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Context  situations  policy

Context  situations  policy. Daniel Cutting, Aaron Quigley University of Sydney. Introduction. 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.

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Context  situations  policy

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  1. Context  situations  policy Daniel Cutting, Aaron Quigley University of Sydney

  2. Introduction • 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

  3. Outline • Pervasive computing • Motivating scenario (art gallery) • Middleware • data distribution policies • Context spaces • Application to scenario • Discussion Daniel Cutting

  4. Pervasive 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 Daniel Cutting

  5. Art gallery scenario Bob was here. Gillian Edward Bob Cynthia Sunflowers, Van Gogh Bob was here.

  6. Art 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) Daniel Cutting

  7. Middleware • 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 Daniel Cutting

  8. Middleware 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 Daniel Cutting

  9. Context • 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 Daniel Cutting

  10. Context 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 Daniel Cutting

  11. Zz z z Context spaces Daniel Cutting

  12. Dynamic 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 Daniel Cutting

  13. Scenario: 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. Daniel Cutting

  14. Scenario: 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) Daniel Cutting

  15. Scenario: 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 Daniel Cutting

  16. Gillian Edward Bob Cynthia Scenario: context  policy Group photo at Sunflowers Group photo at Sunflowers Group photo at Sunflowers Nearest situation vector is photo photo digest Unimportant (0.2) Long-lived (0.7) Large size (0.9)

  17. Discussion • 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? Daniel Cutting

  18. Static 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 Daniel Cutting

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