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On the critical phase transition time of wireless multi-hop networks with random failures
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International Conference on Mobile Computing and Networking archive
Proceedings of the 14th ACM international conference on Mobile computing and networking table of contents
San Francisco, California, USA
SESSION: Algorithms and modeling table of contents
Pages 175-186  
Year of Publication: 2008
ISBN:978-1-60558-096-8
Authors
Fei Xing  North Carolina State University, Raleigh, NC, USA
Wenye Wang  North Carolina State University, Raleigh, NC, USA
Sponsors
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper, we study the critical phase transition time of large-scale wireless multi-hop networks when the network topology experiences a partition due to increasing random node failures. We first define two new metrics, namely the last connection time and first partition time. The former is the last time that the network keeps a majority of surviving nodes connected in a single giant component; while the latter is the first time that the remaining surviving nodes are partitioned into multiple small components. Then we analyze the devolution process in a geometric random graph of n nodes based on percolation-theory connectivity and obtain the sufficient condition under which the graph is percolated. Based on the percolation condition, the last connection time and first partition time are found to be on the same order. Particularly, when the survival function of node lifetime is exponential, they are on the order of log(log n); while if the survival function is Pareto, the order is (log n)1/ρ, where ρ is the shape parameter of Pareto distribution. Finally, simulation results confirm that the last connection time and first partition time serve as the lower and upper bounds of the critical phase transition time, respectively. Further, an interesting result is that the network with heavy-tailed survival functions is no more resilient to random failures than the network with light-tailed ones, in terms of critical phase transition time, if the expected node lifetimes are identical.


REFERENCES

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1
P. Gupta and P.R. Kumar. Critical Power for Asymptotic Connectivity in Wireless Networks. In W.M. McEneaney et al, editors, Stochastic Analysis, Control, Optimization and Applications, pages 547--566. 1998.
2
 
3
4
 
5
R. Meester and R. Roy. Continuum Percolation. Cambridge University Press, 1996.
 
6
G. Grimmett. Percolation. Springer, 2 edition, 1999.
 
7
M. Penrose. Random Geometric Graphs. Oxford University Press, 2003.
 
8
B. Bollobas and O. Riordan. Percolation. Cambridge University Press, 2006.
 
9
R. Zheng. Information Dissemination in Power-constrained Wireless Networks. In Proc. of IEEE Infocom '06, Apr. 2006.
10
 
11
 
12
P. Gupta and P.R. Kumar. The Capacity of Wireless Networks. IEEE Transactions on Information Theory, 46(2):388--404, 2000.
 
13
O. Dousse, M. Franceschetti, N. Macris, R. Meester, and P. Thiran. Percolation in the Signal to Interference Ratio Graph. Journal of Applied Probability, 43(2):552--562, 2006.
 
14
O. Dousse and P. Thiran. Connectivity vs Capacity in Dense Ad Hoc Networks. In Proc. of IEEE Infocom '04, Mar. 2004.
15
16
 
17
 
18
P.N. Balister, B. Bollobas, A. Sarkar, and M. Walters. Connectivity of Random K-nearest-neighbour Graphs. Advances in Applied Probability, 37(1):1--24, 2005.
 
19
 
20
 
21
 
22
J. Dall and M. Christensen. Random Geometric Graphs. Physical Review E, 66(1):016121, Jul. 2002.
 
23
 
24
B. Liang and Z.J. Haas. Predictive Distance-Based Mobility Management for PCS Networks. In Proc. of IEEE Infocom '99, Apr. 1999.
 
25
P. Nain, D. Towsley, B. Liu, and Z. Liu. Properties of Random Direction Models. In Proc. of IEEE Infocom '05, Mar. 2005.
 
26
Y.S. Chow and H. Teicher. Probability Theory: Independence, Interchangeability, Martingales. Springer, 3 edition, 1997.