Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Abstract

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.