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Application of neural networks for decision making and evaluation of trust in ad-hoc networks

机译：神经网络在自组织网络中决策和信任评估中的应用

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摘要

In this paper, we demonstrate that neural networks (NNs) are capable of trust estimation and evaluation in ad-hoc networks. The concept of trust in distributed systems arose from the notion of social trust. By the trust problem, we understand the problem of measuring the confidence in the fact that individual nodes behave correctly. We model trust in ad-hoc networks using the packet delivery ratio (PDR) metric. We have developed a method to apply NNs for solving the trust problem in ad-hoc networks. We have conducted a series of simulation experiments and measured the quality of our new method. The results show in average 98% accuracy of the classification and 94% of the regression problem. An important contribution of our research is a verification of the hypothesis that synthetic generation of ad-hoc network traffic in a simulator is sufficient for training of a NN that is then capable to accurately estimate trust in an ad-hoc network.
机译：在本文中，我们证明了神经网络（NN）能够在自组织网络中进行信任估计和评估。分布式系统中的信任概念源于社会信任的概念。通过信任问题，我们理解了衡量各个节点正确运行这一事实的置信度的问题。我们使用数据包传输率（PDR）指标对自组织网络中的信任进行建模。我们已经开发了一种方法来应用NN解决ad-hoc网络中的信任问题。我们进行了一系列的模拟实验，并测量了我们新方法的质量。结果表明，分类的平均准确性为98 \％，回归问题的平均准确性为94 \％。我们的研究的重要贡献是对以下假设的验证：在模拟器中合成生成的ad-hoc网络流量足以训练NN，从而能够准确地估计ad-hoc网络中的信任度。

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