hole detection and boundary recognition in wireless sensor networks kun-ying hsieh ( 謝坤穎 )...
DESCRIPTION
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.TRANSCRIPT
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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
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Outline
• Introduction• Related Works• Assumption• Distributed Boundary Recognition Algorithm• Simulation and Performance Analysis• Conclusion
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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.
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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.
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Problem
• Discovering the nodes on the boundaries which may be inner that encircles the holes and outer that surrounds the network boundaries.
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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.
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Related Works
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Related Works
• Determine a shortest cycle, R, enclosing the composite hole; R serves as a coarse inner boundary
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Related Works
• Flood the network from the cycle R. Each node in the network records its minimum hop count to R.
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Related Works
• Detect “extremal nodes” whose hop counts to R are locally maximal
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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.
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Related Works
• Higher packet control overheard– Collect information form neighboring nodes
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Assumption
• Sensor node has a unique ID• Without having location information• Communication graph is a unit disk graph.
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Distributed Boundary Recognition Algorithm
• Closure nodes selection• Coarse boundary cycles identification• Discover exact boundary nodes
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CLOSURE NODES SELECTIONDistributed Boundary Recognition Algorithm
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Closure nodes selection
rn
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Closure nodes selection
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.
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Closure nodes selection
Normal node
Landmark node
Closure node
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COARSE BOUNDARY CYCLES IDENTIFICATION
Distributed Boundary Recognition Algorithm
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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).
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Coarse boundary cycles identification
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.
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Coarse boundary cycles identification
Normal node
Landmark node
Closure node
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Coarse boundary cycles identification
•
A B C D E
KJIH
GF
Landmark node
Closure node
CBC_1
CBC_2
CBC_create
CBC_create_reply
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Coarse boundary cycles identification
Normal node
Landmark node
Closure node
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DISCOVER EXACT BOUNDARY NODES
Distributed Boundary Recognition Algorithm
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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
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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
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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
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Discover exact boundary nodes
A
B
C
maximum hop counts
←Boundary node x
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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
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Discover exact boundary nodes
C
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Discover exact boundary nodes
• 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.
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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
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Simulation and Performance Analysis
• Effect of node degree on percentage of accuracy ratio
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Simulation and Performance Analysis
• Effect of number of holes on control packet overhead
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Simulation and Performance Analysis
• Effect of number of holes on simulation time
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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.