Elephant herd clustering with adaptive optimization for secure routing protocol in IoT network

The rapid proliferation of the Internet of Things (IoT) has led to the integration of numerous interconnected devices, presenting opportunities and challenges in network management and security. This study proposes a novel protocol integrating Elephant Herding Optimization and Adaptive Humpback Whale Optimization with fuzzy logic to balance the trade-off between energy efficiency and security in IoT networks. The protocol achieves secure data dissemination through lightweight authentication mechanisms while minimizing energy consumption via optimized cluster formation and routing in Wireless Sensor Networks (WSNs) a critical component in IoT networks. The EHC-FD approach leverages elephant herd optimization and fuzzy logic for optimal cluster formation and routing, ensuring efficient data aggregation and resource allocation. In addition, a trust-based authentication protocol is integrated to secure data dissemination within the network. The methodology integrates an Adaptive Humpback Whale Optimization Algorithm (AHWOA) for dynamic cluster head selection, enhancing network lifespan and efficiency. The proposed model is evaluated through extensive simulations in NS3, demonstrating significant improvements in performance metrics, including energy utilization, network lifetime, throughput, computational and communication costs, average signing and verification times, packet delivery ratio, and network delay. Compared to existing protocols, EHC-FD consistently outperforms in diverse network scenarios, highlighting its potential as a robust solution for IoT networks.