An Intelligent Grey Wolf Optimization Framework for Parameter Adaptation in EAOMDV Routing Protocol
TL;DR
The proposed GWO-EAOMDV framework outperforms both traditional EAOMDV and alternative optimization-based routing protocols and reduces end-to-end latency by 15-22% while simultaneously enhancing PDR by 12-18% and improving energy efficiency.
Abstract
In this paper, a parameter adaption method is suggested for MANET routing efficiency enhancement. It combines the EAOMDV protocol with intelligent Grey Wolf Optimization (GWO). Improving the network's routing patterns through the use of several quality-of-service (QoS) indicators is the main goal. Some of these metrics are the rate of packet delivery (PDR), residual energy, end-to-end delay, and routing overhead. By merging normalized performance measures into a single fitness function, the suggested strategy reframes a weighted single-objective problem as route optimization. In order to automatically optimize routing parameters and choose ideal routes, the suggested system uses GWO, which is modeled after the pack structure and hunting methods used by grey wolves. Better fault tolerance and route stability are achieved by the use of EAOMDV, which guarantees the preservation of multiple loop-free pathways. To avoid optimizing faulty or redundant paths, a method called discrete path validation is used. Many simulations have been run with different node densities and mobility parameters. The proposed GWO-EAOMDV framework outperforms both traditional EAOMDV and alternative optimization-based routing protocols. Reduces end-to-end latency by 15-22% while simultaneously enhancing PDR by 12-18% and improving energy efficiency. The robustness of the proposed model was confirmed by statistical validation employing several simulation runs.