ZONE BASED ROUTING FOR OPTIMAL PATH SELECTION USING LOCUST SWARM OPTIMIZATION IN ENERGY-EFFICIENT IOT-MANETS

The increasing reliance on the Internet of Things (IoT) in Mobile Ad Hoc Networks (MANETs) has necessitated the development of energy-efficient routing strategies to ensure sustainable network operations. MANETs face challenges such as dynamic topology, limited energy resources, and increased latency due to inefficient routing protocols. To address these issues, this work introduces a Zone-Based Routing Protocol (ZBRP) integrated with Locust Swarm Optimization (LSO) for optimal path selection, aiming to enhance energy efficiency in IoT-MANETs. The proposed method divides the network into zones based on node proximity, reducing unnecessary routing overhead. Within each zone, LSO identifies the optimal path by evaluating metrics such as residual energy, hop count, and signal strength, thereby minimizing energy consumption. Simulations conducted using Python on a network of 200 nodes show significant improvements compared to existing protocols. The proposed method achieves an 18.6% reduction in energy consumption, 22.3% improvement in packet delivery ratio (PDR), and 14.5% lower end-to-end delay, making it a robust solution for resource-constrained IoT-MANETs. These results demonstrate the potential of the ZBRP-LSO framework to enable long-lasting and reliable MANETs for IoT applications.