Harbin Institute of Technology Application-Aware Data Collection in Wireless Sensor Networks Fang...
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Harbin Institute of Technology
Application-Aware Data Application-Aware Data Collection in Wireless Sensor Collection in Wireless Sensor
NetworksNetworks
Fang XiaolinHarbin Institute of Technology
2Harbin Institute of Technology
OutlineOutline Existing work Our problem An approximation algorithm An special instance Simulations
3Harbin Institute of Technology
Existing workExisting work Multi-application based data collection
Data sharing [9]Sample as less data as possible
Data point
Existing work studies data point sampling
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Our problemOur problem Multi-application based data collection Sample 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 definitionProblem definition Given a set of n tasks T , each task Ti
is 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 complexityProblem 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 overviewGreedy 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 overviewGreedy 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 overviewGreedy 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 overviewGreedy 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 PFind solution for P Compute [s,e] for the tasks overlap
with each other
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Approximation algorithm Approximation algorithm analysisanalysis
Approximation ratio is 2 Time complexity is
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A special instanceA special instance General problem Ti = <bi,ei,li> Special instance Ti = <bi,ei,l> The data length is the same
Can be solved in O(n2)
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Algorithm overview 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 Algorithm overview 1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
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Algorithm overview 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 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]
18Harbin Institute of Technology
Algorithm overview 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 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
x(1,2) U x(3,3)xmin
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SimulationSimulation Tossim Four cases Tasks are from multi-applications Each application consists of
periodical tasks
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SimulationSimulation short sampling interval lengths
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SimulationSimulation longer sampling interval lengths
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SimulationSimulation Different Window size
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SimulationSimulation Data loss
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