Application-Aware Data Collection in Wireless Sensor Networks

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1 This image cannot currently be displayed. Application-Aware Data Collection in Wireless Sensor Networks Fang Xiaolin, Gao Hong, Li Jianzhong Harbin Institute of Technology Li Yingshu Georgia State University

2 Outline Existing work Our problem An approximation algorithm A special instance Simulations 2

3 Existing work Multi-application based data collection Data sharing [9] Sample as less data as possible Existing work studies data point sampling Data point 3

4 Our 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 4

video information [10], [11] Vibration measurement

5 Problem definition Given a set of n tasks T, each task Ti is denoted as Ti = <bi,ei,li> bi: beginning time ei: end time li: data sampling interval length Find a continuous sub-interval Ii for each task Ti so that 5

6 Problem complexity Non-linear non-convex optimization problem Nonlinear integer programming problem, If bi,ei,li are regarded as integers 6

7 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 7

8 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 8

9 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 9

10 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 10

11 Find solution for P Compute [s,e] for the tasks overlap with each other s=5 e=14 [s,e]=[5,14] 11

12 Approximation algorithm analysis Approximation ratio is 2 Time complexity is 12

13 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(n 2 ) 13

14 Algorithm overview 1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming Does not affect the result 14

15 Algorithm overview 1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming 15

16 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] 16

17 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] 17

18 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] 18

19 Algorithm overview 1. Sort by the end times 2. Remove tasks cover other tasks 3. Dynamic programming x(1,3) = Computing x(i,j), then x(1,n) is the result min [3,10] x(1,1) U + x(2,3) x(1,2) U + x(3,3) 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] 19

20 Tossim Four cases Simulation Tasks are from multi-applications Each application consists of periodical tasks 20

21 Simulation short sampling interval lengths 21

22 Simulation longer sampling interval lengths 22

23 Simulation Different Window size 23

24 Simulation Data loss 24

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