Post on 16-Jun-2015
description
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?
- p 2
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
- p 3
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
- p 4
Reusing domain knowledge
- p 5
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
- p 6
M3 rules used in IoT application templates
The M3 framework generates IoT application templates with the M3 interoperable domain rules.
- p 7
Scenario: Integrating M3 in smart cars
- p 8
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
- p 9
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
- p 10
Thank you!
- p 11
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
- p 12
Generate IoT application Template
- p 13
IoT application to reason on sensor data
- p 14