Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T16:16:34.690Z Has data issue: false hasContentIssue false

Information Transmission under Random Emission Constraints

Published online by Cambridge University Press:  04 September 2014

FRANCIS COMETS
Affiliation:
Laboratoire de Probabilités et Modèles Aléatoires, Université Paris Diderot-Paris 7, Case 7012, 75205 Paris CEDEX 13, France (e-mail: [email protected], http://www.proba.jussieu.fr/~comets)
FRANÇOIS DELARUE
Affiliation:
Laboratoire J.-A. Dieudonné, Université de Nice Sophia Antipolis, Parc Valrose, 06108 Nice CEDEX 02, France (e-mail: [email protected], http://math.unice.fr/~delarue)
RENÉ SCHOTT
Affiliation:
IECL and LORIA, Université de Lorraine, 54506 Vandoeuvre-lès-Nancy, France (e-mail: [email protected], http://www.loria.fr/~schott)

Abstract

We model the transmission of a message on the complete graph with n vertices and limited resources. The vertices of the graph represent servers that may broadcast the message at random. Each server has a random emission capital that decreases at each emission. Quantities of interest are the number of servers that receive the information before the capital of all the informed servers is exhausted and the exhaustion time. We establish limit theorems (law of large numbers, central limit theorem and large deviation principle), as n → ∞, for the proportion of informed vertices before exhaustion and for the total duration. The analysis relies on a construction of the transmission procedure as a dynamical selection of successful nodes in a Galton–Watson tree with respect to the success epochs of the coupon collector problem.

Type
Paper
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Alves, O., Lebensztayn, E., Machado, F. and Martinez, M. (2006) Random walks systems on complete graphs. Bull. Braz. Math. Soc. (NS) 37 571580.Google Scholar
[2]Alves, O., Machado, F. and Popov, S. (2002) Phase transition for the frog model. Electron. J. Probab. 7 #16.Google Scholar
[3]Alves, O., Machado, F. and Popov, S. (2002) The shape theorem for the frog model. Ann. Appl. Probab. 12 533546.Google Scholar
[4]Baccelli, F., Blaszczyszyn, B. and Mirsadeghi, M. (2011) Optimal paths on the space–time SINR random graph, Adv. Appl. Probab. 43 131150.CrossRefGoogle Scholar
[5]Baum, L. and Billingsley, P. (1965) Asymptotic distributions for the coupon collector's problem. Ann. Math. Statist. 36 18351839.CrossRefGoogle Scholar
[6]Billingsley, P. (1968) Convergence of Probability Measures, Wiley.Google Scholar
[7]Boucheron, S., Gamboa, F. and Léonard, C. (2002) Bins and balls: Large deviations of the empirical occupancy process. Ann. Appl. Probab. 12 607636.CrossRefGoogle Scholar
[8]Comets, F., Quastel, J. and Ramírez, A. (2007) Fluctuations of the front in a stochastic combustion model. Ann. Inst. H. Poincaré Probab. Statist. 43 147162.Google Scholar
[9]Comets, F., Quastel, J. and Ramírez, A. (2009) Fluctuations of the front in a one dimensional model of X+Y → 2X. Trans. Amer. Math. Soc. 361 61656189.CrossRefGoogle Scholar
[10]Dacunha-Castelle, D. and Duflo, M. (1983) Probabilités et Statistiques 2: Temps Mobile, Masson.Google Scholar
[11]Dembo, A. and Zeitouni, O. (1998) Large Deviations Techniques and Applications, second edition, Springer.Google Scholar
[12]Ding, L. and Guan, Z.-H. (2008) Modeling wireless sensor networks using random graph theory. Physica A 387 30083016.Google Scholar
[13]Dupuis, P., Nuzman, C. and Whiting, P. (2004) Large deviation asymptotics for occupancy problems. Ann. Probab. 32 27652818.Google Scholar
[14]Duquesne, T. and Le Gall, J.-F. (2002) Random trees, Lévy processes and spatial branching processes. Astérisque 281.Google Scholar
[15]Durrett, R. (1995) Probability: Theory and Examples, second edition, Duxbury.Google Scholar
[16]Erdös, P. and Rényi, A. (1961) On a classical problem of probability theory. Magyar Tud. Akad. Mat. Kutató Int. Közl. 6 215220.Google Scholar
[17]Flajolet, P., Gardy, D. and Thimonier, L. (1992) Birthday paradox, coupon collectors, caching algorithms and self-organizing search. Discrete Appl. Math. 39 207229.CrossRefGoogle Scholar
[18]Jacod, J. and Shiryaev, A. N. (2002) Limit Theorems for Stochastic Processes, second edition, Springer.Google Scholar
[19]Jia, X. (2004) Wireless networks and random geometric graphs. In Proc. 2004 International Symposium on Parallel Architectures, Algorithms and Networks: ISPAN'04, pp. 575–580.Google Scholar
[20]Kan, N. (2002) The martingale approach to the coupon collection problem. Zap. Nauchn. Sem. S.-Peterburg. Otdel. Mat. Inst. Steklov. 294 113126. Translation in J. Math. Sci. 127 (2005) 1737–1744.Google Scholar
[21]Kawahigashi, H., Terashima, Y., Miyauchi, N. and Nakakawaji, T. (2005) Modeling ad hoc sensor networks using random graph theory. In Proc. Second IEEE Consumer Communications and Networking Conference: CCNC 2005.Google Scholar
[22]Kesten, H. and Sidoravicius, V. (2005) The spread of a rumor or infection in a moving population. Ann. Probab. 33 24022462.Google Scholar
[23]Kesten, H. and Sidoravicius, V. (2006) A phase transition in a model for the spread of an infection. Illinois J. Math. 50 547634.CrossRefGoogle Scholar
[24]Kesten, H. and Stigum, B. P. (1966) A limit theorem for multidimensional Galton–Watson processes. Ann. Math. Statist. 37 12111223.CrossRefGoogle Scholar
[25]Kurkova, I., Popov, S. and Vachkovskaia, M. (2004) On infection spreading and competition between independent random walks. Electron. J. Probab. 9 122.Google Scholar
[26]Kurtz, T., Lebensztayn, E., Leichsenring, A. R. and Machado, F. P. (2008) Limit theorems for an epidemic model on the complete graph. ALEA Lat. Am. J. Probab. Math. Stat. 4 4555.Google Scholar
[27]Machado, F., Machurian, H. and Matzinger, H. (2011) CLT for the proportion of infected individuals for an epidemic model on a complete graph. Markov Proc. Rel. Fields 17 209224.Google Scholar
[28]Neveu, J. (1986) Arbres et processus de Galton–Watson. Annales de l'IHP B 22 199207.Google Scholar
[29]Pitman, J. (2006) Combinatorial Stochastic Processes: St. Flour 2002, Vol. 1875 of Lecture Notes in Mathematics, Springer.Google Scholar
[30]Ramírez, A. and Sidoravicius, V. (2004) Asymptotic behavior of a stochastic combustion growth process. J. Eur. Math. Soc. 6 293334.Google Scholar
[31]Sedgewick, R. and Flajolet, P. (1996) An Introduction to the Analysis of Algorithms, Addison-Wesley.Google Scholar
[32]Zhukovskiï, M. E. (2012) The law of large numbers for an epidemic model (Russian). Dokl. Akad. Nauk 442 736739. Translation in Dokl. Math. 85 (2012) 113–116.Google Scholar