Performance Enhancement of Cross-Layer Cognitive Media Access Control Protocol for Wireless Sensing Networks Using Hybrid Intelligent Optimization Approach

Abstract

In recent times, wireless sensing networks (WSNs) application usage is increased in a great way. Access to the communication channel is handled by the Medium Access Control (MAC) layer. At the MAC, a control channel is utilized to determine collision-free data transport pathways. As a result, with cognitive radio (CR) technology, the control channel architecture is critical to obtaining mandatory quality of Service (QoS). However, latency, network communication delay, and energy consumption are the main problem. In this article, a novel African Buffalo allied Jellfish Optimization (AFJO) is proposed for clustering and optimal Cluster Head (CH) selection. The hybrid intelligence method uses a unique probabilistic assessment rule purpose as a fitness role to find the best data transmission path while avoiding congestion, which is named as Fuzzy Interfaced Red Deer (FIRD). The proposed protocol’s performance is evaluated using Network Simulator (NS2), which takes into account parameters such as energy consumption, computational complexity, and Quality of Service (QoS) performance with radio frequency integrated parameters. The suggested technique decreases energy consumption, end-to-end latency, communication overhead, and maximizes network throughput when compared to state-of-the-art cross layer cognitive mac protocol for WSNs system approaches.