智能如何重塑今天:物联网边缘网络?

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Syed Usman Jamil , M. Arif Khan , M.A. Rahman , Muhammad Ali Paracha , Tanveer Zia , Syed Sadiqur Rahman , Syed Bilal Ahmed
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引用次数: 0

摘要

万物互联(IoE)设备的快速发展,加上第六代(6G)等新兴通信技术,正在促进基于IoE的边缘网络的发展,其中服务更接近网络外围。在这些网络中,设备交换信息并共享计算资源。本研究论文提出了一种新的方法框架,旨在优化IoE-6G系统中的任务分配和设备选择。通过集成智能算法,我们的研究调查了设备选择延迟、计算效率和任务成功率对整体系统性能的影响。6G的独特功能,如人工智能原生基础设施、超可靠低延迟通信(URLLC)、大规模机器类型通信(mMTC)、太赫兹(THz)频段和动态网络切片,构成了我们提出的框架的基础推动者。这些功能对于可扩展和实时的IoE操作至关重要,并紧密集成到我们提出的系统算法设计中。我们的方法框架包括广泛的模拟,在各种情况下评估拟议的系统,重点是基本概念和性能指标,这对于理解影响我们研究成果的参数至关重要。我们提供了传统和智能调度算法的详细比较,展示了当使用智能时在任务分配和完成时间方面的显着改进。智能主任务卸载调度算法(iMTOSA)的新颖性在于其动态智能和自适应调度,通过关注高级指标组和深入讨论现实世界IoE- 6g环境的影响,以最小的通信优化IoE任务卸载和增强的可扩展性。我们的研究结果有助于更深入地理解现代通信网络中智能系统的集成,为未来物联网技术的发展铺平道路。在整体性能上,该算法的智能变体受影响的任务小于50%,而非智能调度算法受影响的任务超过90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How intelligence is reshaping today: IoE edge networks?
The rapid advancement of Internet of Everything (IoE) devices, coupled with emergent communication technologies such as Sixth Generation (6G), is facilitating the development of IoE-based edge networks wherein services are rendered closer to the network periphery. In these networks, devices exchange information and share computational resources. This research paper presents a novel methodological framework designed to optimise task allocation and device selection in IoE-6G systems. By integrating intelligent algorithms, our study investigates the impact of device selection delay, computational efficiency, and task success rates on overall system performance. The unique capabilities of 6G, such as AI-native infrastructure, Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), Terahertz (THz) frequency bands, and dynamic network slicing, form the foundational enablers of our proposed framework. These features are essential for scalable and real-time IoE operations and are tightly integrated into our proposed system’s algorithmic design. Our methodological framework involves extensive simulations that evaluate the proposed system across various scenarios, focusing on foundational concepts and performance metrics that are essential for understanding the parameters influencing our research outcomes. We provide a detailed comparison of traditional and intelligent scheduling algorithms, showcasing significant improvements in task allocation and completion times when intelligence is employed. The novelty of Intelligent Main Task Off-loading Scheduling Algorithm (iMTOSA) lies in its dynamic intelligence and adaptive scheduling, optimising IoE task offloading with minimal communication and enhanced scalability by focusing on advanced groups of metrics and thoroughly discussing the implications for real-world IoE-6G environments. Our results contribute to a deeper understanding of the integration of intelligent systems in modern communication networks, paving the way for future advancements in IoE technologies. In overall performance, the intelligent variants of the proposed algorithm show less than 50% affected tasks, while non-intelligent scheduling algorithms exceed 90% affected tasks.
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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