Post on 13-Jan-2016
description
transcript
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
19th July 2004 Daniel Cutting 3
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
19th July 2004 Daniel Cutting 4
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.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.QuickTime™ and a
TIFF (LZW) decompressorare needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.QuickTime™ and a
TIFF (LZW) decompressorare needed to see this picture.
Edward
BobCynthia
Gillian
Sunflowers, Van Gogh
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Bob was here.
Bob was here.
19th July 2004 Daniel Cutting 6
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)
19th July 2004 Daniel Cutting 7
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
19th July 2004 Daniel Cutting 8
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
19th July 2004 Daniel Cutting 9
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
19th July 2004 Daniel Cutting 10
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
19th July 2004 Daniel Cutting 11
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
19th July 2004 Daniel Cutting 12
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
19th July 2004 Daniel Cutting 13
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.
19th July 2004 Daniel Cutting 14
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)
19th July 2004 Daniel Cutting 15
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
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.QuickTime™ and a
TIFF (LZW) decompressorare needed to see this picture.
Edward
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Bob
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Cynthia
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.QuickTime™ and a
TIFF (LZW) decompressorare needed to see this picture.
Gillian
Unimportant (0.2)Long-lived (0.7)Large size (0.9)
Group photoat Sunflowers
Group photoat Sunflowers
Group photoat Sunflowers
aa
Latitude
sleeping[10,30,2300]working[50,20,0900]v
Nearest situation vector is photophoto digest
19th July 2004 Daniel Cutting 17
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?
19th July 2004 Daniel Cutting 18
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