Designing Cross-Domain Semantic Web of Things Applications Amelie Gyrard Christian Bonnet (Eurecom, Mobile Communication) Karima Boudaoud (I3S, Security)
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Designing Cross-Domain
Semantic Web of Things
Applications
Amelie Gyrard
Christian Bonnet (Eurecom, Mobile Communication)
Karima Boudaoud (I3S, Security)
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Components
Use cases
Evaluations
Demonstrations
Conclusion & Future work
2
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?
How to combine and reuse IoT data?
How to get
additional
information?
How to combine data from
different domains?
4
How to
combine
domains?
How to describe data?
How to describe data?
Taking inspiration from the Web
Automatically built
by machines
5
How to get additional information?
Agreeing on common
vocabularies to describe data
on the web:
Semantic search engines
Web sites
They built together Schema.org
6
How to apply semantic web technologies to
Internet of Things?
Machine-understandable data
Describe data with common vocabularies
Reuse domain knowledge
Link to other data
Ease the reasoning
=> How to provide a common description of sensor
data to later reason on it?
7
How to combine IoT data from different domains?
8
Innovative
applications
Interoperability on protocols or data?
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Conclusion & Future work
9
“Semantic Web of Things: an analysis of the application semantics for the IoT moving
towards the IoT convergence” [Jara et al. 2014]
Semantic Web of Things: Main challenges (1)
Machine-to-Machine (M2M): no human intervention
Global
interoperability
How?
Why? Common description
Common App. Protocol
Device Abstraction
Common Nwk. Protocol
10
Semantic Web of Things: Main challenges (2)
“Semantics for the Internet of Things: early progress and back to the future” [Barnaghi et al.
2012] 11
State of the Art: Semantic Sensor Networks
2008
‘Semantic Sensor Web’
‘Linked Sensor Data’
2013 2014
SemSOS,
‘Semantic
Perception’ ‘Infer high-level
abstraction’
‘Linked Stream Data’
2015
‘SPARQLStream’
• A) How to design
semantic-based
IoT applications?
• B) Interpret data?
Combine domains ?
Reuse domain knowledge?
• C) Security & IoT?
2011
W3C SSN ontology
Real-time? Use
semantic web
technologies?
Interpret data?
12
State of the art: W3C SSN ontology
Limitations of W3C SSN ontology:
Interoperability issues to reuse and combine domain ontologies
Need of a common description to describe sensor measurements
Need of an approach to share and reuse the reasoning approach
Need to integrate semantics to IoT and M2M
=> How to extend the W3C SSN ontology to provide a
common description of sensor data to later reason on it
by reusing domain knowledge?
http://www.w3.org/2005/Incubator/ssn/ssnx/ssn#
http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
http://www.w3.org/2015/spatial/charter 13
Three main research challenges to address
Challenge A: How to design semantic-based IoT
applications?
Challenge B: How to interpret IoT data?
Challenge B.1: How to reuse and combine IoT data?
Challenge B.2: How to reuse and combine domain knowledge?
Challenge C: How to secure IoT applications?
14
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Conclusion & Future work
15
Our solution: 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 C:
Secure IoT
applications
Challenge B.2:
Reuse domain
knowledge 16
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Conclusion & Future work
17
SWoT generator
Template used in 3 steps:
1) Designing phase
2) Development phase
3) Running phase
SWoT template
=> Benefits: No need to learn semantic web technologies
IoT
Application
generate
build
use
IoT
developers
18
Designing phase
19
*
* Domain where is deployed the sensor, not the applicative domain
Challenge A: Design
semantic based IoT
applications
Development phase
IoT
developers
SWoT
template
1) Load:
- M3 ontologies
- M3 IoT data
- M3 datasets
4) Get M3 suggestions or
high level abstractions STEPS BEFORE
Get
template
3) Execute M3 SPARQL query +
SPARQL engine
Se
ma
ntic
We
b
Fra
mew
ork
2) Execute M3 rules +
reasoning engine
20
Running phase
21
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Conclusion & Future work
22
M3 language & ontology
23
Challenge B.1 &
B.2: Combine
data and domains
M3 language & M3 ontology
Data is from heterogeneous projects and domains
Domain (e.g., health, smart building, weather, room, city, etc.)
Measurement type (e.g., t = temp = temperature)
Sensor type (e.g., rainfall sensor = precipitation sensor)
Units (e.g., Celsius, Fahrenheit, Kelvin)
M3 language implemented in the M3 ontology
Describe 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
24 http://www.sensormeasurement.appspot.com/documentation/Nomenclat
ureSensorData.pdf
M3 language: a hub for cross-domain
ontologies and datasets
25
Sensor-based Linked Open Rules (S-LOR)
26
Challenge B:
Interpret IoT
data
S-LOR: Deducing new knowledge
How to deduce new knowledge?
S-LOR: a dataset of interoperable 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!
27 Demo paper: Helping IoT application developers with Sensor-based Linked Open
Rules [Gyrard et al., ISWC 2014, SSN workshop]
Linked Open Vocabularies for
Internet of Things (LOV4IoT)
Challenge B.2:
Reuse domain
knowledge 28
A dataset of more than 270 ontology-based projects
relevant for IoT
Ontologies
Datasets
Rules to interpret IoT data
Technologies used
Sensors used
Security mechanisms used
Domains relevant for IoT
LOV4IoT
http://www.sensormeasurement.appspot.com/?p=ontologies 29
A second life for ontologies!
