An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the ınternet of thıngs

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dc.authorscopusidAli Ghaffari / 57197223215
dc.authorwosidAli Ghaffari / GVQ-6011-2022
dc.contributor.authorMoghaddasi, Komeil
dc.contributor.authorRajabi, Shakiba
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorGhaffari, Ali
dc.date.accessioned2025-04-17T20:56:01Z
dc.date.available2025-04-17T20:56:01Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe 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
dc.identifier.citationMoghaddasi, 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.
dc.identifier.doi10.1016/j.suscom.2024.100992
dc.identifier.issn22105379
dc.identifier.scopus2-s2.0-85192685663
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6315
dc.identifier.volume43
dc.indekslendigikaynakScopus
dc.institutionauthorGhaffari, Ali
dc.institutionauthoridAli Ghaffari / 0000-0001-5407-8629
dc.language.isoen
dc.publisherElsevier Inc.
dc.relation.ispartofSustainable Computing: Informatics and Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleAn advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the ınternet of thıngs
dc.typeArticle

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