Rijal Abdullah, Noorulsadiqin Azbiya Yaacob, Anas A. Salameh, Nur Amalina Mohamad Zaki, Nur Fadhilah Bahardin
Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multi-access MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scal-ability. The results demonstrate that the DRL-based approach effectively balances the trade-offs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks. © 2024 by the authors of this article. Published under CC-BY.
Faculty of Engineering, Universitas Negeri Padang, Padang, Indonesia; School of Technology Management and Logistics, Universiti Utara Malaysia, Kedah, Malaysia; Department of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia; Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, Terengganu, Malaysia; Department of Built Environment and Technology, College of Built Environment, UiTM Perak Branch, Perak, Malaysia
