1. Hole Detection and Boundary Recognition in Wireless Sensor Networks Kun-Ying Hsieh ( 謝坤穎 ) Dept. of Computer Science and Information Engineering National Central University Jang-Ping Sheu ( 許健平 ) Dept. of Computer Science National Tsing Hua University IEEE PIMRC 2009

2. Outline Introduction Related Works Assumption Distributed Boundary Recognition Algorithm Simulation and Performance Analysis Conclusion

3. Introduction Wireless sensor network (WSN) is composed of several sensor nodes deployed and scattered over a specific monitoring region for collecting sensed data. Most of the applications in WSNs require sufficient sensing coverage and connectivity.

4. Introduction However, holes may exist within the network due to obstacles such as ponds or small hills that cause the network partitioned and uncovered. Moreover, the holes may make the routing failure when a node transmits sensing data back to the sink.

5. Problem Discovering the nodes on the boundaries which may be inner that encircles the holes and outer that surrounds the network boundaries.

6. Related Works Y. Wang, J. Gao, and J. S. B. Mitchell, “Boundary Recognition in Sensor Networks by Topological Methods,” in Proc. of MobiCom, pp.122-133, USA, Sept. 2006. Flood the network from an arbitrary node, r.

7. Related Works

8. Determine a shortest cycle, R, enclosing the composite hole; R serves as a coarse inner boundary

9. Related Works Flood the network from the cycle R. Each node in the network records its minimum hop count to R.

10. Related Works Detect “ extremal nodes ” whose hop counts to R are locally maximal

11. Related Works Refine the coarse inner boundary R to provide tight inner and outer boundaries. These boundaries are in fact cycles of shortest paths connecting adjacent extremal nodes.

12. Related Works Higher packet control overheard – Collect information form neighboring nodes

13. Assumption Sensor node has a unique ID Without having location information Communication graph is a unit disk graph.

14. Distributed Boundary Recognition Algorithm Closure nodes selection Coarse boundary cycles identification Discover exact boundary nodes

15. CLOSURE NODES SELECTION Distributed Boundary Recognition Algorithm

16. Closure nodes selection r n

17. A B D F I E C H Landmark Node (LN) K G J Virtual Hexagonal Landmark (VHL) Construct a Virtual Hexagonal Landmark (VHL) by selecting some specific nodes to be the Landmark Nodes (LNs) within the network.

18. Closure nodes selection Normal node Landmark node Closure node

19. COARSE BOUNDARY CYCLES IDENTIFICATION Distributed Boundary Recognition Algorithm

20. Coarse boundary cycles identification Connect the CNs to form the rough boundaries enclosing the obstacles. These rough boundaries are named as Coarse Boundary Cycles (CBCs) and each of them is assigned a unique ID (i.e. CBC_ID).

21. Coarse boundary cycles identification Normal node Landmark node Closure node Will check whether its ID is larger than other two adjacent CN’s IDs. The CN broadcasts a CBC_create(CN’s ID, CBC’s ID, CBC_list) packet.

22. Coarse boundary cycles identification Normal node Landmark node Closure node

23. Coarse boundary cycles identification ABC DE K JIH GF Landmark node Closure node CBC_1 CBC_2 CBC_create CBC_create_reply

24. Coarse boundary cycles identification Normal node Landmark node Closure node

25. DISCOVER EXACT BOUNDARY NODES Distributed Boundary Recognition Algorithm

26. Discover exact boundary nodes Each CN broadcasts the CN_info packet to inform its adjacent CNs and the node within this broadcasting range A B C

27. Discover exact boundary nodes Each CN’s ring-shaped area must pass through its two adjacent CNs. Similarly, each CN is also passed through by its two adjacent CNs’ ring-shaped areas. A B C

28. Discover exact boundary nodes Additionally, some CNs’ ring-shaped areas are cut off by obstacles; the flooding of packets along these ring-shaped areas must be stopped by the boundaries of obstacles. A B C ←Cut-edge

29. Discover exact boundary nodes A B C maximum hop counts ←Boundary node x

30. Discover exact boundary nodes The best selected new BN is located on the intersection point of this two virtual limit lines as it is very close to the boundary of the obstacle. C A virtual limit lines

31. Discover exact boundary nodes C

32. Each BN to select two BNs on its two-side is that each BN firstly chooses two different adjacent CN on its two-side as reference CNs, separately. The BN x referring to (a) the reference CN A to select node z as the new BN and (b) the reference CN C to select node y as the new BN.

33. Simulation and Performance Analysis Simulation parametersInitial values implementedNs-2 with the latest version 2.33 Number of nodes3500 Shape of sensing filedSquare Size of sensing field500m × 500m Communication range13m, 15m, 17m, 20m Node degree7, 10, 13, 16 Shape of holesCircle Number of holes1, 2, 3, 4, 5, 6, 7, 8 r value6 n value1

34. Simulation and Performance Analysis Effect of node degree on percentage of accuracy ratio

35. Simulation and Performance Analysis Effect of number of holes on control packet overhead

36. Simulation and Performance Analysis Effect of number of holes on simulation time

37. Conclusion Proposed a distributed protocol to find the boundary nodes enclosing the holes and the frontier of the network This paper has less control message overhead and simulation time than previous work when number of holes is larger than 6.