Optimising the Cuckoo Search Algorithm for Improved Quality of Service in Cognitive Radio ad hoc Networks

Abstract

This study proposes an optimised routing scheme, called OCS-AODV, for Cognitive Radio Ad Hoc Networks (CRAHNs) to enhance Quality of Service (QoS). The scheme applies the Cuckoo Search (CS) algorithm optimised with a fitness function to improve the performance of the Ad Hoc On-Demand Distance Vector (AODV). The objective of the study is to evaluate the proposed scheme's performance with respect to delay, packet loss, packet delivery ratio and throughput. The literature review shows that the existing routing protocols have limitations which impact performance in dynamic environments. The proposed OCS-AODV scheme aims to address these limitations by selecting reliable paths based on a fitness function that considers the lifetime of nodes, reliability, and available buffer capacity. The simulation results have shown that the OCS-AODV scheme outperforms the CS-DSDV and ACO-AODV schemes in terms of PDR, packet loss, delay, and throughput. The study concludes that the proposed scheme improves the QoS of routing in CRAHNs. However, the use of a single fitness function may not be optimal for all network scenarios. Multiple fitness functions may be considered in future and the schemes be evaluated in real-world CRAHNs