Energy-Efficient Offloading with Distributed Reinforcement Learning for Edge Computing in Home Networks

Energy-Efficient Offloading with Distributed Reinforcement Learning for Edge Computing in Home Networks

This paper introduces a decision-making framework for offloading tasks in home network environments, utilizing Distributed Reinforcement Learning (DRL). The proposed scheme optimizes energy efficiency while maintaining system reliability within a lightweight edge computing setup. Effective resource management has become crucial with the increasing prevalence of intelligent devices. Conventional methods, including on-device processing and offloading to edge or cloud systems, need help to balance energy conservation, response time, and dependability. To tackle these issues, we propose a DRL-based scheme that allows flexible and enhanced decision-making regarding offloading. Simulation results demonstrate that the proposed method outperforms the baseline approaches in reducing energy consumption and latency while maintaining a higher success rate. These findings highlight the potential of the proposed scheme for efficient resource management in home networks and broader IoT environments.