1. Compressive Data Gathering for Large- Scale Wireless Sensor Networks Chong Luo Feng Wu Shanghai Jiao Tong University Microsoft Research Asia Jun Sun Chang Wen Chen Shanghai Jiao Tong University SUNY at Buffalo, NY 14260- 2000, USA MobiCom 2009, Sep. 20-25

2. Outline Compression techniques on sensor networks – Compression with explicit communication – Distributed source coding – Compressive Sensing(sampling) Proposed Compressive Data Gathering – Data gathering diagram – Compressive sensing Simulation Conclusions

3. Compression Techniques on Sensor Networks Compression with explicit communication Cristescu et al. (2006) proposed a joint entropy coding approach 12 X1X1 H(X 2 |X 1 ) X 1, H(X 2 |X 1 ) EZLMS Link: http://www.powercam.cc/slide/3023

4. Distributed Wavelet Transform Assumptions: piecewise smooth data – Ciancio et al. (2006) and A’cimovi’c et al. (2005) (1)Even nodes first broadcast their readings. (2)Upon receiving the readings from both sides, odd nodes compute the high pass coefficients h(·) (3)Then, odd nodes transmit h(·) back and even nodes compute the low pass coefficients l(·) (4) After the transform, nodes transmit significant coefficients to the sink

5. Distributed Source Coding -- Slepian-Wolf coding D. Slepian and J. K. Wolf (1973) EZLMS Link: http://www.powercam.cc/slide/3189

6. Compressive Sensing Measurement matrix

7. Compressive Sensing transform basiscoefficient

8. Compressive Sensing transform basiscoefficient

9. G. Quer et al. (2009) x 11 x 12 x 13 x 14 x 21 x 22 x 23 x 24 …….. … …… X Example of the considered multi-hop topology. Irregular network setting [4] (1)Graph wavelet (2)Diffusion wavelet Network Scenario Setting

10. Measurement matrix  Built on routing path Routing path …………………… …… …… …… ……………………

11. Proposed Compressive Data Gathering -- Measurement Matrix

12. Goal: (1)Reduce global communication cost. (2)Load balance

13. Proposed Compressive Data Gathering -- Measurement Matrix

14. Proposed Compressive Data Gathering -- Data Recovery Conditions: (1) (2) Incoherence: correlation between  and 

15. Reconstruction: optimization Linear programming Orthogonal matching pursuit (OMP)

16. Recover Data with Abnormal Readings

17. Proposed Solution Normal reading Deviated values of abnormal readings New basis

18. NS-2 Simulation Topology: – Chain vs. Grid Data sparsity is assumed to be 5%. – For example, when N = 1000, K = 50, and M = 200

19. Capacity -- Chain topology N=1000 The distance between adjacent nodes are 10 meters

20. Capacity -- Grid topology N=1089 33 rows x 33 cols The distance between adjacent nodes is 14 meters

21. Packet Loss Rate -- Grid topology

22. Experiments on Real Data Sets -- CTD Data from Ocean K=40M=100

23. Experiments on Real Data Sets -- CTD Data from Ocean

24. Experiments on Real Data Sets -- Temperature in Data Center

25. Low spatial correlation : not sparse

26. Experiments on Real Data Sets -- Temperature in Data Center Sort d i in ascending order according to their sensing values at a particular moment t 0 – The resulting readings are piece-wise smooth. – server temperatures do not change violently, sensor readings collected within a relatively short time period can also be regard as piece-wise smooth if organized in the same order. N=498

27. Experiments on Real Data Sets -- Temperature in Data Center

28. Conclusions This paper proposed a novel scheme for energy efficient data gathering in large scale wireless sensor networks based on compressive sampling theory. – Convert compress-then-transmit process into compress-with-transmission process We have shown that CDG can achieve a capacity gain of N/M over baseline transmission.