International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Energy-Efficient Offloading with Distributed Reinforcement Learning for Edge Computing in Home Networks

  • Ducsun Lim (Department of Computer Software, Hanyang University) ;
  • Dongkyun Lim (Department of Computer Science Engineering, Hanyang Cyber University)
  • Received : 2024.09.05
  • Accepted : 2024.09.15
  • Published : 2024.11.30
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Abstract

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.

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Acknowledgement

This work was partially supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)) and the research fund of Hanyang University (HY-2024)

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