容迟网络中基于演化博弈的合作行为

针对容迟网络中节点由于资源有限而表现出来的自私特性,为改善网络中节点的合作行为,进而提高网络的整体性能,提出一种基于演化博弈(EGT)的节点合作行为促进机制。首先,采用囚徒困境模型建立节点与其邻居博弈的收益矩阵;其次,基于度中心性定义节点的社会权威性;进一步地,在节点策略更新规则时考虑社会权威的影响,选择当前邻居中社会权威较高的节点进行模仿学习;最后,在机会网络环境仿真器上基于真实的动态网络拓扑数据进行仿真实验。仿真结果表明,与随机选择邻居的费米(Fermi)更新规则相比,考虑社会权威的更新规则能够更好地促进节点合作行为的涌现,进而提升网络的整体性能。

Cooperative behavior based on evolutionary game in delay tolerant networks

Due to the limited resources, nodes in Delay Tolerant Network (DTN) behave selfishly, i.e. nodes refuse to help forward message for others. In order to improve the cooperative behavior of nodes, and enhance the overall network performance, a new incentive mechanism of node behavior based on Evolutionary Game Theory (EGT) was proposed. In the proposed mechanism, the prisoner's dilemma model was employed to establish payoff matrix between the node and its neighbors. Then, based on the degree centricity, social authority of the node was defined. Further, when designing the strategy update rule, the influence of social authority was considered. That is, nodes with higher social authority were selected from the current neighborhood to imitate and learn. Finally, on the basis of real dynamic network topology, the simulation experiments were conducted by the Opportunistic Network Environment (ONE) simulator. The simulation results show that, compared with the Fermi update rule which chooses neighbors randomly, the strategy update rule which considers the social authority can promote the cooperative behavior, accordingly, improve the overall performance of the network.