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Analysing the development of cooperation in MANETs using evolutionary game theory

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Abstract

A wireless mobile ad hoc network is an autonomous system of mobile nodes, which have to cooperate in packet relaying in order to provide the necessary network functionality. Packet relaying based on reciprocity generates strong cooperation incentives even among purely self-regarding nodes. The necessary distinction between selfish and cooperative participants is provided to a node by its trust system. In this article we analyse the particular case where a reputation system is not present in the network, i.e. nodes use solely their local trust systems. In such a case, whether the cooperation is based on direct or on indirect reciprocity, depends on the information type used by trust systems of the network participants. The influence of two factors on the development of cooperation—the network size and the type of participating nodes—is analysed using evolutionary game theory. Computational experiments demonstrate that, in a small network or in a network with many non-cooperative nodes, the cooperation is more likely to be developed on the basis of both reciprocity mechanisms. On the other hand, a large network and the existence of many unconditionally cooperative nodes favour the development of cooperation on the basis of direct reciprocity.

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Acknowledgement

This work has been partially founded by C08/IS/21 TITAN Project (CORE programme) financed by National Research Fund of Luxembourg. A preliminary version of this article appeared in [12].

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Authors and Affiliations

  1. Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 6, rue Coudenhove Kalergi, 1359, Luxembourg, Luxembourg

    Marcin Seredynski

  2. Faculty of Sciences, Technology and Communication, University of Luxembourg, Luxembourg, Luxembourg

    Pascal Bouvry

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  1. Marcin Seredynski
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  2. Pascal Bouvry
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Correspondence to Marcin Seredynski.

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Seredynski, M., Bouvry, P. Analysing the development of cooperation in MANETs using evolutionary game theory. J Supercomput 63, 854–870 (2013). https://doi.org/10.1007/s11227-012-0769-7

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  • DOI: https://doi.org/10.1007/s11227-012-0769-7

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