Fine-Grain Adaptation Using Context Information Iqbal Mohomed Department of Computer Science University of Toronto Advisor: Prof. Eyal de Lara HotMobile.

Slides:



Advertisements
Similar presentations
Correlation-Based Content Adaptation For Mobile Web Browsing Iqbal Mohomed, Adin Scannell, Nilton Bila, Jin Zhang, Eyal de Lara Department of Computer.
Advertisements

6LoWPAN Extending IP to Low-Power WPAN 1 By: Shadi Janansefat CS441 Dr. Kemal Akkaya Fall 2011.
START By: Mamello Thinyane Thursday, 20 th March, 2003 Supervisor: Greg Foster 00:00 BSc Hons – Rhodes University Project Proposal for:
Electrical & Computer Engineering Department Ryerson University EDP Topics of Xavier Fernando
Community Driven Adaptation Iqbal Mohomed Eyal de Lara Department of Computer Science University of Toronto.
Presented at ICC 2012 – Wireless Network Symposium – June 14 th 2012.
LBVC: Towards Low-bandwidth Video Chats on Smartphones Xin Qi, Qing Yang, David T. Nguyen, Gang Zhou, Ge Peng College of William and Mary 1.
ASNA Architecture and Services of Network Applications Research overview and opportunities L. Ferreira Pires.
Rev BMarch 2004 The ABC Service as a Research Infrastructure Rajesh Mishra Per Johansson Cahit Akin Salih Ergut.
Reducing the Energy Usage of Office Applications Jason Flinn M. Satyanarayanan Carnegie Mellon University Eyal de Lara Dan S. Wallach Willy Zwaenepoel.
ISIS Katrinebjerg i n t e r a c t i v e s p a c e s. n e t 1 Frank Allan Hansen, Integrating the Web and the World: Contextual Trails on.
Web Clipping Presentation By: Alex Jacobs, Philip Kim, Nathan Po Web Clipping.
What is adaptive web technology?  There is an increasingly large demand for software systems which are able to operate effectively in dynamic environments.
Sujit Dey Adaptive Applications for Wireless Information Technology Sujit Dey ECE Department University of California, San Diego
POLITECNICO DI TORINO TRIBUTE and DIMMER. DIMMER - The context One of the major challenges in today’s economy concerns the reduction in energy usage and.
1 Wi-Fi applications in Paris Urban Transport Metro and Buses Régie Autonome des Transports Parisiens - RATP J. Richert Appear Networks X. Aubry.
© Siemens 2006 All Rights Reserved 1 Challenges and Limitations in a Back-End Controlled SmartHome Thesis Work Presentation Niklas Salmela Supervisor:
Mobile cloud computing: survey 1. Introduction  In recent years, applications targeted at mobile devices havs started becoming abundant with applications.
Slide 1 The 5R Adaptation Framework for Location- Based Mobile Learning Systems Kinshuk, PhD Associate Dean, Faculty of Science & Technology Professor,
Ch 1. Mobile Adaptive Computing Myungchul Kim
INFORMATION TECHNOLOGY IN BUSINESS AND SOCIETY SESSION 21 – LOCATION-BASED SERVICES SEAN J. TAYLOR.
Electronic Visualization Laboratory, University of Illinois at Chicago PAVIS Pervasive Adaptive Visualization and Interaction Service Javid Alimohideen.
IntroOH-1 CSE 5810 Wireless Body Sensor Networks (WBSN) in Healthcare Aljoharah A. Algwaiz Computer Science & Engineering Department The University of.
Copyright © 2006, Dr. Carlos Cordeiro and Prof. Dharma P. Agrawal, All rights reserved. 1 Carlos Cordeiro Philips Research North America Briarcliff Manor,
IBM Research © 2006 IBM Corporation HARMONI: Client Middleware for Long-Term, Continuous, Remote Health Monitoring Iqbal Mohomed, Maria Ebling, William.
Patterns for Location and Context-based access control
Context Modeling and Reasoning Framework for CARA Pervasive Healthcare
A Transcoding Proxy for HTML Web Pages: Web Page Sampling and Conversion Evaluation. Andrew Stone CS525m.
Information-Based Building Energy Management SEEDM Breakout Session #4.
Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing By Archan Misra (School of Information Systems, Singapore.
Software Engineering for Business Information Systems (sebis) Department of Informatics Technische Universität München, Germany wwwmatthes.in.tum.de Design.
Content Analysis Techniques to Ease Browsing with Handhelds Jalal Mahmud Yevgen Borodin I.V. Ramakrishnan Department of Computer Science State University.
Community-Driven Adaptation Iqbal Mohomed Department of Computer Science University of Toronto.
