A Hybrid Cross Layer with Fuzzy Chaotic Whale Wolf Optimization-Based Efficient Routing for Wireless Sensor Networks

An efficient clustering and routing of Wireless Sensor Networks (WSNs) is a critical challenge, with a significant impact on Quality of Service (QoS) performance. In this research, a Cross-Layer-based Fuzzy Chaotic Whale Wolf Optimisation (CL-FCWWO) is proposed to improve the performance in WSNs. The K-medoids with Improved Artificial-Bee-Colony (K-IABC)-based energy-efficient clustering and the routing protocol is utilized to address the power asymmetry issue in WSNs. Cross-layer routing is suggested to reduce network transmission latency and power consumption. The MATLAB is used to construct this approach and compared the results to other techniques. The suggested CL-FCWWO outperforms in terms of energy consumption, throughput, and network lifetime. These results are validated by comparisons with existing routing algorithms such as Cross-layer based Opportunistic Routing Protocol (CORP), Cross-Layer Harris-Hawks-Optimization-algorithm (CL-HHO) and Adaptive Sailfish Optimisation (ASFO). From the results, it showed that proposed CL-FCWWO algorithm performed better than CORP, ASFO and CL-HHO methods, showing its effectiveness in improving WSNs performance.