Fault-tolerant Relay Node Placement in
Heterogeneous Wireless Sensor Networks
Xiaofeng Han, Xiang Cao, Errol L. Lloyd, and
Chien-Chung Shen [Delaware University]
INFOCOM 2007
전산학과 강유화
2008. 12. 11.
KAIST
Contents
Introduction
Network Model and Preliminaries
One-way Partial Fault-tolerance Relay Node Placement
Two-way Partial Fault-tolerance Relay Node Placement
One-way and Two-way Full Fault-tolerance Relay Node
Placement
Heuristic Implementations
Experimental Results
Conclusion
Discussion
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Introduction
Fault tolerance in wireless sensor networks
Energy depletion
Harsh environment
Malicious attacks
One approach to achieve fault tolerance in WSN
to deploy a small number of additional relay nodes to provide k (k ≥ 1)
vertex-disjoint paths between every pair of functioning devices
Relay node placement
full fault-tolerance relay node placement (FFRP)
partial fault-tolerance relay node placement (PFRP)
relay node placement has also been studied in two-tiered homogeneous
wireless sensor networks
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Problem definition
Heterogeneous wireless sensor networks (H-WSNs)
The different transmission radii of target nodes introduce asymmetric
communication links
One-way / Two-way partial fault-tolerance relay node placement (Oneway / Two-way PFRP).
One-way / Two-way full fault-tolerance relay node placement (One-way /
Two-way FFRP).
Contribution
O(σk2) approximation algorithms for both One-way PFRP and Two-way
PFRP;
O(σk3)approximation algorithms for both One-way FFRP and Two way
FFRP
evaluate heuristic implementations of the proposed algorithms
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Proposed two-tier (cluster-based) architecture
“Fault-Tolerant Clustering of Wireless Sensor Networks”
G.Gupta and Mohamed Yunis, 2003 IEEE proceedings of WCNC
Relay node
Sensor node
Network Model and Preliminaries
Terms, Symbols and their Semantics
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Network Model and Preliminaries
One-way Steinerization. (Equation 1)
Two-way Steinerization: (Equation 2)
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Network Model and Preliminaries
Minimum k-Vertex Connected Spanning Graph
An important problem related to the relay node placement is to find a
minimum k-vertex connected spanning graph (MKCSG).
MKCSG problem is to compute a k-vertex connected spanning
graph
For undirected graphs, when k ≥ 2, this problem is NP-hard,
For directed graphs, this problem is Np hard when k ≥ 1.
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One-way PFRP
One-way Partial k-Vertex Connected Graph
O(σk2)-approximation algorithm.
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One-way PFRP
Definition 3.8: [Relay Component][5]

We start at r (relay node), travel along each edge incident to r in G

If we meet a relay node, we repeat the process; if we meet a target node,
we stop.

all of the nodes (target or relay) and edges visited in this recursive process
form a relay component of r.
Definition 3.3: [Super Path][4]

G = (V ∪ R, E) be a one-way partial k-vertex connected graph for V

a one-way path PG(u, v) in G is a super path, if u and v are target nodes
→
and every interior node (if any) of PG(u, v) is a relay node
→
Every relay node placed by Algorithm 1 is on exactly one super
path
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Two-way PFRP
Two-way partial k-vertex connected graph
O(σk2)-approximation algorithm.
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One-way and Two-way PFRP
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One-way and Two-way PFRP
Algorithm 3 for One-way (Two-way) FFRP
→

In Step 6, Algorithm 3 connects the relay nodes on PG(u, v) with u and v
as well as their in-neighbors and out-neighbors [full-connection]

In Step 7, Algorithm 3 tests each cluster of relay nodes deployed in Step 6,
and removes the cluster if the graph of the resulting network remains a
directed k-vertex connected graph.

O(σk3)-approximation algorithm.
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Heuristic Implementations
Most existing approximation algorithms for MKCSG problem
quite complicated and difficult to implement in computation- and
communication constrained sensor networks
Motivated by the heuristic algorithms, greedy heuristic algorithm
as a practical alternative for the MKCSG problem
repeatedly add the edges that can best help to improve the graph
connectivity until the graph becomes k-vertex connected.
Definition 7.1: [Contribution]: In a graph that is not k-vertex connected,
the connectivity between node pairs is lower than k. unsaturated node
→ (or uv) is defined as the number of
pairs. The contribution of an edge uv
unsaturated node pairs whose connectivity can be improved by the
→
deployment of edge uv
(or uv) in this graph.
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Heuristic Implementations
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Experimental Results
Performance of our algorithms using heuristic implementations
Qualnet 3.8[9] as the simulation platform.
Randomly place target nodes in a 1000m × 1000m 2D terrain
To model a H-WSN, we set T (min) = 200m and T (max) =
500m, and let every target node use a random transmission radius
between T (min) and T (max) in each simulation.
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Experimental Results
The number of relay nodes first increases, then decreases
One-way FFRP algorithm requires 5.9 times and 10.2 times more relay nodes t
han the One-way PFRP algorithm
Two-way FFRP algorithm requires 4.6 times and 7.7 times more relay nodes t
han the Two-way PFRP algorithm
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Experimental Results
The case of k = 4, N (target node) stands for the network size
In a sparse network with 20 target nodes, when T(relay) increases from T (mi
n) to T (max), and the number of relay nodes computed by the One-way and t
he Two-way PFRP algorithms drops 36% and 42%
In a dense network with 60 target nodes, the performance of the One-way and
the Two-way PFRP algorithms remain stable.
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Conclusions
Addresses the problem of deploying a minimum number of relay
nodes to achieve diverse levels of fault-tolerance in the context
of heterogeneous wireless sensor networks
Develop approximation algorithms
O(σk2) approximation algorithms for one-way and two way partial faulttolerance relay node placement
O(σk3)- approximation algorithms for one-way and two-way full fault
tolerance relay node placement.
To support real applications,
provide heuristic implementations of these algorithms
performance of the proposed algorithms is much better than the
performance ratios derived
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Discussion
Energy efficiency?
One relay node failure?

Survivability: ensure alternate path exists for sensor nodes when one of
the relay nodes fail
Convergence time in simulation result
Locally optimal relay node placement in heterogeneous wireless
sensor networks

GLOBECOM 2005

Quanhong Wang Kenan Xu Takahara, G. Hassanein, H.
Dept. of Electr. & Comput. Eng., Queen's Univ., Canada
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References
[4] E. L. Lloyd and G. Xue, “Relay node placement in wireless sensor networks,”
IEEE Transactions on Computers, vol. 56, pp. 134–138, 2007.
[5] J. L. Bredin, E. D. Demaine, M. Hajiaghayi, and D. Rus, “Deploying sensor
networks with guaranteed capacity and fault tolerance,” in ACM MobiHoc,
2005
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INFOCOM 2007
Session 47: Sensor Networks IV
1.
2.
3.
4.
Fault-Tolerant Relay Node Placement in Wireless Sensor Networks:
Problems and Algorithms Weiyi Zhang, Guoliang Xue, Satyajayant
Misra (Arizona State University, US)
Data Persistence in Large-scale Sensor Networks with Decentralized
Fountain Codes Yunfeng Lin, Ben Liang, Baochun Li (University of
Toronto, CA)
Fault-tolerant Relay Node Placement in Heterogeneous Wireless Sensor
Networks Xiaofeng Han, Xiang Cao, Errol Lloyd, Chien-Chung Shen
(University of Delaware, US)
Optimal Policies for Distributed Data Aggregation in Wireless Sensor
Networks Zhenzhen Ye, Alhussein Abouzeid, Jing Ai (Rensselaer
Polytechnic Institute, US)