1. Data Fusion Improves the Coverage of Wireless Sensor Networks Guoliang Xing 1, Rui Tan 2, Benyuan Liu 3, Jianping Wang 2, Xiaohua Jia 2,Chih-wei Yi 4 1 Michigan State University, 2 City University of Hong Kong, 3 University of Massachusetts Lowell, 4 National Chiao Tung University, Taiwan

2. Outline Motivation –Limitations of current studies on sensing coverage Problem definition –(α,β)-coverage under disc and fusion models Scaling laws of network density for coverage –Disc model vs. data fusion model Simulations 2

3. Mission-critical Sensing Applications Large spatial deployment region Resource-constrained sensor nodes Stringent performance requirements –High sensing prob., e.g., 99%, low false alarm rate, e.g., 1% 3 100 seismometers in UCLA campus [Estrin 02] acoustic sensors detecting AAV http://www.ece.wisc.edu/~sensit/

4. Fundamental requirement of critical apps –How well is a region monitored by sensors? –Full coverage: any point in a region is covered Network density to achieve full coverage –Critical metric for deployment cost and lifetime Sensing Coverage 4

5. State of the Art Numerous studies on coverage protocols/analysis –Our earlier work [sensys 03] cited >600 on Google Scholar –K-coverage and barrier coverage Most existing results are based on simplistic models –All 5 related papers since MobiCom 04 assumed disc model –Ignored sensing uncertainties and collaboration Collaborative signal processing theories –Focused on small-scale networks –Made performance analysis of large networks difficult 5

6. 6 Sensing Model ✘ The (in)famous disc model ✘ Any target within r is detected ✘ Deterministic and independent sensing ✔ Real-world event sensing Probabilistic, no cookie-cutter like “sensing range”! Collaborative sensing is a must r Real Acoustic Vehicle Detection Experiments [Duarte 04]

7. 7 Sensor Measurement Model Reading of sensor i is y i = s i + n i Decayed target energy Noise energy follows normal distribution n i ~ N(μ,σ 2 ) Signal to noise ratio (SNR) is S /σ, 2 ≤ k ≤ 5 Real Acoustic Vehicle Detection Experiments [Duarte 04]

8. N – CDF of Normal distribution s i – Energy reading of sensor i Data Fusion Model Sensors within distance R from target fuse their readings –The sum of readings is compared again a threshold η –R is the fusion range False alarm rate P F = 1-N(n· η) Detection probability P D = 1 –N(n·η - Σs i ) R 8

9. Outline Motivation –Limitations of current studies on sensing coverage Problem definition –(α,β)-coverage under disc and fusion models Scaling laws of network density for coverage –Disc model vs. data fusion model Simulations 9

10. (α,β)-Coverage A physical point p is (α,β)- covered if –The system false alarm rate P F ≤ α –For target at p, the detection prob. P D ≥ β (α,β)-coverage is the fraction of points in a region that are (α,β)-covered –Full (α,β)-coverage: any point is (α,β)-covered Random network deployment –Nodes deployed by Poisson process of density ρ 10

11. Disc and Fusion Coverage Coverage under the disc model –Sensors independently detect targets within sensing range r Coverage under the fusion model –Sensors collaborate to detect targets within fusion range R 11 grayscale represents P D

12. (α,β)-Coverage under Disc Model Choose sensing range r s.t. if any point is covered by a sensor, the region is ( α,β )-covered ρ d : density of network Q -1 : inverse Complementary CDF of std Normal distr. 12 [Liu 2004]

13. (α,β)-Coverage under Fusion Model The (α,β)-coverage of a network of density ρ f Г(R) is function of fusion range R, α, β and w(.) 13 optimal fusion range opt fusion range grows w density!

14. Outline Motivation –Limitations of current studies on sensing coverage Problem definition –(α,β)-coverage under disc and fusion models Scaling laws of network density for coverage –How does network density grow when coverage  1 –Disc model vs. data fusion model Simulations 14

15. 15 Full Coverage with Opt Fusion Range ρ f and ρ d are densities of random networks under fusion and disc models When k=2 (acoustic signals) Density significantly reduced via data fusion!

16. Network Density vs. SNR For full coverage with fixed fusion range R Disc model is good for high SNR and small k –Most low-power sensors have low SNRs, and k ≥ 2 16

17. Coverage with Non-opt Fusion Range Density ratio ρ f / ρ d satisfies ρ f << ρ d for high coverage requirement –Fusion range R may grow with network density –Sensing range r is a constant 17

18. 18 Trace-driven Simulations Data traces collected from 75 acoustic sensors in vehicle detection experiments [Duarte 04] –α=0.05, β=0.95, deployment region: 1000m x 1000m fusion saves more sensors

19. Conclusions Reveal limitations of current analytical results –Only applicable for slowly decaying signals with high SNRs –Disc model significantly underestimates coverage Provide insights into fusion design of large networks –Data fusion can significantly improve coverage! –Fusion parameters (e.g., fusion range) are critical First step toward bridging the gap bw CSP and performance analysis of sensor networks 19

20. Future Work Fusion-based coverage for regular deployments Fusion-based coverage for moving targets Deployment algorithms for fusion-based coverage

21. Simulation on Synthetic Data k=2, target position is localized as the geometric center of fusing nodes 21

22. Coverage under Disc Model Deterministic deployment –Optimal topology is hexagon Random deployment –Sensors deployed by a Poisson point process of density ρ –The coverage (fraction of points covered by at least one sensor): deterministic deployment random deployment 22 [ Liu 2004 ]

23. Contributions Introduce probabilistic and collaborative sensing models in the analysis of coverage –Data fusion: sensors combine data for better inferences Derive scaling laws of network density for full coverage Compare the performance of disc and fusion models –Data fusion can significantly improve coverage! 23