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Ontology based approach for data management
Ilkka Niskanen
EuroSSC 2009 One Minute Madness
Poster & Demos
2
Ontology based approach for home data management
• Sensors in smart homes• Provide various measurements from the surrounding environment
• How to integrate the heterogeneous sensor data?
• Ontologies• Provide efficient and machine readable way of representing and sharing
knowledge• Enable automated context reasoning
• VantagePoint - the ontology based home management approach• Visualizes semantic context information• Integrates heterogeneous sensor data into
contextual models
• Experiments with sensor data• SimuContext virtual sensors emulate the behavior of life context sources• Carerider bed sensors collect versatile context data concerning the sleep of a
person• This data can be utilized when creating informative diagrams
Home ReACT – a tool for real-time indoor environmental
monitoring
Tessa Daniel
EuroSSC 2009 One Minute Madness
Poster & Demos
Detect and track events
Analyse data
HomeReACT – the Realtime Indoor Environmental Monitoring Tool
Monitor comfort
Monitor faults
Tessa Daniel : Cogent Computing Applied Research Centre, Coventry University
Towards semantic enablement for spatial data infrastructure
Krzysztof Janowicz
EuroSSC 2009 One Minute Madness
Poster & Demos
Semantic Rules for Context-Aware Geographical
Information Retrieval
Krzysztof Janowicz
EuroSSC 2009 One Minute Madness
Poster & Demos
Energy-optimized sensor data processing
Elena Chervakova
EuroSSC 2009 One Minute Madness
Poster & Demos
www.imms.de
EuroSSC 2009 [email protected]
16 Sept 2009© by IMMS gGmbH, 2009
Energy-optimized Sensor Data Processing
ConSAS - Configurable Sensor and Actuator System
AnduIN: Data Stream Management System and In-Network Query Processor
Recognition of contexts detecting correlations and attributes in the measured data
Evaluating known correlations for the creation of “virtual sensors”
Query processing within sensor network or at a central instance
Tai Chi motion recognition using wearable sensors and
Hidden Markov Model method
Lars Widmer
EuroSSC 2009 One Minute Madness
Poster & Demos
Quantization HMMs
Majority Vote
Generated feature data:1. Angles between Limbs2. Limb-to-Limb orientation3. Limb Positions
Tai Chi Motion Recognition Using Wearable Sensors and Hidden Markov Model Method
For 5 Tai Chi sub-movements, data was recorded and classification methods compared, indicating the superiority of using clustered limb positions as feature input.
Time-lag as limiting factor for indoor walking navigation
Markus Straub
EuroSSC 2009 One Minute Madness
Poster & Demos
River Water-level Estimation Using Visual Sensing
Edel O‘Connor
EuroSSC 2009 One Minute Madness
Poster & Demos
UNIVERSITY COLLEGE DUBLIN DUBLIN CITY UNIVERSITY TYNDALL NATIONAL INSTITUTE 18
River Water-level Estimation Using Visual Sensing
E O’Connor, C O’Conaire, A. F. Smeaton, N. E. O’Connor, D. Diamond
Examples of the challenging image data we are using, demonstrating disparate appearance due to varying rover conditions.
Image Data
Water management is an important part of monitoring the natural environment and includes monitoring the water quality of coastal and inland marine environments.
Visual sensing can help to overcome some of the problems associated with in-situ wireless sensor networks and provide context to what is being sensed.
The development of a smart multi-modal sensor network will lead to a more robust and effective environmental sensing system.
We report on our initial work on using visual sensing to monitor a river environment.
Speed-dependent information retrieving for efficient
navigation in large-scale sensor networks
Kazumasa Ogawa
EuroSSC 2009 One Minute Madness
Poster & Demos
Urban-scale Sensor Network
Urban-scale Sensor Network
Navigation System for Mobile User
Navigation System for Mobile User
A lo
t of t
raffi
c
User
review
User
review
User
review
User
review
Vast Environmental Information
Traffic, Weather, Accident, Event, …
Flooded Navigation Screen Managed by Mobility
What is necessary information for you?How do you get?
What is necessary information for you?How do you get?
r
θSlow
Fast
Scaling Search Model
1Cspeedr ・
speedC2θ
Speed-dependent information retrieving
Map scale adjusting by speed
Constant amount of information
We focus users’ mobility. Our concept:
Speed-dependent Information Retrieving for Efficient Navigation in Large Scale Sensor NetworkSpeed-dependent Information Retrieving for Efficient Navigation in Large Scale Sensor NetworkKazumasa Ogawa and Hiroki SaitoKazumasa Ogawa and Hiroki Saito
Department of Information Systems and Multimedia Design, Tokyo Denki University, JapanDepartment of Information Systems and Multimedia Design, Tokyo Denki University, Japan
r
θ
Direction
• Urban sensing systems enable us to obtain huge amount of environmental information. However, for using this system in navigation, vast information floods users’ understandability.• We focus on users’ mobility:
• How to obtain suitable surrounding information based on users’ mobility.• How to query for appropriate range of area and how to obtain detailed suitable information.
• We propose Speed-dependent information retrieving schema for mobile user navigation.• Our technical contribution is: Scaling search model, Priority-k method, and Map scale adjusting.
Please come to our poster for further content on our investigation.
Service and Content Presentation in
Ubiquitous Environments
Suparna De
EuroSSC 2009 One Minute Madness
Poster & Demos
www.mobilevce.com
© 2009 Mobile VCE
Service/ Content Presentation in Ubiquitous Environments
Suparna De, Abdelhak Attou, Klaus Moessner
Mobile Context Toolbox an extensible context framework for S60 mobile phones
Jakob Eg Larsen and Kristian Jensen Technical University of Denmark{jel|krije}@imm.dtu.dk