The document proposes the M3 framework to help IoT application developers build interoperable applications that can reason on sensor data. The M3 framework uses the M3 ontology to classify sensor data and extends the SSN ontology. It also develops interoperable domain knowledge graphs by redesigning existing domain ontologies. The framework generates application templates that include sensor-based rules compliant with the M3 ontology and domain knowledge graphs, allowing developers to easily develop applications that can reason across domains. Future work includes automating rule extraction from ontologies and supporting more complex rules.

1. Demo Paper: Helping IoT Application Developers with Sensor-based Linked Open Rules
Amelie Gyrard
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Christian Bonnet (Eurecom, Mobile Communication)
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Karima Boudaoud (I3S, Security)

2. Motivation: How to build interoperable IoT applications and reason on sensor data?
- p 2
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Definitions:
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Internet of Things (IoT): Connect objects to internet
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Machine-to-Machine (M2M): communication between machines without human intervention
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How to help developers to build IoT applications:
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Reasoning on sensor data
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Reusing domain knowledge
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Combining domains

3. Proposed approach: The M3 framework
- p 3
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Machine-to-Machine Measurement (M3) framework
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Focusing on the reasoning part

4. The Machine to Machine Measurement (M3) ontology
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Sensor data: SenML
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Media Types for Sensor Markup Language (SENML) draft-jennings- senml-10 [Jennings 2012]
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Extension of the W3C Semantic Sensor Networks (SSN) ontology (Observation Value concept)
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To provide a basis for reasoning that can ease the development of advanced applications
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Classify all the concepts in the Machine-to-Machine (M3) ontology
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Domain (health, smart building, weather, room, city, etc.)
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Measurement type (t = temp = temperature)
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Sensor type (rainfall sensor = precipitation sensor)
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Units
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http://www.sensormeasurement.appspot.com/documentation/NomenclatureSensorData.pdf - p 4

5. Reusing domain knowledge
- p 5
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Linked Open Vocabularies for Internet of Things (LOV4IoT)
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More than 200 domain knowledge referenced for Internet of Things
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http://www.sensormeasurement.appspot.com/?p=ontologies
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Domain knowledge not interoperable:
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Lack of semantic web best practices
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Rules implemented with heterogeneous languages
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Ontology mapping tool limitations
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=> Redesigning an interoperable M3 domain knowledge

6. Reasoning on sensor data
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Sensor-based Linked Open Rules (1st step)
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http://www.sensormeasurement.appspot.com/?p=swot_template
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Compliant with the M3 ontology and M3 domain knowledge
- p 6

7. M3 rules used in IoT application templates
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The M3 framework generates IoT application templates with the M3 interoperable domain rules.
- p 7

8. Scenario: Integrating M3 in smart cars
- p 8

9. Conclusion & Future works
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The M3 framework:
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Building IoT applications
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Reusing domain knowledge
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Reasoning on cross-domain sensor data
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Future works:
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Automatically extracting rules from domain ontologies
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More complicated rules (e.g., activities)
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Combining domain knowledge with mapping tools
- p 9

10. Demonstration
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Test the demonstration on your device:
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http://www.sensormeasurement.appspot.com/
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Generating templates
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http://www.sensormeasurement.appspot.com/?p=m3api
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Transport scenarios:
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http://www.sensormeasurement.appspot.com/?p=transport
- p 10

11. Thank you!
- p 11
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Looking for real sensor data:
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SenML: domain, sensor, measurement type + value + unit
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E.g., temperature, luminosity, humidity, precipitation, wind speed, cloud cover, etc.
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gyrard@eurecom.fr
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http://sensormeasurement.appspot.com/

12. Evaluation
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Performance
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Reasoning between 16 – 31 ms
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Few data (not real, 11kB)
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Rules split by domains
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Best practices
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M3 ontologies & datasets
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LOV, Vapour, Oops, RDF validator, TripleChecker
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Web site
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Google Analytics
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User form
- p 12

13. Generate IoT application Template
- p 13

14. IoT application to reason on sensor data
- p 14