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首页> 外文期刊>IEEE transactions on industrial informatics >Online GMM Clustering and Mini-Batch Gradient Descent Based Optimization for Industrial IoT 4.0
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Online GMM Clustering and Mini-Batch Gradient Descent Based Optimization for Industrial IoT 4.0

机译：工业IOT 4.0的在线GMM聚类和基于Mini-Batch梯度下降的优化

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摘要

The future fifth-generation (5G) networks are expected to support a huge number of connected devices with various and multitude services having different quality of service (QoS) requirements. Communication in Industry 4.0 is one of the flagships and special applications of the 5G due to the specificity of the industrial environment as well as the variety of its services such as safety communication, robot's communications, and machine monitoring. In this context, we propose a new resource allocation for the future Industry 4.0 based on software-defined networking and network function virtualization technologies, machine learning tools and the slicing paradigm where each slice of the network is dedicated to a category of services having similar QoS requirement level. In this article, the proposed solution ensures the allocation of the resources to the slices depending on their requirements in terms of bandwidth, delay, and reliability. Toward this goal, our solution is performed in three main steps: first, Internet of Things (IoT) devices assignment to the slices step based on online Gaussian mixture model clustering algorithm, second, inter-slices resources reservations step based on mini-batch gradient descent, and third, intra-slices resources allocations based on the max-utility algorithm. We have performed extensive simulations in a realistic industrial scenario using NS3 simulator. Numerical results show the effectiveness of our proposed solution in terms of reducing packet error rate, energy consumption, and in terms of increasing the percentage of served devices in delay comparing to the traditional approaches.
机译：预计未来第五代（5G）网络将支持大量连接设备，其中具有不同的服务质量（QoS）要求的各种和众多服务。由于工业环境的特殊性以及安全通信，机器人通信和机器监控等服务的特殊性，Industrial 4.0是5G的旗舰和特殊应用之一。在这方面，我们提出了基于软件定义的网络和网络功能虚拟化技术，机器学习工具和切片范例的新资源配置4.0，其中每片网络都专用于具有类似QoS的服务类别要求水平。在本文中，所提出的解决方案确保根据它们在带宽，延迟和可靠性方面的要求来将资源分配给切片。对此目标，我们的解决方案在三个主要步骤中进行：第一，事物互联网（物联网）基于在线高斯混合模型聚类算法的切片步骤，第二，基于迷你批量梯度的切片资源预留步骤基于MAX-Utility算法的下降和第三，切片内部资源分配。我们在使用NS3模拟器中在现实的工业场景中进行了广泛的模拟。数值结果表明了我们提出的解决方案在降低分组错误率，能耗以及增加与传统方法的延迟比较时的服务设备的百分比的效果。

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