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MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Hybrid Context Inconsistency Resolution for Context-aware Services Chenhua Chen 1 , Chunyang Ye 2, 3 and Hans-Arno Jacobsen 2 1 Department of Computer Science, University of Saarland 2 Middleware Systems Research Group, University of Toronto 3 Institute of Software, Chinese Academy of Sciences
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MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Hybrid Context Inconsistency Resolution for Context-aware Services.

Dec 14, 2015

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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.

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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MSRG.ORG

Chen, Ye and Jacobsen, PerCom'11, Seattle 4

Supply Chain Scenario

Reading RFID tagsUpdate warehouse

database

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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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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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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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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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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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MSRG.ORG

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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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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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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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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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Example of Error Recovery

• Backward recovery– Backtrack the

movement• Forward recovery– Select a different path

warehouse

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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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Resolution Algorithm

Inconsistencydetected

Postponeresolution

Applicationcontinues

Collectapplication semantics

Buildcorrelation

graphCalculate

probability

Computeerror

recovery cost

Resolveinconsistency

Errorrecovery

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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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ResultsL-RL: Remove latest

L-RO: Remove oldestM-H: Hybrid solution

Higher error rate

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Higher threshold

Location-aware

ResultsL-RL: Remove latest

L-RO: Remove oldestM-H: Hybrid solution

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Higher error rate

ResultsL-RL: Remove latest

L-RO: Remove oldestM-H: Hybrid solution

H-ER: Error recovery only

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Higher threshold

Location-aware

ResultsL-RL: Remove latest

L-RO: Remove oldestM-H: Hybrid solution

H-ER: Error recovery only

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ScalabilityRandomly generate correlation graph

Calculate probabilityof each context beinginaccurate

Record the time needed

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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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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