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SCIENCE CHINA Information Sciences, Volume 60, Issue 11: 110205(2017) https://doi.org/10.1007/s11432-017-9165-7

Cooperation and distributed optimization for the unreliable wireless game with indirect reciprocity

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  • ReceivedApr 21, 2017
  • AcceptedJul 10, 2017
  • PublishedSep 22, 2017

Abstract

Cooperation in packet forwarding among users and operators of a distributed wireless network has been widely studied.However, because of the limited computational resources, users in wireless communication do not prefer to cooperate with others unless cooperation may improve their own performance.Therefore, the key problem in cooperation enforcement must be solved first to enable a wireless network to be efficient.Yet, most of the existing game-theoretic cooperation stimulation approaches assume that the interactions between any pair of players (users) are long-lasting.In this paper, we apply game theory to optimize the communication efficiency of a distributed wireless network with finite number of interactions between any pair of players.Based on the mechanism of indirect reciprocity, we theoretically analyze the optimal action rule with the method of dynamic programming,and derive the approximate threshold of benefit-to-cost ratio to achieve the optimal action rule.Furthermore, we adopt the replicator dynamics to assess the evolutionary stability of the optimal action rule against the perturbation effect.Numerical illustrations verify the performance of the proposed method on wireless cooperation.


Acknowledgment

This work was supported by National Science Fund for Distinguished Young Scholar of China (Grant No. 61425019), Key Projects of National Natural Science Foundation of China (Grant No. 71731004), National Natural Science Foundation of China (Grant Nos. 61403059, 61503342, 11572288, 61672468), and Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LY15F020013, LY16F030002).


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  • Figure 1

    (Color online) The illustration of the packet forwarding on distributed wireless network. At each time slot, the packets of the provider will be forwarded or dropped by the relay to the receiver depending on the provider’s reputation under channel loss probability $p_{e}$. Afterwards, reputation of the relay will get updated with the observed receiver signal under reputation updating error $\mu$. Then, the relay’s reputation is spread through a channel of noisy gossip from the receiver and the observers to the whole network. After the interaction, each participant returns to the network with probability $\omega$, or leaves the network with probability $1-\omega$ without return.

  • Figure 2

    The illustration of dynamic programming.

  • Figure 3

    The illustration of expected payoffs' calculation to the actions $FF$.

  • Figure 4

    (Color online) The evolutionary stability of the action $FD$ with $b=2.4$ and $c=1$. (a) The initial frequency of the action $FD$ is setting to $0.6$; (b) the initial frequency of the action $FD$ is setting to $0.35$.

  • Figure 5

    (Color online) The evolutionary stability of the action $FD$ with $b=1.6$ and $c=1$. (a) The initial frequency of the action $FD$ is setting to $0.85$; (b) the initial frequency of the action $FD$ is setting to $0.35$.

  • Figure 6

    (Color online) The average node payoffs of the full cooperation action and the optimal action with different channel loss probability when $\mu=0.01$.

  • Figure 7

    (Color online) The normalized data session throughput of the full cooperation action and optimal action with different channel loss probability when $\mu=0.01$.

  • Figure 8

    (Color online) The normalized data session throughput of hop count with different channel loss probability when $\mu=0.01$.

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