Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization

Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge are timed swap between different states achieve stabilized efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading computational for efficacy. proposed scheme employs state learning swapping intervals scheduling or depending on load. distributed at time of organized first come, serve features. In learning, reward is allocated successful prevent device exhaustion. computation therefore swapped energy-reserved based previous computed reward. cognitive management induces allocation availability convergence. limited in recent works due non-linear missing CEMS addresses this issue through precise earlier exhaustion identification. convergence addressed using rewards assigned post transitions. transition process, multiple levels considered. consideration prevents early detection exhaustive devices, unlike conventional wireless networks. scheme’s performance compared metrics rate time, efficacy, ratio, failures. experimental results show that improves efficacy 7.2% 9.32%, respectively, varying devices. It reduces failures, 14.97%, 7.27%, 14.48%, respectively.