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A Neural-Network-Based Optimal Resource Allocation Method for Secure IIoT Network
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DOI：10.1109/JIOT.2021.3084636.png)
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Abstract
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Data security and resource allocation are two important terms associated with the Internet of Things (IoT). This recent technical evolution has made its mark in industrial applications making the network more flexible and computation friendly through connecting all the devices. As a subset of IoT, the framework of Industrial IoT (IIoT) is based on the huge number of nodes with the continuous process of multiple works at a time. Due to this, multiobjective network, interference in the path always becomes the reason for the loss of network resources as well as the security of data becomes vulnerable. In most of the previous works, dedicated channel states are considered for fixed resources which remains a major issue of IIoT network flexibility along with security. In this article, both the problems are incorporated by calculating the channel security and using convolutional neural network (CNN) optimal channel state extracted for different applications. This results as a fast system with proper utilization of resources and validated with mathematical analysis and simulations.
Keywords:
Channel security
convolutional neural network (CNN)
Industrial Internet of Things (IIoT)
resource allocation
signal-to-interference ratio (SIR)
wireless sensor network (WSN)
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