PFCS: An efficient Path setup and Fast Channel Switching protocol for cognitive mobile IoTs

Abstract

Cognitive Radio Networks (CRNs) have become a prominent platform in recent years, particularly in the context of the Internet of Things (IoT) and Industry 5.0. CRNs include Primary Users (PUs) and Secondary Users (SUs). PUs are licensed users with priority over SUs for spectrum utilization. The growth rate of wireless devices is 40% annually. The available wireless spectrum is not quite enough to handle this immense growth rate. These facts makes channel assignment in CRNs as one of the highly explored research areas. In this study, we propose a cross-layer efficient routing protocol and channel selection algorithm for IoT named, an efficeint Path setup and Fast Channel Switching (PFCS) for IoT-based Mobile Cognitive Radio Ad Hoc Networks (CRAHNs). PFCS evaluates multiple paths using various important network parameters and selects the path with minimal end-to-end channel switching and the highest score. Additionally, PFCS proposes a local channel recovery algorithm to handle PU activity through controlled broadcast. The recovery algorithm aims to resume ongoing communication with minimal control overhead. The proposed PFCS is compared with state-of-the-art, Software-Defined Routing Protocol (SDRP) and Optimal Channel Selection algorithm for CRahNs (OCSCRN) with varying numbers of PUs and SUs in Mobile Cognitive Radio Networks (MCRNs). Results demonstrate that PFCS outperforms SDRP and OCSCRN in terms of Delay, Packet Delivery Function (PDF), Routing overhead, and Network life.