Optimal Cluster Head Selection and Reliable Routing Using Enhanced Butterfly Optimization and Improved Dynamic Source Routing Protocol Over Hybrid Networks

The integration of Internet of Things (IoT), Mobile Ad-hoc Networks (MANETs), and Wireless Sensor Networks (WSNs) under a unified cloud-based system presents unique challenges and opportunities for optimizing network functionality and scalability. This integration aims to leverage the strengths of each technology IoT’s extensive connectivity, MANET’s flexible topology, and WSN’s efficient local data collection and monitoring to create a robust, scalable, and efficient network system capable of dynamic data processing and enhanced decision-making. Still, the existing system has problem with lower packet delivery ratio since inappropriate Cluster Head (CH) node selection process. To address the aforementioned issue, in this work, an innovative approach using Enhanced Butterfly Optimization (EBO) and Improved Dynamic Source Routing (IDSR) protocol is proposed for optimal CH node selection and reliable routing in heterogeneous networks. Initially, the hybrid system model is constructed via sensor nodes, MANET nodes, IoT sensor nodes and cloud server. This methodology leverages the EBO algorithm to strategically select CHs based on a composite metric that considers energy efficiency, delay, throughput and lifetime metrics, thus ensuring sustainable network operation and reduced energy consumption. Concurrently, the IDSR protocol has been tailored to address the dynamic and ad-hoc nature of IoT and MANET communications, incorporating enhanced security measures and trust-based routing to mitigate spoofing attacks and ensure data integrity. Between sensors in the Internet Cloud, MANET is establishing the faster and most reliable path. Extensive simulations demonstrate that our integrated approach significantly outperforms traditional methods in terms of end to end delay, energy consumption, network longevity, data throughput, and security, confirming its effectiveness in optimizing network operations across diverse deployment scenarios.