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Abstract

Mobile Edge Computing (MEC) is an inventive paradigm for computing possesses that has the potential to notably diminish latency and energy consumption by transferring computationally demanding jobs to edge clouds near intelligent mobile users. The aim of this investigation is t0 reduce offloading and 1atency between multiple users and edge computing in the context of Internet of Things (IoT) applications in the fifth generation (5G) by utilizing an optimization algorithm called the Bald Eagle Search Optimization Algorithm. Despite the fact that employing a deep learning methods might result in increased time consumption and computational complexity, a system of edge computing enables devices to transfer their demanding jobs to edge servers, thereby decreasing 1atency and conserving energy. The Bald Eagle Algorithm (BES) is an advanced optimization algorithm inspired by eagle hunting strategies and consists of select, search, and swooping stages. To further enhance the BES algorithm, a resource estimation stage is introduced to select the most suitable resources. By transferring the most suitable Internet of Things subtasks to edge servers, the edge system minimizes the anticipated execution time. To attain rapid and nearly optimal IoT device performance, a Bald Eagle Search Optimization Algorithm is suggested on the basis of multiuser offloading. The utilization of edge computing effectively diminishes latency, surmounting the limitations of cloud-based processing. The BES algorithm outperforms other existing methods, such as Deep-Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), in terms of lessening offloading latency. Finally, simulations are carried out to exhibit the attained power efficiency and stability through the abatement of offloading latency.

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