An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the ınternet of thıngs
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The Internet of Things (IoTs) has transformed the digital landscape by interconnecting billions of devices worldwide, paving the way for smart cities, homes, and industries. With the exponential growth of IoT devices and the vast amount of data they generate, concerns have arisen regarding efficient task-offloading strategies. Traditional cloud and edge computing methods, paired with basic Machine Learning (ML) algorithms, face several challenges in this regard. In this paper, we propose a novel approach to task offloading in a Device-to-Device (D2D)-Edge-Cloud computing using the Rainbow Deep Q-Network (DQN), an advanced Deep Reinforcement Learning (DRL) algorithm. This algorithm utilizes advanced neural networks to optimize task offloading in the three-tier framework. It balances the trade-offs among D2D, Device-to-Edge (D2E), and Device/Edge-to-Cloud (D2C/E2C) communications, benefiting both end users and servers. These networks leverage Deep Learning (DL) to discern patterns, evaluate potential offloading decisions, and adapt in real time to dynamic environments. We compared our proposed algorithm against other state-of-the-art methods. Through rigorous simulations, we achieved remarkable improvements across key metrics: an increase in energy efficiency by 29.8%, a 27.5% reduction in latency, and a 43.1% surge in utility. © 2024
Açıklama
Anahtar Kelimeler
Kaynak
Sustainable Computing: Informatics and Systems
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
43
Sayı
Künye
Moghaddasi, K., Rajabi, S., Gharehchopogh, F. S., & Ghaffari, A. (2024). An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of Things. Sustainable Computing: Informatics and Systems, 43, 100992.
