Harbin Institute of Technology

Application-Aware Data Application-Aware Data Collection in Wireless Sensor Collection in Wireless Sensor

NetworksNetworks

Fang XiaolinHarbin Institute of Technology

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OutlineOutline Existing work Our problem An approximation algorithm An special instance Simulations

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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]

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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

[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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