دانشگاه مازندران

 آمار

تعداد نشریات31
تعداد شماره‌ها487
تعداد مقالات4,758
تعداد مشاهده مقاله7,388,553
تعداد دریافت فایل اصل مقاله5,517,088
Mohammadi Ghaleh, Armin, Tabatabaei, Sayed Gholam Hassan. (1404). MDEU-A2C: A Mobility, Deadline, Energy and Utilization Aware Multi-Agent A2C Scheduling Approach to Support Fog and Edge Computing in IoT Applications. پژوهشنامه مدیریت اجرایی, 1(1), 42-56. doi: 10.22080/frai.2025.29341.1018
Armin Mohammadi Ghaleh; Sayed Gholam Hassan Tabatabaei. "MDEU-A2C: A Mobility, Deadline, Energy and Utilization Aware Multi-Agent A2C Scheduling Approach to Support Fog and Edge Computing in IoT Applications". پژوهشنامه مدیریت اجرایی, 1, 1, 1404, 42-56. doi: 10.22080/frai.2025.29341.1018
Mohammadi Ghaleh, Armin, Tabatabaei, Sayed Gholam Hassan. (1404). 'MDEU-A2C: A Mobility, Deadline, Energy and Utilization Aware Multi-Agent A2C Scheduling Approach to Support Fog and Edge Computing in IoT Applications', پژوهشنامه مدیریت اجرایی, 1(1), pp. 42-56. doi: 10.22080/frai.2025.29341.1018
Mohammadi Ghaleh, Armin, Tabatabaei, Sayed Gholam Hassan. MDEU-A2C: A Mobility, Deadline, Energy and Utilization Aware Multi-Agent A2C Scheduling Approach to Support Fog and Edge Computing in IoT Applications. پژوهشنامه مدیریت اجرایی, 1404; 1(1): 42-56. doi: 10.22080/frai.2025.29341.1018

MDEU-A2C: A Mobility, Deadline, Energy and Utilization Aware Multi-Agent A2C Scheduling Approach to Support Fog and Edge Computing in IoT Applications

Future Research on AI and IoT
مقاله 6، دوره 1، شماره 1، شهریور 2025، صفحه 42-56    اصل مقاله (1.46 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22080/frai.2025.29341.1018
نویسندگان
Armin Mohammadi Ghaleh1؛ Sayed Gholam Hassan Tabatabaei* 2
1K. N. Toosi University of Technology, Tehran, Iran
2Department of Electrical and Computer Engineering, Malek-e-Ashtar University of Technology, Tehran, Iran
تاریخ دریافت: 05 خرداد 1404،  تاریخ بازنگری: 12 خرداد 1404،  تاریخ پذیرش: 12 خرداد 1404 
چکیده
Mobile Edge Computing reduces latency and response time by bringing computational resources closer to end-user. However, user mobility poses a significant challenge, as users continuously move between coverage areas of different edge nodes with limited range. This dynamic environment demands efficient scheduling mechanisms that can adapt to user movement while meeting application deadlines and optimizing edge resource utilization. This paper proposes an approach for scheduling based on Deep Reinforcement Learning, specifically using an Advantage Actor-Critic architecture within a Fog and Edge computing framework for IoT applications. The method enables distributed decision-making by deploying actor agents at edge nodes and a centralized critic at the fog node, facilitating continuous adaptation through system-wide feedback. User mobility is addressed using location prediction via RNN models embedded at each edge node, allowing proactive and informed offloading decisions. Experimental results demonstrate the proposed approach significantly improves task completion rate by 50%, failure rate by 26%, and response latency by 60%, while also adapting well to dynamic environments, outperforming state-of-the-art methods in real-world-inspired scenarios.
کلیدواژه‌ها
Mobile Edge Computing (MEC)؛ Fog and Edge Computing (FEC)؛ Multi-Agent Reinforcement Learning؛ Advantage Actor-Critic (A2C)؛ Decentralized Scheduling
آمار
تعداد مشاهده مقاله: 1
تعداد دریافت فایل اصل مقاله: 4


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب