harbin institute of technology application-aware data collection in wireless sensor networks fang...
DESCRIPTION
3 Harbin Institute of Technology Existing work n Multi-application based data collection Data sharing [9] Sample as less data as possible Data point Existing work studies data point samplingTRANSCRIPT
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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
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2Harbin Institute of Technology
OutlineOutline Existing work Our problem An approximation algorithm An special instance Simulations
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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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4Harbin Institute of Technology
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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5Harbin Institute of Technology
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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6Harbin Institute of Technology
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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7Harbin Institute of Technology
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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8Harbin Institute of Technology
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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9Harbin Institute of Technology
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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10Harbin Institute of Technology
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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11Harbin Institute of Technology
Find solution for PFind solution for P Compute [s,e] for the tasks overlap
with each other
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12Harbin Institute of Technology
Approximation algorithm Approximation algorithm analysisanalysis
Approximation ratio is 2 Time complexity is
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13Harbin Institute of Technology
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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14Harbin Institute of Technology
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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15Harbin Institute of Technology
Algorithm overview Algorithm overview 1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
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16Harbin 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[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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17Harbin 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[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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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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19Harbin 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[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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20Harbin Institute of Technology
SimulationSimulation Tossim Four cases Tasks are from multi-applications Each application consists of
periodical tasks
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21Harbin Institute of Technology
SimulationSimulation short sampling interval lengths
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22Harbin Institute of Technology
SimulationSimulation longer sampling interval lengths
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23Harbin Institute of Technology
SimulationSimulation Different Window size
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24Harbin Institute of Technology
SimulationSimulation Data loss
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25Harbin Institute of Technology