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

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.