2. Ontology based approach for data management Ilkka Niskanen EuroSSC 2009 One Minute Madness Poster & Demos

3. 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

4. Home ReACT – a tool for real- time indoor environmental monitoring Tessa Daniel EuroSSC 2009 One Minute Madness Poster & Demos

5. 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

6. Towards semantic enablement for spatial data infrastructure Krzysztof Janowicz EuroSSC 2009 One Minute Madness Poster & Demos

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8. Semantic Rules for Context-Aware Geographical Information Retrieval Krzysztof Janowicz EuroSSC 2009 One Minute Madness Poster & Demos

10. Mobile Access to Smart Home Devices Safiyya Rusli EuroSSC 2009 One Minute Madness Poster & Demos

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12. Energy-optimized sensor data processing Elena Chervakova EuroSSC 2009 One Minute Madness Poster & Demos

13. www.imms.de EuroSSC 2009 elena.chervakova@imms.de 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

14. Tai Chi motion recognition using wearable sensors and Hidden Markov Model method Lars Widmer EuroSSC 2009 One Minute Madness Poster & Demos

15. Quantization HMMs Majority Vote Generated feature data: 1. Angles between Limbs 2. Limb-to-Limb orientation 3. 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.

16. Time-lag as limiting factor for indoor walking navigation Markus Straub EuroSSC 2009 One Minute Madness Poster & Demos

18. River Water-level Estimation Using Visual Sensing Edel O‘Connor EuroSSC 2009 One Minute Madness Poster & Demos

19. 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.

20. Speed-dependent information retrieving for efficient navigation in large-scale sensor networks Kazumasa Ogawa EuroSSC 2009 One Minute Madness Poster & Demos

21. Urban-scale Sensor Network Navigation System for Mobile User A lot of traffic Use r revi ew 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 θ Slow Fast Scaling Search Model 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 Network Kazumasa Ogawa and Hiroki Saito Department 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.

22. Service and Content Presentation in Ubiquitous Environments Suparna De EuroSSC 2009 One Minute Madness Poster & Demos

23. www.mobilevce.com © 2009 Mobile VCE Service/ Content Presentation in Ubiquitous Environments Suparna De, Abdelhak Attou, Klaus Moessner

24. Mobile Context Toolbox Jakob Eg Larsen EuroSSC 2009 One Minute Madness Poster & Demos

25. 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

26. That‘s it! Enjoy the Poster & Demo session! EuroSSC 2009 One Minute Madness Poster & Demos