The document discusses the M3 framework for developing cross-domain Internet of Things (IoT) applications, emphasizing the evaluation of its components and capabilities. Key challenges addressed include interpreting IoT data, reusing domain knowledge, and achieving interoperability through common data descriptions. It also covers specific use cases, such as integrating M3 into smart fridge applications, and presents evaluation results demonstrating the framework's efficiency.

1. Cross-Domain Internet of Things
Application Development:
M3 Framework and Evaluation
FiCloud 24-26 August 2015, Rome, Italy
Amelie Gyrard, Insight, Ireland
Soumya Kanti Datta, Eurecom, France
Christian Bonnet, Eurecom, France
Karima Boudaoud, University of Nice Sophia Antipolis France

2. Agenda
• Introduction & Motivation
• State of The Art & Main challenges
• Contributions: M3 framework
– Components
– Use case
– Evaluation
– Demonstration
• Conclusion & Future work
2

3. How to interpret Internet of Things (IoT) data?
Thermometer
Sensor data
Applications to visualize data
Interpretation
by humans
How machines can
interpret data?
3
Machine learning?
Reusing domain knowledge?

4. 4
How to combine and reuse IoT data from different
domains?
How to build
innovative
applications?
How to describe data?
How to
combine data
from different
domains?
How to get additional
information?
Milk contains lactose
Oven, body, external
temperature?
1liter: milk

5. How to describe data and get additional
information?
=> Taking inspiration from the Web
Automatically built
by machines

6. “Semantic Web of Things: an analysis of the application semantics for the IoT moving towards the IoT
convergence” [Jara et al. 2014]
How to apply semantic web technologies to Internet of
Things?
Global
interoperability
⇒ How to provide a common
description of sensor data
to later reason on it?
Common description
Common App. Protocol
Device Abstraction
Common Nwk. Protocol
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• Machine-understandable data
• Describe data with common
vocabularies
• Reuse domain knowledge
• Link to other data
• Ease the reasoning

7. Our contribution: Machine-to-Machine
Measurement Framework (M3)
Challenge A: Design
semantic based IoT
applications
Challenge B.1 &
B.2: Combine
data and domains
Challenge B:
Interpret IoT
data
Challenge B.2:
Reuse domain
knowledge 7

8. SWoT generator
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*
Challenge A: Design
semantic based IoT
applications
* Domain where is deployed the sensor, not the applicative domain
=> Benefits: No need to learn semantic web technologies

9. SWoT template: interoperable domain knowledge
• Need to have the set of files generated in the template
compatible with sensor data
– Ontologies + datasets + rules + sensor data
– Domain knowledge structured in the same way
Domain
ontologies
Domain
datasets
Rules
Interoperable
IoT
Application
Provide
sensor data
SWoT templateUnified
IoT data
Produce
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10. M3 language & ontology
Challenge B.1 &
B.2: Combine
data and domains
• Describing data in an unified way
• Extension of the W3C Semantic Sensor Networks (SSN)
ontology (Observation Value concept)
• Provide a basis for reasoning and cross-domain
interlinking 10

11. Sensor-based Linked Open Rules (S-LOR)
11
Challenge B:
Interpret IoT
data

12. Demo paper: Helping IoT application developers with Sensor-based Linked Open
Rules [Gyrard et al., ISWC 2014, SSN workshop]
S-LOR: Deducing new knowledge
• How to deduce new knowledge?
– S-LOR: a dataset of interoperable SWRL rules
• Rules example:
– If Domain == Health && MeasurementType == Temperature
then NewType = BodyTemperature
– If BodyTemperature > 38,7°C then “Fever”
• BodyTemperature and Fever are already described in
domain ontologies or datasets!
12

13. Linked Open Vocabularies for Internet of Things (LOV4IoT)
Challenge B.2:
Reuse domain
knowledge 13

14. LOV4IoT
http://www.sensormeasurement.appspot.com/?p=ontologies
http://lov.okfn.org/dataset/lov/
Collect
Classify Interoperability
SWoT
template
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A second life for ontologies!
• A dataset of 278 ontology-based projects relevant for IoT
– Ontologies, Datasets, Rules, Technologies, Sensors and
Domains
– Used to build the SWoT template
– Extension of Linked Open Vocabularies (LOV)
Collect

15. Paper: Enrich Machine-to-Machine Data with Semantic Web Technologies for Cross-Domain
Applications [Gyrard et al., WF-IoT 2014]
M3 semantic engine : An entire chain to interpret IoT
and build cross-domain applications
15

16. Use Case: Embedding M3 in smart fridges
M3 suggestions:
Home remedies
Get temperature
measurement
Stop to be sick with M3!
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17. Evaluating M3 software performances
• Goal: The semantic engine is not too resource consuming
• Evaluation: Measuring time consumed
• Results: Encouraging (16 – 31 ms)
• Could be embedded on
Android-powered device
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18. Demo
• Demo
http://sensormeasurement.appspot.com
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19. ⇒ Our proposed
approach:
M3 framework
Conclusion & Future work
Extract & combine
domain knowledge
Merge M3 to existing SWoT projects
Global
interoperability
Common description
Device Abstraction
Common App. Protocol
Common Nwk. Protocol
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S-LOR with more reasoning
• M3 hides semantic web technologies to the users
• M3 generic enough for other domains than IoT

20. Thank you!
• Thanks to FIESTA-IoT and Martin Serrano
• amelie.gyrard@insight-centre.org
• http://sensormeasurement.appspot.com/
• Slideshare
• Twitter
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