Adaptive Tree-based Convergecast Protocol CS 234 Project - Anirudh Ramesh Iyer, Swaroop Kashyap Tiptur Srinivasa, Tameem Anwar Guide - Prof. Nalini Venkatasubramanian,
Confidential & proprietary M2M communications in Transportation industry.
Context-Aware Interactive Content Adaptation Iqbal Mohomed, Jim Cai, Sina Chavoshi, Eyal de Lara Department of Computer Science University of Toronto MobiSys2006.
Fair Sharing of MAC under TCP in Wireless Ad Hoc Networks Mario Gerla Computer Science Department University of California, Los Angeles Los Angeles, CA.
Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing By Archan Misra (School of Information Systems, Singapore.
 Wendy Trem, User Experience Practice Director  Matt Miller, User Experience Designer  Bill Wolohan, Senior ASP.NET and CRM Developer  Jim Raden,
The University of SydneyPage 1 Remote laboratories and mediated interactions: The real opportunity for enhancing learning Professor David Lowe Faculty.
Community-Driven Adaptation Iqbal Mohomed Department of Computer Science University of Toronto.
Engineering on Display: Back-End Development for Sensor Instrumentation Systems Student: Brian J Kapala Supervisor: Dr. Cavalcanti.
Kiew-Hong Chua a.k.a Francis Computer Network Presentation 12/5/00.
Personalization for Location-Based E-Learning Rui Zhou and Klaus Rechert Communication Systems, Dept. of Computer Science The University of Freiburg, Germany.
OPERETTA: An Optimal Energy Efficient Bandwidth Aggregation System Karim Habak†, Khaled A. Harras‡, and Moustafa Youssef† †Egypt-Japan University of Sc.
A Context Model based on Ontological Languages: a Proposal for Information Visualization School of Informatics Castilla-La Mancha University Ramón Hervás.
L. Ardissono, C. Barbero, A. Goy and G. Petrone Dipartimento di Informatica Universita’ di Torino, Torino, Italy
REU 2004 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Distributed Rational.
Multiuser Receiver Aware Multicast in CDMA-based Multihop Wireless Ad-hoc Networks Parmesh Ramanathan Department of ECE University of Wisconsin-Madison.
Web Usability Made Easier Adaptation personalization vs. customization Aleksandra Stoeva.
Real-time Content Filtering for Mobile Devices Philip West Greg Foster and Peter Clayton Department of Computer Science Rhodes University.
UbiConn: Providing a Ubiquitous Connectivity Experience Katherine Everitt T. Scott Saponas Susumu Harada December 6, 2004.
Mobile Computing and Wireless Communication Pisa 26 November 2002 Roberto Baldoni University of Roma “La Sapienza”
REU 2007 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Information Processing.
LineUp: Visual Analysis of Multi- Attribute Rankings Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister, and Marc Streit.
9/30/2001Craig Ganoe Methods Supporting Usability Evaluation of the Collaborative Meeting Place Craig Ganoe Project Description LiNC (Learning.
REU 2009 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Information Processing.
Gaia Ubiquitous Computing Directions Roy Campbell University of Illinois at Urbana-Champaign.
Discovering Sensor Networks: Applications in Structural Health Monitoring Summary Lecture Wireless Communications.
Kalman Filter and Data Streaming Presented By :- Ankur Jain Department of Computer Science 7/21/03.
Software Architecture of Sensors. Hardware - Sensor Nodes Sensing: sensor --a transducer that converts a physical, chemical, or biological parameter into.
SOURCE:2014 IEEE 17TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING AUTHER: MINGLIU LIU, DESHI LI, HAILI MAO SPEAKER: JIAN-MING HONG.
Introduction to Mobile-Cloud Computing. What is Mobile Cloud Computing? an infrastructure where both the data storage and processing happen outside of.
Authors: Christos Stergiou Andreas P. Plageras Kostas E. Psannis
Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing By Archan Misra (School of Information Systems, Singapore.
Knowledge management in transition from e-learning to ubiquitous learning: innovations and personalization issues Research team: Maiga Chang Jon Dron Sabine.
Managing Online Services
Video Transcoding for Wireless Video
Efficient and Transparent Dynamic Content Updates for Mobile Clients
Course Project Topics for CSE5469
Presentation transcript:

