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![Page 1: MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Hybrid Context Inconsistency Resolution for Context-aware Services.](https://reader030.fdocuments.us/reader030/viewer/2022033105/56649c755503460f94929b69/html5/thumbnails/1.jpg)
MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Hybrid Context Inconsistency Resolution for Context-aware Services
Chenhua Chen1, Chunyang Ye2, 3 and Hans-Arno Jacobsen2
1Department of Computer Science, University of Saarland2Middleware Systems Research Group, University of Toronto
3 Institute of Software, Chinese Academy of Sciences
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 2
Outline• Background– Context-awareness
• Research Problem– Context Inconsistency Resolution
• Hybrid Solution– Context Correlation Model– Application Recovery Model
• Experimental Results
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 3
Context-awarenessAn important feature of pervasive applications
Context-awarenessSense environment
automaticallyRemember historyAdapt to changing
situations
Contexts locations, time etc. Implicit input/outputSeamless integrated
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 4
Supply Chain Scenario
Reading RFID tagsUpdate warehouse
database
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and 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
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 6
Context Inconsistency Resolution
Context queue
Consistencyconstraints
Validate consistency constraints
Inconsistent contexts
Inconsistencyresolution
1) Remove latest
2) Remove oldest
3) Remove all
4) User preference, heuristics etc.
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 7
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
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 8
Our Proposal: Hybrid Solution
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 9
Example of Our Proposal
1. Two readers report inconsistent readings
2. Postpone inconsistency resolution3. Warehouse check in, collect weight info
4. Update profile of goods
5. Resolve inconsistent readings based on weight and profile
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 10
Challenges
When to resolve?Close to T2:
unacceptable recovery cost
Close to T0: Semantic
information is of limited usefulness
How to make use of the application
semantics in resolution?
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 11
Example of Application Semantics
warehouse
• 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)
• (2, 3) is more likely to be inaccurate, since it is impossible to move from (2, 3) to (4, 4) in two steps.
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 12
Context-correlation Model
C1
C2 C3
C4
C5 C7
C6
C1
C2 C7
C4C5
C8
C9
f e (c 3, a)
Current contexts
Contexts after invoking action a fe(CL, a): | NL – CL|≤ 1
CL
NL
C3 C8
At least one of C3 and C8 is inaccurate!
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
13
C1
C2
C3…
C7
C8
C9…
Ci
Cj
Ck…
…
Context Ci1 Contexts Ci2 Contexts Cin
C1
C2
C3
p1
p2
p3
C3
C1
C2
p1
p2
p1 ≥ 1- p2 * p3
Context-correlation Model
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 14
Application Error Recovery
Inconsistencyresolution
s0 s2a1 a2s1 s3a3 s4a4
Sensing c
Inconsistency detection
a2
s2’ s3’a3
b4
s2”
b3
b2
Backward recovery
Forward recovery
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 15
Example of Error Recovery
• Backward recovery– Backtrack the
movement• Forward recovery– Select a different path
warehouse
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 16
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
m
i j
n
i i beccacpc11
)()(
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 17
Resolution Algorithm
Inconsistencydetected
Postponeresolution
Applicationcontinues
Collectapplication semantics
Buildcorrelation
graphCalculate
probability
Computeerror
recovery cost
Resolveinconsistency
Errorrecovery
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 18
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
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 19
ResultsL-RL: Remove latest
L-RO: Remove oldestM-H: Hybrid solution
Higher error rate
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 20
Higher threshold
Location-aware
ResultsL-RL: Remove latest
L-RO: Remove oldestM-H: Hybrid solution
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 21
Higher error rate
ResultsL-RL: Remove latest
L-RO: Remove oldestM-H: Hybrid solution
H-ER: Error recovery only
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 22
Higher threshold
Location-aware
ResultsL-RL: Remove latest
L-RO: Remove oldestM-H: Hybrid solution
H-ER: Error recovery only
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 23
ScalabilityRandomly generate correlation graph
Calculate probabilityof each context beinginaccurate
Record the time needed
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 24
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
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 25
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MIDDLEWARE SYSTEMSRESEARCH GROUP
MSRG.ORG
Chen, Ye and Jacobsen, PerCom'11, Seattle 26
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