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 framework
• Demo
http://sensormeasurement.appspot.com 10
Reasoning engine Annotator
Query Engine & Services
Composer Annotator
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
13
Comparison of Use Cases
Conclusion & Future Work
14
• 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