NHS, GP IT systems
Possible links toOther Test Beds
HyperCat
Gateway
HyperCat
HyperCat
HyperCat
Data-driven and patient centered
Healthcare Applications
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 & FarnhamLiving Lab
Guildford & Waverley
Rest of Surrey
And beyond…
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
Accumulated and connected knowledge?
93Image courtesy: IEEE Spectrum
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