Fuzzy probabilistic QoS routing for uncertain networks

Fuzzy Computing and Probabilistic Computing are two of the fields that constitute Soft Computing. The integration of both systems can result in powerful tools that enable solutions for difficult computational problems, which involve uncertain complex dynamical systems. QoS routing in large-scale dynamical networks with uncertain parameters is one such a problem. In this paper we discuss the domains, differences, and merits of each paradigm and the importance of their integration for solving problems of complex dynamical systems under uncertainty. For application, we present the problem of QoS routing in large-scale dynamical networks with uncertain parameters. We propose a novel multi-constraint routing algorithm that builds on the network state updating policy presented in [12]. The proposed algorithm utilizes fuzzy modeling to capture the uncertainty in network state information, probabilistic computing to compute routing paths with highest probability to satisfy a QoS communication request, and BK-products of fuzzy relations to aggregate the computation and select the optimal path.