1. Hybrid Context Inconsistency Resolution for Context-aware Services
Chenhua Chen1, Chunyang Ye2, 3 and Hans-Arno Jacobsen2
1Department of Computer Science, University of Saarland
2Middleware Systems Research Group, University of Toronto
3 Institute of Software, Chinese Academy of Sciences
Chenhua Chen, Chunyang Ye and Hans-Arno Jacobsen, PerCom'11, Seattle

2. Chen, Ye and Jacobsen, PerCom'11, Seattle
Outline
Background
Context-awareness
Research Problem
Context Inconsistency Resolution
Hybrid Solution
Context Correlation Model
Application Recovery Model
Experimental Results
Chen, Ye and Jacobsen, PerCom'11, Seattle
Chenhua Chen, Chunyang Ye and Hans-Arno Jacobsen, PerCom'11, Seattle

3. Chen, Ye and Jacobsen, PerCom'11, Seattle
Context-awareness
An important feature of pervasive applications
Contexts
locations, time etc.
Implicit input/output
Seamless integrated
Context-awareness
Sense environment
automatically
Remember history
Adapt to changing
situations
Chen, Ye and Jacobsen, PerCom'11, Seattle
Chenhua Chen, Chunyang Ye and Hans-Arno Jacobsen, PerCom'11, Seattle

4. Chen, Ye and Jacobsen, PerCom'11, Seattle
Supply Chain Scenario
Update warehouse
database
Reading RFID tags
Chen, Ye and Jacobsen, PerCom'11, Seattle
Chenhua Chen, Chunyang Ye and Hans-Arno Jacobsen, PerCom'11, Seattle

5. Context Inconsistency
Reasons
Environmental noise
Examples
RFID reader report wrong readings
Register incorrect number in warehouse
GPS or GSM devices report
inaccurate location
Pick wrong route
Chen, Ye and Jacobsen, PerCom'11, Seattle
Chenhua Chen, Chunyang Ye and Hans-Arno Jacobsen, PerCom'11, Seattle

6. Context Inconsistency Resolution
Validate consistency
constraints
Consistency
constraints
Context queue
Inconsistency
resolution
2) Remove oldest
3) Remove all
Inconsistent
contexts
4) User preference,
heuristics etc.
1) Remove latest
Chen, Ye and Jacobsen, PerCom'11, Seattle
Chenhua Chen, Chunyang Ye and Hans-Arno Jacobsen, PerCom'11, Seattle

7. Chen, Ye and Jacobsen, PerCom'11, Seattle
Limitations
Difficult to identify problematic contexts
E.g., remove the latest, oldest,
least frequently used etc.
Counter example to remove the latest
Two RFID readers, the first one is inaccurate,
the second one is accurate
Resolution approaches rely heavily on constraints
Accuracy and completeness of constraints are crucial
Counter example
Constraint: Two RFID readers report identical readings
Reported readings are the same but inaccurate
Chen, Ye and Jacobsen, PerCom'11, Seattle

8. Our Proposal: Hybrid Solution
Chen, Ye and Jacobsen, PerCom'11, Seattle

9. Example of Our Proposal
1. Two readers report inconsistent readings
2. Postpone inconsistency resolution
3. Warehouse check in, collect weight info
4. Update profile of goods
5. Resolve inconsistent readings
based on weight and profile
Chen, Ye and Jacobsen, PerCom'11, Seattle

10. Challenges How to make use of the application semantics in resolution?
Close to T0: Semantic information is of limited usefulness
When to resolve?
Close to T2: unacceptable recovery cost
Chen, Ye and Jacobsen, PerCom'11, Seattle

11. Example of Application Semantics
Previous Location: (2, 3)
Current Location: (4, 5)
Inconsistency found!
The probability of each
context being inaccurate
is 50%
Continue move one step
New Location: (4, 4)
warehouse
(2, 3) is more likely to be inaccurate, since it is impossible to
move from (2, 3) to (4, 4) in two steps.
Chen, Ye and Jacobsen, PerCom'11, Seattle

