정하경 MMLAB Challenges : An Application Model for Pervasive Computing Guruduth Banavar, James Beck, Eugene Gluzberg, Jonathan Munson, Jeremy Sussman, DeborraZukowski.

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정하경 MMLAB Challenges : An Application Model for Pervasive Computing Guruduth Banavar, James Beck, Eugene Gluzberg, Jonathan Munson, Jeremy Sussman, DeborraZukowski. Proc. 6th ACM MOBICOM, Boston, Mass., August Presenter : Youn Do, Lee

MMLAB Why this presentation? To recognize challenges for Pervasive Computing especially from the perspective of application. Pervasive Computing!

MMLAB Outline  Pervasive Computing  Today’s problem  New Vision  New Application Model  A Glimpse of tomorrow  Related Work  Conclude

MMLAB Pervasive Computing  Anytime – Anywhere – Anydevice – Anydata- Anyservice  Smart or Active Space  Context Awareness

MMLAB Today’s Problem  The program has to be different on the different devices.  The program doesn’t live in the environment.  Some of devices cannot speak to one another.  The program requires the specific services.

MMLAB New Vision  Vision can be summarized in three precepts. A device is a repository of custom software managed by user An application is a piece of software that is written to exploit a device’s capabilities The computing environment is a virtual space that exists to store and run software A device is a portal into an application/data space An application is a means by Which a user performs a task The computing environment is the user’s information-enha nced physical surroundings.

MMLAB New Application Model(1) Design Time Load Time Run Time  The life cycle of an application The developer creates, maintains and enhances the application The system loads the application components into an application instance on particular devices The end-user invokes the applica tion and uses its functionality

MMLAB New Application Model(2)  Design Time  Identifying abstract interaction elements that capture user intent, not device mechanism.  Specifying an abstract service description language.  Creating a task-based model for program structure.  Specifying what causes a task to begin and end, and what tasks precede and follow it. An ideal development methodology for building an application is to focus on the user task, rather than the user's interaction with an interface on a specific device in a specific environment.

MMLAB New Application Model(3)  Load Time  Dynamic Discovery – Mobile device can dynamically identify and enumerate the applications and services in its local vicinity.  Requirements and capability negotiation - A device needs to negotiate with a server that hosts applications and services.  Presentation Selection – The system needs to support dynamic selection of an appropriate application interface from a set of available interfaces based on the device’s resources and form- factor.  Adaptation and Composition – The system needs to seamlessly integrate the applications and services found in the environment.

MMLAB New Application Model(4)  Run Time  Monitoring and Redistribution – The system needs to detect changes in the resources of any device or environment.  Handoff of task – The system must allow a user to initiate and perform a task in an uninterrupted manner, despite changes in the environment and devices.  Disconnection – If the network connection between client and server is detected to degrade, code might be migrated from the server to the client.  Failure Detection and Recovery – Many existing failure detection and recovery techniques may need to be modified. (e. g., Understanding disconnection.)

MMLAB A Glimpse of Tomorrow  The application is built to be run on any device.  The application is no longer thought of as a selling tool for a device.  The concept of “upgrading” software may become anachronistic.  Etc.

MMLAB Related Work  Existing technologies  User-Interface Management Systems (UIMS)  Client-server Computing Model  Java Computing Model  Web Technologies  Service Technologies (e. g., Bluetooth, MOCA)  On-going pervasive efforts  Portolano, University of Washington  Oxygen, MIT

MMLAB Conclude  PIMA, IBM T.J. Watson Research Center  Project Leader’s tomorrow  Having the same vision while lying in the different beds.  This paper challenges you, not others.

MMLAB Thank you, Any Question?