A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor

Data to End-Users Applications

8th IEEE International Conference on Internet of Things (iThings 2015) 11-13 December 2015, Sydney, Australia

Amelie Gyrard, Insight, Ireland Martin Serrano, Insight, Ireland

Agenda • Introduction & Motivation Why should we integrate semantic web technologies

with IoT and smart cities?

• State of The Art & Main challenges Semantic-based projects for smart cities

• Contributions: Our vision Semantic Engine for IoT and Smart Cities Use Cases: M3, FIESTA-IoT and VITAL

• Conclusion & Future work

2

Why should we integrate semantic web technologies with IoT and smart cities?

Thermometer

Sensor data

Applications to visualize data

Interpretation by humans

How machines can interpret data?

3

Reusing domain knowledge already available on the web?

State of the Art: Semantic-based Projects for Smart Cities

4

http://sensormeasurement.appspot.com/?p=stateOfTheArt

=> How to design a generic approach to unify semantic-based IoT data and applications?

Features/Projects

STAR-CITY

Real-time No Yes Yes Yes Scalability Yes Yes Yes Yes Applicative domains All Transport All All

Horizontal objective No No No No

Semantic web technologies used

Yes Yes Yes Ongoing

Technologies required to build Smart Cities (1)

Machine- to-Machine (M2M)

Web of Things (WoT)

Wireless

technologies Internet

Internet of Things (IoT)

Hardware

1) Generate data

2) Transfer data

3) Connect devices to Internet

4) Send data to the Web

Technologies required to build Smart Cities (2)

Machine- to-Machine (M2M)

Semantic Web of Things (SWoT)

Web of Things (WoT)

Wireless technologies

Internet

Internet of T Things (IoT)

Artificial Intelligence (AI)

Semantic Sensor Networks (SSN)

Hardware

Services & applications

5) Unify virtual sensor networks

6) Unify data 7) Interpret data

8) Consume data

1. Unifying data Sharing and reusing data: Linked Open Data

2. Unifying models/vocabularies/ontologies Structure data: Linked Open Vocabularies

3. Unifying reasoning to interpret data Interpret data: Linked Open Rules

4. Unifying services Composition of services: Linked Open Services 7

Challenges and Research Directions

Composer (Unifying data)

Annotator (Unifying model)

Unified language to describe IoT data

(semantic annotation) Semantic reasoning engine (Unifying reasoning)

Semantic query Engine (Unifying querying)

Services (Unifying APIs, RESTful web services)

Deduce new knowledge

Semantic IoT data + related

ontologies

Users

Raw IoT data

Semantic IoT data

Contribution: The Semantic Engine for IoT and Smart Cities Rule-based

reasoning

=> Interoperability of IoT data

Use Case 1: The M3 Semantic Engine

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Composer Reasoning

engine

Annotator Query Engine

& Services

Use Case 1: The M3 framework

• Demo

http://sensormeasurement.appspot.com 10

Reasoning engine Annotator

Query Engine & Services

Composer Annotator

Use Case 2: The EU H2020 FIESTA-IoT Project

11 => Reusing the expertise to build a methodology?

Use Case 3: The EU FP7 VITAL Project

12 http://vital-iot.eu/ http://ercim-news.ercim.eu/en98/special/moving-towards-interoperable-internet-of-things-deployments-in-smart-cities

Services

Annotator

Composer

Reasoning engine

Unification feature/Projects

IoT data Yes Yes Yes (Ongoing)

Ontology Yes No Yes (Ongoing)

Reasoning Yes Yes Yes (Ongoing)

Architecture No No Yes

Services Yes

Yes

Yes

Real-time & scalability

No Yes Yes

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Comparison of Use Cases

Conclusion & Future Work

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• Contributions: Shared our Vision for Semantic-based IoT projects Semantic Engine for IoT and Smart Cities 3 use-cases: M3 project, EU H2020 FIESTA-IoT, FP7 EU

Project

• Future work: Composition of unified services A generic methodology from Linked Open Data to Linked

Open Services

Thank you!

• amelie.gyrard@insight-centre.org • http://sensormeasurement.appspot.com/ • Slideshare • Twitter

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