查看文章

With the proliferation of Internet of Things (IoT) devices and the increasing demand for low-latency data processing, edge computing has emerged as a critical paradigm. However, challenges related to energy efficiency and security in this dynamic and distributed environment persist. This paper proposes a novel Hybrid Election-based Ladybug Beetle Optimization (ELBO-H) method tailored to address these challenges concurrently. By synergizing the principles of Election-based Optimization Algorithm (EBOA) and the Ladybug Beetle Optimization (LBO), our approach aims to enhance energy efficiency and bolster authentication protocols in IoT-enabled edge computing environments. We begin by elucidating the exigencies of energy-efficient operations and trust-based authentication within the burgeoning landscape of IoT and edge computing. The proposed ELBO-H method involves integrating this algorithm into the edge computing architecture, optimizing energy usage while ensuring robust security measures between IoT devices, edge nodes. To evaluate the proposed method, we conducted simulations in a controlled environment, considering various scenarios and workload conditions. Results indicate a significant improvement in energy efficiency without compromising security. Our proposed ELBO-H method demonstrates an average Attack detection rate of 94.28% compared to the IB-SEC, G-BHO, DEEC-KSA, and CPSO methods, which have average Attack detection rates of 78.23%, 72.45%, 74.89%, and 52.67%, respectively.