Resilient multicast routing in CRNs using a multilayer hyper-graph approach

Abstract

Cognitive Radio Networks (CRNs) have a dynamic nature where channels availability changes over time. In this paper, we introduce a strategy to route multicast sessions in CRNs and to protect them against failures or disappearance of channels. We model the network as a Multilayer Hyper-Graph (MLHG), such that a group of Secondary Users (SUs) which have a common channel are modeled by a hyper-edge. Also, each layer in the MLHG represents a different channel. Primary paths from a source SU to destination SUs are selected by considering channels' switching delay, and transmission delay. To protect the multicast session, we select a backup path for primary path, if feasible, such that the primary and backup paths are Shared Risk Hyper-edge Groups (SRHEGs) disjoint. We develop an Integer Linear programming (ILP) model, in order to find the multicast primary paths and their backup paths, minimize the maximum path delay, and minimize the number of selected channel links. Our simulation results show that when the number of available channels increases, the number of primary and backup paths that can be routed in the CRN increases, and the maximum path delay decreases almost linearly.