Fine-Grain Adaptation Using Context Information

Iqbal MohomedDepartment of Computer Science

University of TorontoAdvisor: 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

10KB20KB

AdaptationProxy

Mobile 1

Taking Usage Context Into Account

Application

Server 2

Server 1Improve Fidelity

Mobile 2

Application

40KB

Tailoring Content to Situational Context

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Image Fidelity

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Content

Tailoring Content to Situational Context

Content

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Image Fidelity

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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

Collaborators:@ UofT; Prof. Eyal de Lara, Jin Zhang, Jim Cai, Sina Chavoshi and Alvin Chin@ 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