Demo Paper: Helping IoT Application Developers with Sensor-based

Linked Open Rules

Amelie Gyrard Christian Bonnet (Eurecom, Mobile

Communication)

Karima Boudaoud (I3S, Security)

Motivation: How to build interoperable IoT applications and reason on sensor data?

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Definitions: Internet of Things (IoT): Connect objects to internet Machine-to-Machine (M2M): communication between machines without

human intervention

How to help developers to build IoT applications: Reasoning on sensor data Reusing domain knowledge Combining domains

Proposed approach: The M3 framework

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Machine-to-Machine Measurement (M3) framework

Focusing on the reasoning part

The Machine to Machine Measurement (M3) ontology Sensor data: SenML

Media Types for Sensor Markup Language (SENML) draft-jennings-senml-10 [Jennings 2012]

Extension of the W3C Semantic Sensor Networks (SSN) ontology (Observation Value concept) To provide a basis for reasoning that can ease the development of

advanced applications

Classify all the concepts in the Machine-to-Machine (M3) ontology Domain (health, smart building, weather, room, city, etc.) Measurement type (t = temp = temperature) Sensor type (rainfall sensor = precipitation sensor) Units http://www.sensormeasurement.appspot.com/documentation/Nomenclat

ureSensorData.pdf

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Reusing domain knowledge

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Linked Open Vocabularies for Internet of Things (LOV4IoT) More than 200 domain knowledge referenced for Internet of Things http://www.sensormeasurement.appspot.com/?p=ontologies

Domain knowledge not interoperable: Lack of semantic web best practices Rules implemented with heterogeneous languages Ontology mapping tool limitations

=> Redesigning an interoperable M3 domain knowledge

Reasoning on sensor data

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

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M3 rules used in IoT application templates

The M3 framework generates IoT application templates with the M3 interoperable domain rules.

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Scenario: Integrating M3 in smart cars

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Conclusion & Future works

The M3 framework: Building IoT applications Reusing domain knowledge Reasoning on cross-domain sensor data

Future works: Automatically extracting rules from domain ontologies More complicated rules (e.g., activities) Combining domain knowledge with mapping tools

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Demonstration

Test the demonstration on your device: http://www.sensormeasurement.appspot.com/

Generating templates http://www.sensormeasurement.appspot.com/?p=m3api

Transport scenarios: http://www.sensormeasurement.appspot.com/?p=transport

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Thank you!

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Looking for real sensor data: SenML: domain, sensor, measurement type + value + unit E.g., temperature, luminosity, humidity, precipitation, wind speed,

cloud cover, etc.

gyrard@eurecom.fr

http://sensormeasurement.appspot.com/

Evaluation

Performance Reasoning between 16 –

31 ms Few data (not real, 11kB) Rules split by domains

Best practices M3 ontologies & datasets LOV, Vapour, Oops, RDF

validator, TripleChecker

Web site Google Analytics User form

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Generate IoT application Template

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IoT application to reason on sensor data

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