Opportunities and Challenges of Large-scale IoT Data Analytics

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Payam BarnaghiInstitute for Communication Systems (ICS)/5G Innovation Centre University of SurreyGuildford, United KingdomASEAN IoT Innovation Forum, Kuala Lumpur, Malaysia, August 2015

Cyber-Physical-Social Data

2P. 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.

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.

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“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.”?

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!

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Data Lifecycle

6Source: 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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“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.

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“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.

Device/Data interoperability

9The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.

Search on the Internet/Web in the early days

1010

Accessing IoT data

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“ 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.

IoT environments are usually dynamic and (near-) real-time

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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?

14Source LAT Times, http://documents.latimes.com/la-2013/

A smart City exampleFuture cities: A view from 1998

15Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/

Source: wikipedia

Back to the Future: 2013

Common problems

16Source: thestar.com.my & skyscrappercity.com

Guildford, Surrey

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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…

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EU FP7 CityPulse Project

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CityPulse Consortium

Industrial SIE (Austria,

Romania),ERIC

SME AI,

HigherEducation

UNIS, NUIG,UASO, WSU

City BR, AA

Partners:

Duration: 36 months (2014-2017)

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AnalyticsToolbox

Context-awareDecision Support,

Visualisation

Knowledge-based

Stream Processing

Real-TimeMonitoring &

Testing

Accuracy & Trust

Modelling

SemanticIntegration

On Demand Data

Federation

OpenReferenceData Sets

Real-TimeIoT InformationExtraction

IoT StreamProcessing

Federation ofHeterogenousData Streams

Design-Time Run-Time Testing

Exposure APIs

Designing for real world problems

101 Smart City scenarios

23http://www.ict-citypulse.eu/scenarios/

Dr Mirko PresserAlexandra Institute Denmark

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Data Visualisation

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Event Visualisation

CityPulse demo

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Data abstraction

27F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.

Adaptable and dynamic learning methods

http://kat.ee.surrey.ac.uk/

Correlation analysis

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Analysing social streams

30With

City event extraction from social streams

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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.

Collaboration with Kno.e.sis, Wright State University

Geohashing

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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

437.74933, -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 67 8 9 B C D EF G H I J K L

0 172 3 4

5 6 8 9

0 1 2 3 4

5 6 7

0 1 23 4 5

6 7 8

Social media analysis

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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.

Social media analysis (deep learning – under construction)

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http://iot.ee.surrey.ac.uk/citypulse-social/

Accumulated and connected knowledge?

35Image courtesy: IEEE Spectrum

Reference Datasets

36http://iot.ee.surrey.ac.uk:8080/datasets.html

Importance of Complementary Data

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Users in control or losing control?

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Image source: Julian Walker, Flicker

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;− …

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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

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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. 41

IET sector briefing report

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Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm

CityPulse stakeholder report

43http://www.ict-citypulse.eu/page/sites/default/files/citypulse_annual_report.pdf

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

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Q&A

− Thank you.

http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/

@pbarnaghi

p.barnaghi@surrey.ac.uk