Fine-Grain Adaptation Using Context Information Iqbal Mohomed Department of Computer Science University of Toronto Advisor: Prof. Eyal de Lara HotMobile 2007: Doctoral Consortium

Challenge One size does not fit all

Challenge One size does not fit all Adaptation can help! Challenge: How to pick appropriate adaptation? Existing techniques based on rules/constraints do not consider relevance of content

Thesis Use context information to determine relevance of content and adapt based on this information We investigate two domains: Web Adaptation Remote Health Monitoring

Web Adaptation: Factors to Consider Usage Context

Web Adaptation: Factors to Consider Usage Context Varying Relevance

Web Adaptation: Factors to Consider Usage Context Varying Relevance Multiple Usage

Web Adaptation: Factors to Consider Usage Context Varying Relevance Multiple Usage Situational Content E.g. Type of device, characteristics of available wireless link, user’s location

Web Adaptation: Factors to Consider Usage Context Varying Relevance Multiple Usage Situational Content E.g. Type of device, characteristics of available wireless link, user’s location For fine-grain adaptation, content must be tailored for both usage context and situational context!

Prediction 10KB 20KB Adaptation Proxy Mobile 1 Taking Usage Context Into Account Application Server 2 Server 1 Improve Fidelity Mobile 2 Application 40KB

Tailoring Content to Situational Context Content

Tailoring Content to Situational Context Content

Remote Health Monitoring Bluetooth, ZigBee, etc. Wifi, GPRS, etc.

Remote Health Monitoring Context-Aware Filtering can significantly reduce the amount of data transmitted Use context information to judge what sensor readings are expected Vary fidelity of transmitted data based on whether sensor readings conform to expectations Bluetooth, ZigBee, etc. Wifi, GPRS, etc.

Next Steps Web Adaptation Can we reduce the amount of interaction required, while still providing fine-grain adaptation? How well will our techniques work on a large scale in the real-world, over an extended period of time?

Next Steps Web Adaptation Can we reduce the amount of interaction required, while still providing fine-grain adaptation? How well will our techniques work on a large scale in the real-world, over an extended period of time? Remote Health Monitoring Can we use context-information to save energy (in ways other than reducing the amount of data)?

Next Steps Web Adaptation Can we reduce the amount of interaction required, while still providing fine-grain adaptation? How well will our techniques work on a large scale in the real-world, over an extended period of time? Remote Health Monitoring Can we use context-information to save energy (in ways other than reducing the amount of data)? Graduate! And live happily ever after …

Conclusions Use context information to determine relevance of data in a given situation When resources are constrained, optimize based on relevance Examples: When bandwidth is costly, or low link-throughput: Perform aggressive fidelity reduction on less relevant images Transmit averages when sensor readings conform to norms When screen real-estate is limited: Simplify web page by removing irrelevant images

Conclusions Use context information to determine relevance of data in a given situation When resources are constrained, optimize based on relevance Examples: When bandwidth is costly, or low link-throughput: Perform aggressive fidelity reduction on less relevant images Transmit averages when sensor readings conform to norms When screen real-estate is limited: Simplify web page by removing irrelevant images UofT; Prof. Eyal de Lara, Jin Zhang, Jim Cai, Sina Chavoshi and Alvin IBM Watson: Dr. Maria Ebling, William Jerome, Dr. Archan Misra

Conclusions Use context information to determine relevance of data in a given situation When resources are constrained, optimize based on relevance Examples: When bandwidth is costly, or low link-throughput: Perform aggressive fidelity reduction on less relevant images Transmit averages when sensor readings conform to norms When screen real-estate is limited: Simplify web page by removing irrelevant images