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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 sampling
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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
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
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
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
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
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
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
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
11Harbin Institute of Technology
Find solution for PFind solution for P Compute [s,e] for the tasks overlap
with each other
12Harbin Institute of Technology
Approximation algorithm Approximation algorithm analysisanalysis
Approximation ratio is 2 Time complexity is
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)
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
15Harbin Institute of Technology
Algorithm overview Algorithm overview 1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming
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]
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]
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]
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
20Harbin Institute of Technology
SimulationSimulation Tossim Four cases Tasks are from multi-applications Each application consists of
periodical tasks
21Harbin Institute of Technology
SimulationSimulation short sampling interval lengths
22Harbin Institute of Technology
SimulationSimulation longer sampling interval lengths
23Harbin Institute of Technology
SimulationSimulation Different Window size
24Harbin Institute of Technology
SimulationSimulation Data loss
25Harbin Institute of Technology
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