Enhancing On-Demand Multicast Routing Protocol (ODMRP) using Mayfly Algorithm and Reference Point Mobility Group Model for Cattle Monitoring

Abstract

Cattle migration presents significant challenges for wireless network routing due to dynamic and unpredictable movement patterns. This study introduces an innovative framework that enhances the On-Demand Multicast Routing Protocol (ODMRP) using the Mayfly Optimization Algorithm (MOA) and the Reference Point Group Mobility Model (RPGM) for cattle monitoring. A simulation environment using Network Simulator 3 (NS-3) was employed to evaluate routing efficiency, energy consumption, and scalability with varying node counts. Various performance metrics such as throughput, energy efficiency, and packet delivery ratio were analysed to compare the effectiveness of the proposed model against traditional random waypoint models. By leveraging RPGM's group-based mobility approach, the framework adapts to cattle movement while optimizing routing efficiency, energy consumption, and scalability. The proposed solution demonstrates superior performance in IoT-enabled livestock monitoring systems, achieving improved throughput, energy efficiency, and reduced latency compared to traditional random waypoint models. The findings confirm superior performance in IoT-enabled livestock monitoring systems, achieving a 35% increase in throughput, a 27% reduction in energy consumption, and a 40% decrease in latency compared to the baseline model. These results establish the enhanced ODMRPMF RPGM framework as a robust, scalable, and reliable solution for dynamic and resource-constrained applications, particularly in real-time livestock tracking.