1. 1 Probabilistic Coverage in Wireless Sensor Networks Nadeem Ahmed, Salil S. Kanhere and Sanjay Jha Computer Science and Engineering, University of New South Wales, Sydney IEEE LCN 2005

2. 2 Outline Introduction Related work Probabilistic Coverage Algorithm (PCA) SimulationConclusion

3. 3 Introduction --- background Binary detection model –The sensing coverage of a sensor node is usually assumed uniform in all directions Detection probability: 1 Detection probability: 0

4. 4 Introduction --- background Probabilistic coverage model –Signal propagation from a target to a sensor node follows a probabilistic model –Ex: acoustic, seismic Signal strength decays with the distance from source

5. 5 Introduction --- motivation and goal Motivation –Target application that require a certain degree of confidence in the detection probability –Ex: Object tracking and intrusion detection Goal –Check whether the currently deployed topology supports the required coverage probability or not

6. 6 Related work The Coverage Problem in a Wireless Sensor Network

7. 7 Related work

8. 8 Suppose that no two sensors are located in the same location. The whole network area A is k-covered iff each sensor in the network is k-perimeter-covered 2 1 2 1 2 1 2 1 2 1 K-perimeter-cover (K=1) k-covered (K=1)

9. 9 PCA --- overview 0 2π d eval Probability at d eval Required probability

10. 10 PCA --- Technical Preliminaries

11. 11 PCA --- Technical Preliminaries Effective coverage range, R effec –Distance of the target from the sensor beyond which the detection probability is negligible C(3,0.997) C(6,0.90) C(9,0.655) C(12,0.41) C(15,0.245) C(18,0.135) C(20,0.1) R effec =20

12. 12 PCA --- Technical Preliminaries Detection probability at a point Midpoint of between the two sensors

13. 13 PCA --- assumptions Sensors are randomly deployed in the field Location information is available to each sensor node Communication range of sensors is at least twice the effective coverage range, R effec Sensors can detect boundary of the region

14. 14 PCA --- the algorithm Ascertain required probability and d eval d eval Covered by required probability Required probability = 0.9 C(9,0.655) C(6,0.90)

15. 15 PCA --- the algorithm A node detects whether it is within vicinity of the region boundary Detection probability : 1 Sufficiently covered

16. 16 PCA --- the algorithm A node calculates neighbors contribution towards detection probability

17. 17 c PCA --- the algorithm The region inside the circle with radius d eval Case 1 Slashed region covers with at least desired detection probability C(6,0.90) C(9,0.655) C(6,0.90) C(9,0.655) a b d Probability of a, b, c, d >required probability sisi sjsj sisi sjsj e f d ij Probability>=required probability d eval

18. 18 PCA --- the algorithm The region inside the circle with radius d eval Case 2 C(6,0.90) C(9,0.655) C(6,0.90) C(9,0.655) a b c d Probability of a, b = 0.88 sisi The probability inside the slashed region increases as we move from segment a-b towards segment c-d d eval sjsj

19. 19 PCA --- the algorithm d eval covered by required probability sufficiently covered with detection probability at least required probability The region inside the circle with radius d eval

20. 20 PCA --- extension Mobile sensors cover holes ρ reqd =1-(1-ρ exist )(1-ρ help ) ρ help =1-(1-ρ reqd )/ (-ρ exist )

21. 21 PCA --- extension Mobile sensors cover holes Check whether the circle with radius c h can completely cover the uncovered segment Yes no

22. 22 Simulation Ns2 simulator Region: 100 x 100 Number of nodes : 60~120 Effective coverage range: 20m Communication range: 40m

23. 23 Simulation C(6,0.90) C(9,0.655) PCA Sensing range=6m

24. 24 Conclusion Conclusion Proposed a probabilistic coverage algorithm to evaluate area coverage Evaluate the maximum supported detection probability for an area

25. 25 Thank you