人工智能和区块链辅助的雾计算卸载和资源分配框架

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammad Aknan, Maheshwari Prasad Singh, Rajeev Arya
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引用次数: 0

摘要

物联网(IoT)应用在医疗保健、农业、学术界、工业、交通、智慧城市等多个领域的作用大大增加,使人类的生活更美好。物联网设备的数量呈指数级增长,并产生物联网节点无法处理的大量数据。集中式云架构可以处理这些庞大的物联网数据,但由于传输延迟高、网络拥塞和带宽,无法提供服务质量(QoS)。雾模式已经发展,将计算资源带到网络边缘,为延迟敏感的物联网应用程序提供服务。然而,卸载决策、异构雾网络、不同的工作负载、安全问题、能源消耗和预期的QoS是该领域的重大挑战。因此,我们提出了一个支持区块链的智能框架来解决上述问题,并在协作云雾环境中为即将到来的物联网请求分配最佳资源。该框架集成了基于人工智能(AI)的元启发式算法,该算法具有高收敛率,并且能够在运行时做出卸载决策,从而提高了结果质量。区块链技术可以保护物联网应用程序及其数据免受现代攻击。实验结果表明,在类似的实验环境下,与其他元启发式方法相比,所提出的框架在执行时间和成本上显著提高了20%,能耗提高了18%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI and Blockchain Assisted Framework for Offloading and Resource Allocation in Fog Computing

The role of Internet of Things (IoT) applications has increased tremendously in several areas like healthcare, agriculture, academia, industries, transportation, smart cities, etc. to make human life better. The number of IoT devices is increasing exponentially, and generating huge amounts of data that IoT nodes cannot handle. The centralized cloud architecture can process this enormous IoT data but fails to offer quality of service (QoS) due to high transmission latency, network congestion, and bandwidth. The fog paradigm has evolved that bring computing resources at the network edge for offering services to latency-sensitive IoT applications. Still, offloading decision, heterogeneous fog network, diverse workload, security issues, energy consumption, and expected QoS is significant challenges in this area. Hence, we have proposed a Blockchain-enabled Intelligent framework to tackle the mentioned issues and allocate the optimal resources for upcoming IoT requests in a collaborative cloud fog environment. The proposed framework is integrated with an Artificial Intelligence (AI) based meta-heuristic algorithm that has a high convergence rate, and the capability to take the offloading decision at run time, leading to improved results quality. Blockchain technology secures IoT applications and their data from modern attacks. The experimental results of the proposed framework exhibit significant improvement by up to 20% in execution time and cost and up to 18% in energy consumption over other meta-heuristic approaches under similar experimental environments.

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CiteScore
7.20
自引率
4.30%
发文量
567
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