Compressive Data Gathering for Large-Scale Wireless Sensor Networks

1. Compressive Data Gathering for Large-Scale Wireless Sensor Networks Chong LuoFeng 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 H(X2|X1) X1 1 2 X1, H(X2|X1) 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) • Even nodes first broadcast their readings. • Upon receiving the readings from both sides, odd nodes compute the high pass coefficients h(·) • 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 basis coefficient

8. Compressive Sensing transform basis coefficient

9. G. Queretal. (2009) X Network Scenario Setting Irregular network setting [4] Graph wavelet Diffusion wavelet Example of the considered multi-hop topology.

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

11. Proposed Compressive Data Gathering-- Measurement Matrix

12. Proposed Compressive Data Gathering-- Measurement Matrix Goal: Reduce global communication cost. 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 M=100 K=40

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

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

25. Experiments on Real Data Sets-- Temperature in Data Center Low spatial correlation : not sparse

26. Experiments on Real Data Sets-- Temperature in Data Center • Sort diin ascending order according to their sensing values at a particular moment t0 • 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-transmissionprocess • We have shown that CDG can achieve a capacity gain of N/M over baseline transmission.