Context situations policy Daniel Cutting, Aaron Quigley University of Sydney Daniel Cutting, Aaron Quigley University of Sydney

Context situations policy

Context situations policy

Daniel Cutting, Aaron Quigley

University of Sydney

Daniel Cutting, Aaron Quigley

University of Sydney

19th July 2004 Daniel Cutting 2

IntroductionIntroduction

Daniel Cutting Ph.D. candidate at University of Sydney

(Aaron Quigley supervisor, John Zic associate supervisor)

Part of the Smart Internet CRC About half-way through Ph.D. Thesis area: application collaboration in

pervasive computing environments

Daniel Cutting Ph.D. candidate at University of Sydney

(Aaron Quigley supervisor, John Zic associate supervisor)

Part of the Smart Internet CRC About half-way through Ph.D. Thesis area: application collaboration in

pervasive computing environments


OutlineOutline

Pervasive computing Motivating scenario (art gallery) Middleware

data distribution policies

Context spaces Application to scenario Discussion

Pervasive computing Motivating scenario (art gallery) Middleware

data distribution policies

Context spaces Application to scenario Discussion


Pervasive computingPervasive computing

Mobile devices (constrained, wireless) + fixed infrastructure (powerful, wireline)

Hypothesis: applications in PCEs can be improved using context maximise availability of data minimise battery usage and network traffic constrained by user preferences use context to aid data distribution

Mobile devices (constrained, wireless) + fixed infrastructure (powerful, wireline)

Hypothesis: applications in PCEs can be improved using context maximise availability of data minimise battery usage and network traffic constrained by user preferences use context to aid data distribution

Art gallery scenarioArt gallery scenario

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.QuickTime™ and a

TIFF (LZW) decompressorare needed to see this picture.


are needed to see this picture.









Edward

BobCynthia

Gillian

Sunflowers, Van Gogh





Bob was here.

Bob was here.


Art gallery scenarioArt gallery scenario

Guide publishes data that is pushed to students (marking image of painting)

Repository shared by group stores long-lived data (group photo)

Public infrastructure stores persistent data (painting images, guest book)

Guide publishes data that is pushed to students (marking image of painting)

Repository shared by group stores long-lived data (group photo)

Public infrastructure stores persistent data (painting images, guest book)


MiddlewareMiddleware

Publish-subscribe: good for events markings on painting image

Tuple spaces: good for data persistence guest book, group repository

Build middleware that combines the two

Publish-subscribe: good for events markings on painting image

Tuple spaces: good for data persistence guest book, group repository

Build middleware that combines the two


Middleware distributionMiddleware distribution

Distributing/storing data is a problem many devices, some small, wireless may have powerful fixed infrastructure, but

sometimes purely ad hoc networks

Middleware needs flexible data distribution and storage policy

Use context to aid this policy

Distributing/storing data is a problem many devices, some small, wireless may have powerful fixed infrastructure, but

sometimes purely ad hoc networks

Middleware needs flexible data distribution and storage policy

Use context to aid this policy


ContextContext

Sensed/inferred values from environment, network, devices, applications and users e.g. beacons, bandwidth, storage capacity,

usage patterns, preferences

Complex to base policy on raw context interpose symbolic situations context situations distribution policy

Sensed/inferred values from environment, network, devices, applications and users e.g. beacons, bandwidth, storage capacity,

usage patterns, preferences

Complex to base policy on raw context interpose symbolic situations context situations distribution policy


Context spacesContext spaces

Treat context as n-dimensional space Each dimension is type of context

e.g. [bandwidth, storage capacity] sample context vector might be [high,low]

Specific situation vectors also exist (statically specified or learnt over time)

Find “nearest” situation vector to convert context vectors to situation

Treat context as n-dimensional space Each dimension is type of context

e.g. [bandwidth, storage capacity] sample context vector might be [high,low]

Specific situation vectors also exist (statically specified or learnt over time)

Find “nearest” situation vector to convert context vectors to situation


Context spacesContext spaces

aa

Time of dayLatitudeLongitude22001229v (12,29,2200)

aa

Latitude

sleeping[10,30,2300]working[50,20,0900]v

Z z z z


Dynamic clusteringDynamic clustering

Don’t specify situation vectors Cluster context vectors to automatically

identify inherent situations How should policy act if no situations

exist until run-time? Situations can shift over time to reflect

changes to contextual sources

Don’t specify situation vectors Cluster context vectors to automatically

identify inherent situations How should policy act if no situations

exist until run-time? Situations can shift over time to reflect

changes to contextual sources


Scenario: context situationsScenario: context situations Decentralised

each device determines own context To build context space, designer

identifies available context, e.g. local power, bandwidth, storage neighbours’ power, bandwidth, storage size, priority, relevance, persistence of

data painting beacons, etc.

Decentralised each device determines own context

To build context space, designer identifies available context, e.g. local power, bandwidth, storage neighbours’ power, bandwidth, storage size, priority, relevance, persistence of

data painting beacons, etc.


Scenario: context situationsScenario: context situations Select context for dimensions

data importance I, persistence P, size S context vector is of form [I,P,S]

For static space, specify situations signature, photo, demonstration e.g. photo [0.1,0.8,0.8] is when data is not

very important, persistent and large (like a photograph)

Select context for dimensions data importance I, persistence P, size S context vector is of form [I,P,S]

For static space, specify situations signature, photo, demonstration e.g. photo [0.1,0.8,0.8] is when data is not

very important, persistent and large (like a photograph)


Scenario: situations policyScenario: situations policy A device putting data into the

middleware system can: store locally, broadcast, broadcast digest

Make distribution policy using situations signature broadcast photo digest demonstration store

A device putting data into the middleware system can: store locally, broadcast, broadcast digest

Make distribution policy using situations signature broadcast photo digest demonstration store

Scenario: context policyScenario: context policy




Edward





Bob





Cynthia




Gillian

Unimportant (0.2)Long-lived (0.7)Large size (0.9)

Group photoat Sunflowers



aa

Latitude

sleeping[10,30,2300]working[50,20,0900]v

Nearest situation vector is photophoto digest


DiscussionDiscussion

Representing nominal and cyclic dimensions is troublesome

Can situations policy be automated in clustered context space?

Unknown values in context vectors could cause spurious results - project to lower dimensions?

Representing nominal and cyclic dimensions is troublesome

Can situations policy be automated in clustered context space?

Unknown values in context vectors could cause spurious results - project to lower dimensions?


Static classificationStatic classification

During design-time manually specify situation vectors

During run-time measure raw context determine context vector find nearest situation vector based on a

metric such as Euclidean distance space is not altered - essentially a lookup

During design-time manually specify situation vectors

During run-time measure raw context determine context vector find nearest situation vector based on a

metric such as Euclidean distance space is not altered - essentially a lookup

Documents

Context situations policy Daniel Cutting, Aaron Quigley University of Sydney Daniel Cutting, Aaron Quigley University of Sydney