Alwakeel Ahmed M, Alnaim Abdulrahman K
Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia.
College of Business Administration, King Faisal University, Al Ahsa 31982, Saudi Arabia.
Sensors (Basel). 2024 Jul 2;24(13):4308. doi: 10.3390/s24134308.
The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with the blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages the blockchain's inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine-learning models like SVM, KNN, and random forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard's effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.
边缘计算和雾计算在现代IT基础设施中的重要性日益凸显，这是由去中心化应用的兴起所推动的。然而，由于设备能力各异以及网络条件动态变化，这些框架内的资源分配具有挑战性。传统方法往往导致资源利用不佳和进展缓慢。本研究提出了一种新颖的策略，通过将机器学习与区块链集成以实现可靠的信任管理，来增强边缘计算和雾计算中的资源分配。我们提出的框架名为CyberGuard，利用区块链固有的不可变特性和去中心化来建立一个可信赖且透明的网络，用于监控和验证边缘计算和雾计算交易。CyberGuard将Trust2Vec模型与支持向量机（SVM）、K近邻（KNN）和随机森林等传统机器学习模型相结合，创建了一个用于评估信任和安全风险的强大机制。通过详细的优化和案例研究，CyberGuard在实际场景中展示了资源分配效率和整体系统性能的显著提升。我们的结果突出了CyberGuard的有效性，其显著的准确率、精确率、召回率和F1分数达到98.18%，证明了我们的综合方法在边缘计算和雾计算环境中的变革潜力。
