Physical-Cyber-Social Data Analytics &

Smart City Applications

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Payam BarnaghiInstitute for Communication Systems (ICS)University of SurreyGuildford, United Kingdom

5th Annual International Cyber-Physical Cloud Computing Workshop

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

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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

Device/Data interoperability

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

W3C semantic sensor network ontology (SSNO)

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.

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Semantic annotation and vocabularies

P. Barnaghi, M. Presser, K. Moessner, "Publishing Linked Sensor Data", in Proc. of the 3rd Int. Workshop onSemantic Sensor Networks (SSN), ISWC2010, 2010.

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IoTLite Ontology

10http://iot.ee.surrey.ac.uk/fiware/ontologies/iot-lite

Search on the Internet/Web in the early days

1111

A discovery engine for the IoT

12A. 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:

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

Creating Patterns- Adaptive sensor SAX

14F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data”, IEEE Sensors Journal, 2013.

From SAX patterns to events/occurrences

15F. Ganz, P. Barnaghi, F. Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal, 2014.

From patterns/events to a situation ontology

16F. Ganz, P. Barnaghi, F. Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal, 2014.

Learning ontology from sensory data

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KAT- Knowledge Acquisition Toolkit

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

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.

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Real world data

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Analysing social streams

20With

City event extraction from social streams

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Tweets from a cityPOS

Tagging

Hybrid NER+ Event term extraction

GeohashingGeohashing

Temporal EstimationTemporal Estimation

Impact Assessment

Impact Assessment

Event Aggregatio

n

Event Aggregatio

nOSM

LocationsOSM

LocationsSCRIBE

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

CRF formalisation – for annotation

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A General CRF Model

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

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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

Automated creation of training data

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Evaluation over 500 randomly chosen tweets from around 8,000 annotated tweets

Extracted events and the ground truth

25Open source software: https://osf.io/b4q2t/

Social media analysis

26http://iot.ee.surrey.ac.uk/citypulse-social/

Not so good examples

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CityPulse demo

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CityPulse: live events from the city of Aarhus

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

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Deep IoT

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In summary

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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.

IoT discovery engines?

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“Working across different systems and various platforms is a key requirement. Internet search engines work very well with textual data, but IoT data comes in various forms and often as streams.”

This requires an ecosystem of IoT systems with several backend support components (e.g. pub/sub, storage, discovery, and access services).

IoT discovery engines?

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“ To make it more complex, IoT resources are often mobile and/or transient. Quality and trust (and obviously privacy) are among the other key challenges”.

This requires efficient distributed index and update mechanisms, quality-aware an resource-aware selection and ranking, and privacy control and preservation methods (and governance models) .

Accessing IoT data

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“ The internet/web norm (for now) is usually searching 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”.

This requires context-aware and opportunistic push mechanism, dynamic device/resource associations and (software-defined) data routing networks.

Web search is already adapting this model

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Image credits: the Economist

The future: borders will blend

37Source: IEEE Internet Computing, Special issue on Physical-Cyber-Social Computing

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 (Deep IoT).

− 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. 38

Smart city datasets

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http://iot.ee.surrey.ac.uk:8080

IET sector briefing report

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

Q&A

− Thank you.

− EU FP7 CityPulse Project:

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

@pbarnaghi

p.barnaghi@surrey.ac.uk