One Minute Madness

One Minute Madness

EuroSSC 2009 One Minute Madness

Poster & Demos

Ontology based approach for data management

Ilkka Niskanen


Poster & Demos

3

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


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


Poster & Demos

A Hybrid Method for Indoor User Localisation

Milan Redžić


Poster & Demos

UNIVERSITY COLLEGE DUBLIN DUBLIN CITY UNIVERSITY TYNDALL NATIONAL INSTITUTE

Abstract In this work we describe an approach to indoor user localisation by combining image-based and RF-based methods and compare this new approach to prior work [1]. This paper details a new algorithm for indoor user localisation, demonstrating more effective user localisation than prior approaches and therefore presents the next step in combining two different technologies for localisation in indoor type environments.

System Overview

A Hybrid Method for Indoor User Localisation Milan Redžić, Ciarán Ó Conaire, Conor

Brennan, Noel O’Connor

RF localisation

where Si represents a location, 1 ≤ i ≤ I

and Oj is observed signal strength data from access point j, where 1 ≤ j ≤ J

Image matching and localisation The image matching uses the well-known SURF algorithm [2] which is implemented and installed on the N95 cell phone. An array of correct matches between given image and set of images is formed and max value indicates the image with most matches.

Experiments and Results Locations scattered throughout DCU(3 floors). Camaignr

software [3] was used. Photos from various angles, rotation, scale were captured. The hybrid technique is based on applying image matching on the smaller set of locations, which are generated by applying Bayesian analysis to the RF signal strength readings.

References1. C. O’Conaire, K. Fogarty, C. Brennan and N O’Connor: User Localisation usingVisual Sensing and RF signal strength. in: The 6th ACM Conference on EmbeddedNetworked Sensor Systems 2008, Raleigh, NC, 5-7 November 2008.2. H. Bay, A. Ess, T. Tuytelaars and L. Van Gool: SURF: Speeded Up Robust Features. in: Computer Vision and Image Understanding (CVIU), Vol. 110, pp. 346-349, 2008.3. http://wiki.urban.cens.ucla.edu/index.php?title=Campaignr

Camaignr programme Camaignr SS data

Table of Localisation results

This work is supported by Science Foundation Ireland under grant 07/CE/I1147

Semantic Rules for Context-Aware Geographical

Information Retrieval

Krzysztof Janowicz


Poster & Demos

Mobile Access to Smart Home Devices

Safiyya Rusli


Poster & Demos

Energy-optimized sensor data processing

Elena Chervakova


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


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


Poster & Demos

River Water-level Estimation Using Visual Sensing

Edel O‘Connor


Poster & Demos

UNIVERSITY COLLEGE DUBLIN DUBLIN CITY UNIVERSITY TYNDALL NATIONAL INSTITUTE 21

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


Poster & Demos

Urban-scale Sensor Network

Navigation System for Mobile User

A lo

t of t

raffi

c User

review

User

review

User

review

User

review

Vast Environmental InformationTraffic, Weather, Accident, Event, …

Flooded Navigation Screen Managed by Mobility

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 NetworkKazumasa 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.

Mobile Context Toolbox

Jakob Eg Larsen


Poster & Demos

Mobile Context Toolbox an extensible context framework for S60 mobile phonesJakob Eg Larsen and Kristian Jensen Technical University of Denmark{jel|krije}@imm.dtu.dk

Service and Content Presentation in

Ubiquitous Environments

Suparna De


Poster & Demos

www.mobilevce.com

© 2009 Mobile VCE

Service/ Content Presentation in Ubiquitous Environments

Suparna De, Abdelhak Attou, Klaus Moessner

That‘s it!

Enjoy the Poster & Demo session!


Poster & Demos

One Minute Madness

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