1. Update meeting November 24 th

2. 2 Outline Paper presented in Int’l Conference on Ubiquitous Computing (UCS’04), Japan Current work on new paper – Data Fusion Algorithm

3. 3 Recap on previous talk: The Context Spaces Model SjSj SiSi RiRi Operators:

4. 4 UCS’04 - Situations in the Smart Room: problem of uncertainty a b c x y z - A Meeting - A Presentation - Friday Gathering visualization of situation definitions in smart room and context states

5. 5 UCS’04 - Verification I State-space difference = where, User in a meeting User working in office

6. 6 UCS’04 - Verification II The Objective: Recognize conflicts and intelligently resort to other elements in the environment for verification. ‘On’‘Off’

7. 7 UCS’04 - Verification II...... Event TypesSituation Subspaces Contradicting Event Values Value corresponds to def. of Identifying supporting events

8. 8 UCS’04 - Verification III Healthy Walking Standing Running Sick Respiratory Rate Heart Rate Running Sick Standing The problem: ‘Running’? ’Sick’? (‘Running’ ‘Sick’)? The procedure: We reveal that: (If ‘Walking’ AND ’Sick’ then infer ‘Running’) ’Walking’ ’Sick’ It is more likely that we are in: ‘Running’

9. 9 UCS’04 - The Reasoning Process Knowledge Synthesis Conflict Analysis Verification Situation Composition Low-Level Discrepancies Discovery Raw Data input Sensor data Knowledge Synthesis Conflict analysis Verification Situations Composition input output Complex Situations Basic Situations Refined Situations Reasoning Process

10. 10 UCS’04 - Verification Layer - the ReaGine Prototype Load Monitor Reasoning Engine Cleaner KB Data Assimilator Data Inserter Monito r Stability Analyzer MQMQ MQMQ Senso Sensor Event Router Algorithms Library Monito r Same run with added uncertainty Experimental run Experiment deployment architecture

11. 11 New Paper – Synthesising Sensor Data for Context-Aware Applications Introduces a new sensor fusion technique: –Built over a general context model –Consider intuitions relevant to context-aware computing –Provides a different perspective to model knowledge & uncertainty e2 e3 e4 e5 e6 e7 e8 e9 situation1 situation2 situation3 situation4 e1 eventssituationssensor fusion Relating events to situations Dempster-Shafer starting point Context Spaces starting point

12. 12 New Paper – Synthesising Sensor Data for Context-Aware Applications for If < C then = 0; for where I. II. III. where = = IV. where and Built over the context spaces model Based on multi-attribute utility theory and probability theory Considers intuitions from human perception for context reasoning

13. 13 New Paper – Algorithm Characteristics   B B A A attribute nameserialimportance (1-5)optionalweight User RFID Y Location14No0.114286 User RFID X Location14No0.114286 User PDA Y Location13Yes0.085714 User PDA X Location13Yes0.085714 MR Light Level14No0.114286 MR Light Level24No0.114286 MR Noise Level12.5No0.071429 MR Motion Detected12.5No0.071429 MR Projector Active14Yes0.114286 MR Microphone Active14Yes0.114286 Examples of factors covered by algorithm: Individual significance of events The importance of a complete match Degree of trust in evidence accuracy Changing significance of match rejection

14. 14 Use Synthesis process to reason about and distinguish between: User presenting User attending another’s presentation User in a meeting Context Attributes: User notebook’s keyboard activity User notebook active presentation processes Light level in room User position (by tracing his mobile devices) New Paper – Smart Room Experiment

15. 15 New Paper – Experiment Deployment & Design Motes Interface Service Positioning Engine Service Hook user activity Identify presentation act. Synthesis Process Data Interpretation   Positioning Engine Notebook Location Light sensors Notebook Inferred Location

16. 16 New Paper – Sensor Technology Ekahau Positioning Engine: Berkeley Motes:

17. 17 New Paper – Results Synthesis results in differentiating between situations         Time (in min.) 15min 30min45min U. Presenting U. in Presentation U. in Meeting          U. in Presentation U. Presenting U. in Meeting        U. in Presentation U. Presenting U. in Meeting Unpredictable user behavior SynthesisDempster-Shafer