Deep-Reinforcement-Learning-Based Resource Allocation for Content Distribution in Fog Radio Access Networks

VNFs boost data processing efficiency in Mobile Edge Computing (MEC)‐driven Internet of Things (IoT) for healthcare, smart cities, and industrial automation. VNF‐based IoT MEC systems encounter a significant security threat due to unauthorized access, posing risks to data privacy and system integrity. Existing approaches struggle to adapt to dynamic environments and lack tamper‐proof enforcement mechanisms. In this work, we propose a novel system combining Reinforcement Learning (RL) and blockchain technology to revoke unauthorized access in VNF‐based IoT MEC. We introduce the Integrated Action‐selection DRL Algorithm for Unauthorized Access Revocation (IASDRL‐UAR), a novel RL approach that excels in dynamic environments by handling both continuous and discrete actions, enabling real‐time optimization of security risk, execution time, and energy consumption. A behavior control contract (BCC) is proposed and integrated into the RL system, automating behavior checks and enforcement, streamlining security management, and reducing manual intervention. RL feedback plays a pivotal role in steering dynamic security adjustments, gaining valuable perspectives from user behavior via trust scores in the behavior contract. The security features of the proposed method are analyzed. Performance comparisons reveal a substantial improvement, with the proposed system outperforming existing methods by 30% in terms of throughput, 21.7% in system stability, and 26% in access revocation latency. Additionally, the system demonstrates a higher security index, energy efficiency, and scalability.

Section: Performance Analysis and Simulation Resultsmentioning

confidence: 99%

Reinforcement learning based blockchain model for revoking unauthorized access in Virtualized Network Functions‐based Internet of Things Mobile Edge Computing

Kalaivani,

Renugadevi,

Gracewell

et al. 2024

“…66 The policy gradient algorithm has been used in several articles for RA in 5G networks. 63,67,68 The policy gradient algorithm for RA in 5G networks offers a significant benefit by effectively managing intricate and ever-changing surroundings, all while considering rewards over extended periods. The algorithm can learn from past experiences and adapt its actions accordingly to optimize the anticipated long-term benefit.…”

Section: Policy Gradientmentioning

confidence: 99%

“…Policy gradient approaches tend to exhibit enhanced efficacy when dealing with scenarios involving continuous action spaces 66 . The policy gradient algorithm has been used in several articles for RA in 5G networks 63,67,68 . The policy gradient algorithm for RA in 5G networks offers a significant benefit by effectively managing intricate and ever‐changing surroundings, all while considering rewards over extended periods.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

Khani,

Jamali,

Sohrabi

et al. 2023

This paper reviews recent research on resource allocation in 5G cloud‐based radio access networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the potential of DRL for learning complex decision‐making policies without human intervention. The paper first introduces the C‐RAN architecture and resource allocation concepts, followed by an overview of DRL algorithms applied to C‐RAN. It discusses the challenges and potential solutions in applying DRL to C‐RAN resource allocation, including scalability, convergence, and fairness. The review concludes by highlighting open research directions for future investigation. By providing insights into the state‐of‐the‐art techniques for resource allocation in 5G C‐RAN using DRL, this paper emphasizes their potential impact on advancing 5G network technology.

“…This article presents a resource allocation system based on distribution in a layered fog radio access network (FRAN) 32 . With the rapid advancement of wireless communication technology, new multimedia applications are causing mobile Internet traffic to explode, increasing service demands on next‐generation wireless networks.…”

Section: Related Workmentioning

confidence: 99%

An intelligent Hybrid‐Q Learning clustering approach and resource management within heterogeneous cluster networks based on reinforcement learning

Mughal,

He,

Zhu

et al. 2023

Recently, heterogeneous cluster networks (HCNs) have been the subject of significant research. The nature of the next‐generation HCN environment is decentralized and highly dynamic; optimization techniques cannot quite express the dynamic characteristics of node resource utilization and communication of HCN networks. In this article, we present an intelligent Hybrid‐Q Learning approach (Hybrid QL)‐based clustering approach for IoT and WSN. Using the self‐learning abilities of (HCNs), we propose a model for dynamic accessing systems on nodes and agents that identify the best possible paths and communication over heterogeneous cluster networks using reinforcement learning. In addition to reducing energy consumption, it creates efficient and effective resource utilization and node communication performance. Through increased throughput and link management, the HCN aims to reduce energy consumption. The proposed model is compared to existing approaches based on various scenarios. Finally, the results of the evaluation tasks demonstrate high accuracy, low‐level complexity, fast dynamic response times, and scalability for heterogeneous cluster networks. Our model showed exceptional node allocation efficiency for dynamic IOT environments and WSNs.