12. Context-correlation ModelCL NL C1 C2 C3 C4 C5 C7 C6 C8 C9
fe (c3, a)
Current
contexts
Contexts after
invoking action a
fe(CL, a): | NL – CL|≤ 1 C3 C8
At least one of C3 and C8 is inaccurate!
Chen, Ye and Jacobsen, PerCom'11, Seattle

13. Context-correlation Model… C7 C8 C9 Ci Cj Ck
Context Ci1
Contexts Ci2
Contexts Cin C1 C2 C3 p1 p2 p3
p1 ≥ 1- p2 * p3

14. Application Error Recovery
Inconsistency
resolution s0 s2 a1 a2 s1 s3 a3 s4 a4
Sensing c
detection
s2’
s3’ b4
s2” b3 b2
Backward recovery
Forward recovery
Chen, Ye and Jacobsen, PerCom'11, Seattle

15. Example of Error Recovery
Backward recovery
Backtrack the movement
Forward recovery
Select a different
path
warehouse
Chen, Ye and Jacobsen, PerCom'11, Seattle

16. Chen, Ye and Jacobsen, PerCom'11, Seattle
Cost Model
Compensation cost (cpc)
For backward recovery
Cost of compensating a task
Execution cost (ecc)
For forward recovery
Cost of executing a task
Total cost for an error recovery plan
Chen, Ye and Jacobsen, PerCom'11, Seattle

17. Resolution Algorithm Collect application semantics Inconsistency
detected
Postpone
resolution
Application
continues
Build
correlation
graph
Compute
error
recovery cost
Resolve
inconsistency
Calculate
probability
Error
recovery
Chen, Ye and Jacobsen, PerCom'11, Seattle

18. Chen, Ye and Jacobsen, PerCom'11, Seattle
Experiment Setup
16 X 16 Map
cpc = ecc = 1
Search the target in
a heuristic way
Random placement
of goods
Metrics:
Accuracy of resolution
Cost of error recovery
warehouse
Chen, Ye and Jacobsen, PerCom'11, Seattle

19. Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution
Higher error rate
Chen, Ye and Jacobsen, PerCom'11, Seattle

20. Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution
Location-aware
Higher threshold
Chen, Ye and Jacobsen, PerCom'11, Seattle

21. Chen, Ye and Jacobsen, PerCom'11, Seattle
Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution H-ER: Error recovery only
Higher error rate
Chen, Ye and Jacobsen, PerCom'11, Seattle

22. Chen, Ye and Jacobsen, PerCom'11, Seattle
Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution H-ER: Error recovery only
Location-aware
Higher threshold
Chen, Ye and Jacobsen, PerCom'11, Seattle

23. Chen, Ye and Jacobsen, PerCom'11, Seattle
Scalability
Randomly generate
correlation graph
Calculate probability
of each context being
inaccurate
Record the time
needed
Chen, Ye and Jacobsen, PerCom'11, Seattle

24. Chen, Ye and Jacobsen, PerCom'11, Seattle
Conclusions
A novel approach to resolve context inconsistency
Combine low-level inconsistency resolution with
high-level error recovery
Correlation model to reason about inaccurate contexts
Cost model to calculate recovery cost
Algorithm to trade off accuracy against recovery cost
Future work
More real-life experiments
Extend the correlation model to support confidence
Chen, Ye and Jacobsen, PerCom'11, Seattle

25. Chen, Ye and Jacobsen, PerCom'11, Seattle

26. Chen, Ye and Jacobsen, PerCom'11, Seattle
Related Work
Existing resolution strategies
[Heckmann, IJCAI-MRC’05]
Remove the latest, the oldest, the least frequently used
[Bu et al. QSIC’06]
Remove all
[Park et al. Compsac’05]
User preference
[Capra et al. TSE’03]
Auction
[Xu et al. ICDCS’08]
Heuristics
Chen, Ye and Jacobsen, PerCom'11, Seattle