机会主义网络的统计学习信誉系统

Diogo Soares, Edjair Mota, C. Souza, P. Manzoni, Juan-Carlos Cano, C. Calafate
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引用次数: 7

摘要

接触是保证机会网络性能的必要条件,但由于资源的限制,一些节点可能不合作。在声誉系统中,代理的感知依赖于过去的观察来分类其实际行为。很少有研究调查鲁棒学习模型对机会主义网络中自利节点分类的有效性。本文提出了一种基于博弈论的分布式信誉算法,以实现机会主义网络中信息的可靠传播。一个接触被建模为一个游戏,节点可以合作也可以不合作。通过统计推理方法,我们从过去的观察中学习得出节点的声誉。我们将所提出的算法应用于一组轨迹,以获得在通信中涉及自私节点时未来行动的分布式预测基础。我们评估数据收集的准确性变得可靠的条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A statistical learning reputation system for opportunistic networks
Contacts are essential to guarantee the performance of opportunistic networks, but due to resource constraints, some nodes may not cooperate. In reputation systems, the perception of an agent depends on past observations to classify its actual behavior. Few studies have investigated the effectiveness of robust learning models for classifying selfish nodes in opportunistic networks. In this paper, we propose a distributed reputation algorithm based on the game theory to achieve reliable information dissemination in opportunistic networks. A contact is modeled as a game, and the nodes can cooperate or not. By using statistical inference methods, we derive the reputation of a node based on learning from past observations. We applied the proposed algorithm to a set of traces to obtain a distributed forecasting base for future action when selfish nodes are involved in the communication. We evaluate the conditions in which the accuracy of data collection becomes reliable.
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