1. Mobility Limited Flip-Based Sensor Networks Deployment Reporter: Po-Chung Shih Computer Science and Information Engineering Department Fu-Jen Catholic University 2015/9/14

2. 2 2 Outline  Introduction  Related work Edmonds-Karp algorithm Assumption  SOLUTION Overview Mobility Model Constructing the Virtual Graph  Performance  Conclusion

3. 3 3 Introduction  It is not practical to manually position sensors in desired locations.  In this paper, we study deployment of sensor networks using mobile sensors.  Our problem is to determine a movement plan for the sensors in order to maximize the sensor network coverage and minimize the number of flips.

4. 4 4 Introduction  A certain number of flip-based sensors are initially deployed in the sensor network that is clustered into multiple regions. (a) movement plan (b) result

5. 5 5 (a) A snapshot of the sensor network and the optimal movement plan. (b) The resulting deployment. Introduction

6. 6 6 Outline  Introduction  Related work Edmonds-Karp algorithm Assumption  SOLUTION Overview Mobility Model Constructing the Virtual Graph  Performance  Conclusion

7. 7 7  Edmonds-Karp algorithm Use BFS to find the augmenting path. The augmenting path is a shortest path from s to t in the residual network. Running Time of Edmonds-Karp algorithm : O(VE 2 ). Given a network of seven nodes, source A, sink G, and capacities as shown below: Related work 7 7

8. 8 8  Assumption All the sensors are mobile. Each sensor knows its position. The sensor network is a square field. It is divided into two-dimensional regions, where each region is a square of size R. { } R, where and are sensing and transmission ranges of the sensors. i.e., R =m*d Sensors can flip only once to a new location. Related work 8 8

9. 9 9 Outline  Introduction  Related work Edmonds-Karp algorithm Assumption  SOLUTION Overview Mobility Model Constructing the Virtual Graph  Performance  Conclusion

10. 10 SOLUTION  Overview Phase 1 ： Each sensor in the network will first determine its position and the region it resides in. Phase 2 ： Sensors then forward their location information to the base-station (region-head). Phase 3 ： The base-station using the region information to determine the movement plan. Phase 4 ： The base-station will then forward the movement plan to corresponding sensors in the network.

11. 11 SOLUTION  Mobility Model First ： The distance to which a sensor can flip is fixed and is equal to F. Second ： Sensors can flip to distances between 0 and F.  Parameters definition F ： The maximum distance a sensor can flip. d ： F is an integral multiple of the basic unit d. ( sensors can flip once to distances d, 2d, 3d,... nd from its current location, where nd = F ). C ： C=n denotes the sensor has n choices for the flip distance ( between d and maximum distance F ).

12. 12 Parameters definition (Cont.) Hole ： Region without any sensor. Source ： Region with at least two sensors. Forwarder ： Region with only one sensor.  EX1 ： F=d, C=1, reachable regions of region 1 are regions 2 and 5.  EX2 ： F=2d, C=1, reachable regions of region 1 are regions 3 and 9.  EX3 ： F=2d, C=n, reachable regions of region 1 are regions 2,3,5, and 9. SOLUTION 12

13. 13 SOLUTION  Constructing the Virtual Graph for the Case R = d, F = d, C = 1

14. 14 SOLUTION  Constructing the Virtual Graph for the Case R = d, F = d, C = 1 Case2 R = d, F = 2d, C = 1 Case3 R = d, F = 2d, C = n

15. 15 SOLUTION 15  Constructing the Virtual Graph for the Case R = 2d, F = d, C = 1 12 34

16. 16 SOLUTION  Theorem 1. Let be the minimum-cost maximum-flow plan in GV. Its corresponding flip plan will maximize coverage and minimize the number of flips.

17. 17 Outline  Introduction  Related work Edmonds-Karp algorithm Assumption  SOLUTION Overview Mobility Model Constructing the Virtual Graph  Performance  Conclusion

18. 18 Performance Qi ： The number of regions with at least one sensor at initial deployment. Qo ： The number of regions with at least one sensor after the movement plan determined by our solution is executed. CI = Qo – Qi (Coverage Improvement) FD=J/Qo – Qi (Denoting J as the optimal number of flips as determined by our solution). Network sizes ： 300*300 units and 150*150 units. The region sizes are R = 10 and R = 20 units. The basic unit of flip distance d = 10 units. C=1 and C=n. The number of sensors deployed is equal to the number of regions. PN = P/Q (Denoting P as the total number of packets (or messages) sent and Q as the number of regions). ： Different distributions in initial deployment.

19. 19 Performance

20. 20 Performance

21. 21 Performance

22. 22 Performance

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24. 24 Performance

25. 25 Outline  Introduction  Related work Edmonds-Karp algorithm Assumption  SOLUTION Overview Mobility Model Constructing the Virtual Graph  Performance  Conclusion

26. 26 Conclusion  We proposed a minimum-cost maximum- flow based solution to optimize coverage and the number of flips.

27. 27 Thanks for your attention