An Optimal Resource Allocation Method for IIoT Network

Pratik Goswami, A. Mukherjee, Pushpita Chatterjee, Lixia Yang
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引用次数: 6

Abstract

The recent technical evolution is revolving around Internet of Things (IoT). The Internet of Softwarized Things (IoST) as a subset of IoT, is making its mark mostly towards industrial applications to connect all the devices and improve the computation capability and networking flexibility. The Industrial IoT (IIoT) consists of a large network, where the multiple works are processed continuously at a time. Therefore, multi-objective interference issue in the path remains as obstacle, for which the networking resources are lost. The existing works were performed with fixed resources and dedicated channel states which make the network less flexible with more time response. In this paper, the problem is addressed with optimal resource allocation using convolutional neural network (CNN) to extract the optimal channel state for different applications, which ease the computations along with efficiency. Furthermore, the proposed method is validated with the mathematical analysis and simulation.
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工业物联网网络资源最优分配方法
最近的技术发展是围绕物联网(IoT)展开的。软件物联网(IoST)作为物联网的一个子集,主要面向工业应用,以连接所有设备,提高计算能力和网络灵活性。工业物联网(IIoT)由一个大型网络组成,其中多个工作同时连续处理。因此,路径上的多目标干扰问题仍然是一个障碍,导致网络资源的损失。现有的工作是在固定的资源和专用的信道状态下完成的,这使得网络的灵活性较低,时间响应较多。本文采用卷积神经网络(convolutional neural network, CNN)对资源进行最优分配,提取不同应用的最优信道状态,在简化计算的同时提高效率。通过数学分析和仿真验证了该方法的有效性。
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