Hole Detection and Boundary Recognition in Wireless Sensor Networks Kun-Ying Hsieh ( 謝坤穎 ) Dept. of Computer Science and Information Engineering National

Hole Detection and BoundaryRecognition in

Wireless Sensor NetworksKun-Ying Hsieh (謝坤穎 )Dept. of Computer Science

and Information EngineeringNational Central University

Jang-Ping Sheu (許健平 )Dept. of Computer Science

National Tsing Hua University

IEEE PIMRC 2009

Outline

• Introduction• Related Works• Assumption• Distributed Boundary Recognition Algorithm• Simulation and Performance Analysis• Conclusion

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.

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.

Problem

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

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.

Related Works

Related Works

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

Related Works

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

Related Works

• Detect “extremal nodes” whose hop counts to R are locally maximal

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.

Related Works

• Higher packet control overheard– Collect information form neighboring nodes

Assumption

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

Distributed Boundary Recognition Algorithm

• Closure nodes selection• Coarse boundary cycles identification• Discover exact boundary nodes

CLOSURE NODES SELECTIONDistributed Boundary Recognition Algorithm

Closure nodes selection

rn


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.


Normal node

Landmark node

Closure node

COARSE BOUNDARY CYCLES IDENTIFICATION


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).


Normal node

Landmark node

Closure nodeWill 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.


Normal node

Landmark node

Closure node


•

A B C D E

KJIH

GF

Landmark node

Closure node

CBC_1

CBC_2

CBC_create

CBC_create_reply


Normal node

Landmark node

Closure node

DISCOVER EXACT BOUNDARY NODES


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


• 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


• 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


A

B

C

maximum hop counts

←Boundary node x


• 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


C


• 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.

Simulation and Performance Analysis

Simulation parameters Initial valuesimplemented Ns-2 with the latest version 2.33

Number of nodes 3500

Shape of sensing filed Square

Size of sensing field 500m × 500m

Communication range 13m, 15m, 17m, 20m

Node degree 7, 10, 13, 16

Shape of holes Circle

Number of holes 1, 2, 3, 4, 5, 6, 7, 8

r value 6

n value 1


• Effect of node degree on percentage of accuracy ratio


• Effect of number of holes on control packet overhead


• Effect of number of holes on simulation time

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.

Documents

Hole Detection and Boundary Recognition in Wireless Sensor Networks Kun-Ying Hsieh ( 謝坤穎 ) Dept. of Computer Science and Information Engineering National