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Massive Data Computing Research Center
Application-Aware Data Collection in Wireless Sensor Networks
Fang Xiaolin, Gao Hong, Li JianzhongHarbin Institute of Technology
Li YingshuGeorgia State University
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Outline
Existing workOur problemAn approximation algorithmA special instanceSimulations
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Existing work
Multi-application based data collectionData sharing [9]Sample as less data as possible
Data point
Existing work studies data point sampling
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Our problem
Multi-application based data collectionSample data for a continuous interval
Acoustic, video information [10], [11] Vibration measurement [12], [13], [14]Speed information [15]
Sample an interval
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Problem definition
Given a set of n tasks T , each task Tiis denoted as Ti = <bi,ei,li>
bi: beginning timeei: end timeli: data sampling interval length
Find a continuous sub-interval Ii for each task Ti so that
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Problem complexity
Non-linear non-convex optimization problem
Nonlinear integer programming problem, If bi,ei,li are regarded as integers
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Greedy algorithm overview
1. Sort by end times 2. Find task set P overlap with first 3. Find solution for P, and Remove 4. Back to step 2
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Greedy algorithm overview
1. Sort by end times 2. Find task set P overlap with first 3. Find solution for P, and Remove 4. Back to step 2
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Greedy algorithm overview
1. Sort by end times 2. Find task set P overlap with first 3. Find solution for P, and Remove 4. Back to step 2
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Greedy algorithm overview
1. Sort by end times 2. Find task set P overlap with first 3. Find solution for P, and Remove 4. Back to step 2
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Find solution for P
Compute [s,e] for the tasks overlap with each other
s=5 e=14
[s,e]=[5,14]
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Approximation algorithm analysis
Approximation ratio is 2Time complexity is
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A special instance
General problem Ti = <bi,ei,li>Special instance Ti = <bi,ei,l>The data lengths are the same
Can be solved in O(n2)
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Algorithm overview
1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
Does not affect the result
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Algorithm overview
1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
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Algorithm overview
1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
Computing x(i,j), then x(1,n) is the result[3,7]
[5,9][12,16]
x(i,j) [s,e]x(1,1) [3,7]x(2,2) [5,9]x(3,3) [12,16]x(1,2) [3,8]x(2,3) [5,10]
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Algorithm overview
1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
Computing x(i,j), then x(1,n) is the result[3,8]
x(i,j) [s,e]x(1,1) [3,7]x(2,2) [5,9]x(3,3) [12,16]x(1,2) [3,8]x(2,3) [5,10]
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Algorithm overview
1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
Computing x(i,j), then x(1,n) is the result
[5,10] x(i,j) [s,e]x(1,1) [3,7]x(2,2) [5,9]x(3,3) [12,16]x(1,2) [3,8]x(2,3) [5,10]
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Algorithm overview
1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
Computing x(i,j), then x(1,n) is the result[3,10]
x(i,j) [s,e]x(1,1) [3,7]x(2,2) [5,9]x(3,3) [12,16]x(1,2) [3,8]x(2,3) [5,10]x(1,3) =
x(1,1) U x(2,3)+
x(1,2) U x(3,3)+min
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Simulation
TossimFour casesTasks are from multi-applicationsEach application consists of periodical
tasks
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Simulation
short sampling interval lengths
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Simulation
longer sampling interval lengths
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Simulation
Different Window size
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Simulation
Data loss
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