Short position paper, accepted at the Semantic Sensor Network workshop, at ISWC 2010

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Myriam Leggieri, Alexandre Passant, Manfred Hauswirth
DERI NUI Galway, Ireland
A contextualised cognitive
perspective for Linked Sensor Data

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Overview
1. Self and context awareness
1. Benefits
2. Research Question
2. Current solutions
1. Sensor ontologies
2. Context classification architectures
3. Research Challenges
3. Our proposal
1. Contextualised cognitive perspective
2. Ontology alignments and extension
4. Conlusions and Future Work

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Self and Context awareness
 Context
– Any information that can be used to characterize the situation of
entities (i.e. whether a person, place or object) that are considered
relevant to the interaction between a user and an application,
including the user and the application themselves [DeyAbowd2000]
 External context
– Measured by hardware sensors
– I.e. location, light, sound, movement, touch,
 Internal context
– Specified by the user or captured monitoring the user’s
interaction
– I.e. user’s goal, tasks, work context, business processes

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Self and Context awareness : benefits

Self-awareness benefits
→ Issue: Find all the sensors acquiring
oceanographic data in Cancùn
– Solution: Self-awareness: auto-determine
• The kind of data acquired
• The location
→ Issue: Observation understanding
– Solution: Machine-understandable
description of oservations and
measurements
How is the ocean like
at Cancùn right now?

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
Context-awareness benefits
Self and Context awareness : benefits
Imagine:
Calm ocean detected
… but at the same time …
another SN detects a movement of earth plates
→ Issue:
Is it generally associated with storm surges?

Solution: Search the LoD cloud

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Self and Context awareness: research challenge
Improvements in
– Hazardous detection
– Sensor retrieval
– Sensor data clustering
What is needed
1. Proper ontologies to support detailed
descriptions
2. Effective context classification
architecture

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Current solutions – sensor ontologies
Sensor features to be described
– Sensor / Device, Capabilities, Process, Physical
properties, Observation, Networks
Current ontologies specialized missed in covering all those
features
– SWAMO - Interoperability Sensor Web products / Sensor Web services
– MMI Device and CSIRO sensor ontologies - System and
capabilities, Process composition, Operational and Response model
With the except of W3C SSN-Xg ontology
– Covers all the basic sensor features; foreseen further
integrations

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Current solutions – context classification
architecture
SOCAM (Service-oriented Context-Aware Middleware)
– Centralized context interpreter
COBRA (Context Broker Architecture)
– Agent based; centralized context broker
(KB, inference, acquisition, etc.)
Context Toolkit
– P2P architecture, still needs a
centralized discoverer

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Current solutions: research challenges
Challenges:
1. Sensor ontologies:
1. Develop and choose the right ontologies to
integrate with the SSN-XG one
2. Context classification
1. Storage space issue; one point of failure
2. P2P: network boundaries; user responsability
3. Data not linked together – unless because of
classification output; but in this way it is not
widely reusable

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Our proposal: contextualized cognitive
perspective
1. Contextualized cognitive approach to sensor data
classification

Cognitive: inspired by associative nature
of human cognitiveness

Contextualized: delimited to the sensor
environment
Human
Memory
LOD
Cloud
C
Environment
Unlimited, unweak,
decentralized

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Our proposal: ontology alignments and
extension
1. Ontology support to context description

Domain-agnostic ontology to
describe sensor-related concepts

Event modelling ontology

Upper-level ontology

Additional concepts:
SensorProject, SensorRole,
SensorHierarchy

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Our proposal: ontology alignments and extension
Sensor-related concept descriptions

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Our proposal: ontology alignments and extension
Context and situation related concept descriptions

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Conclusions and future work
Task: improvement of sensor reality understanding
LoD cloud as
– Enhancement to classification
– New mean for human cognitiveness emulation
Steps
– Ontologies extended and aligned
– Validation of ontology modelling choices
– Continue with the implementation including user
feedback
Great Challenge:
– Effectively browsing the LoD cloud