LOV4IoT is used to build the SWoT template
Used to re-design interoperable ontologies, rules, datasets
Limitations: Manually and not automatically
LOV4IoT
http://www.sensormeasurement.appspot.com/?p=ontologies
Collect Classify Interoperability SWoT
template
30
A second life for ontologies!
M3 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 template M3 IoT
data
Produce
31
M3 semantic engine
Enrich data & combine domains
32 Paper: Enrich Machine-to-Machine Data with Semantic Web Technologies for Cross-
Domain Applications [Gyrard et al., WF-IoT 2014]
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Conclusion & Future work
33
Security Toolbox: Attacks & Countermeasures
(STAC)
Challenge C: Help non-
security experts to secure
IoT applications
34
The STAC ontology
Paper: The STAC (Security Toolbox: Attacks & Countermeasures) ontology
[Gyrard et al., Poster, WWW 2013] 35
STAC Hub
Reusing security knowledge from LOV4IoT
36 Paper: An ontology-based approach for helping to secure the ETSI Machine-to-
Machine Architecture [Gyrard et al., iThings 2014]
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Use cases
Evaluations
Demonstrations
Conclusion & Future work
37
M3 use cases
3 Mock-ups: Naturopathy, Tourism, Transport
Proof of concept: less user-friendly
Integrating the M3 approach everywhere!
Cloud, Android-powered devices and Gateway
Combine domain-specific sophisticated applications
Not just data visualization
Suggestions or high-level abstractions
38
Use Case: Embedding M3 in smart fridges
M3 suggestions:
Home remedies
Get temperature
measurement
Stop to be sick with M3!
39
Use Case: Embedding M3 in smart luggage
M3 suggestions:
Garments & Activities
Get weather
measurement
Stop to forget things with M3!
Smart Luggage
Destination: Mountain in
winter
Destination: Beach in
summer
40
Use Case: Embedding M3 in smart cars
Avoid accidents with M3!
41
Evaluations: Research hypotheses
Templates help IoT projects build their scenarios
The semantic engine is not too resource consuming
The semantic engine is generic enough to support
various kind of IoT measurement.
The interoperable knowledge bases built follows
semantic web best practices.
Our knowledge bases help non-experts in semantic
web or in security
LOV4IoT is exploited outside of the M3 framework.
42
Evaluating the SWoT generator
Do we have templates covering the most popular IoT
use cases?
http://www.sensormeasurement.appspot.com/?p=m3_scenario
Adding a new template?
Less than 1 day
Depends on whether we already have the interoperable domain
knowledge
43
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
44
Demo
Demo
http://sensormeasurement.appspot.com
45
M3 framework at work
Domain
experts
IoT developers
End users
Design
applications
Need new
applications
Standardize
Design new ontology
matching tools +
Automatic extraction of
domain knowledge
Exploit &
Contribute
46
Relevant for different communities
Agenda
Introduction & Motivation
State of The Art & Main challenges
Contributions: M3 framework
Conclusion & Future work
47
Summary of contributions
48
Ch
all
en
ge A
M3: An entire chain from sensor data to build IoT
cross-domain IoT applications
Sensor
data
Interpret data +
Combine
domains
Interoperable
sensor data
descriptions
Reuse domain
knowledge Build IoT
applications Provide
template
Secure
applications
Ch
all
en
ge C
Ch
all
en
ge A
Ch
all
en
ge B
.1
Ch
all
en
ge
B
Ch
all
en
ge B
.2
49
Conclusion & Lessons learnt
M3: an innovative approach to assist users in
designing interoperable cross-domain Semantic Web of
Things applications:
A uniform language for sensor data descriptions
An open-source approach to interpret IoT data
Combine domains
Semantics is hidden to the users
Lessons learnt:
M3 generic enough for other domains than IoT and security
50
Our proposed
approach:
M3 framework
Future work
Sensor Plug & Play
Extract & combine
domain knowledge
Standardizing common descriptions
Merge M3 to existing SWoT projects
Global
interoperability
Common description
Device Abstraction
Common App. Protocol
Common Nwk. Protocol
51
S-LOR with more reasoning
Future work: Merge M3 to existing SWoT projects
Use real datasets & scenarios
+ real-time
Suggest machine learning algorithms
to employ for complicated sensors
Connect
new sensors
52
Rewrite
ontologies
Future work: Extracting and combining domain
knowledge
Extracting popular concepts from domain ontologies
Cloud tag inspired by the W3C SSN validator
Extracting rules from ontologies
OWL 2 RL template, DLEJena
Combining domain knowledge
Design and combine new ontology matching tools
Look at ontology alignment ontology & merging tools
Designing an interoperable domain knowledge
53
Thank you!
http://sensormeasurement.appspot.com/
54
Relevant Publications
International Conferences:
Enrich Machine-to-Machine Data with Semantic Web Technologies
for Cross-Domain Applications (WF-IoT 2014)
An ontology-based approach for helping to secure the ETSI
Machine-to-Machine Architecture (iThings 2014)
A machine-to-machine architecture to merge semantic sensor
measurements (WWW 2013, DC)
International Workshops:
Standardizing Generic Cross-Domain Applications in Internet of
Things (Globecom , WTS, 2014)
Demo paper: Helping IoT application developers with Sensor-based
Linked Open Rules (ISWC, SSN 2014)
See Google Scholar for more publications
Participation to standardizations:
55
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