Industrial Internet of Things

List of Articles Industrial Internet of Things

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1 - The effect of Internet of Things (IOT) implementation on the Rail Freight Industry; A futures study approach
Noureddin Taraz Monfared علی شایان ali rajabzadeh ghotri
20.1001.1.27170411.1402.15.55.10.1

The rail freight industry in Iran has been faced several challenges which affected its performance. Notwithstanding that Internet of Things leverage is rapidly increasing in railway industries-as an experienced solution in other countries-, Iran’s rail freight industry More

The rail freight industry in Iran has been faced several challenges which affected its performance. Notwithstanding that Internet of Things leverage is rapidly increasing in railway industries-as an experienced solution in other countries-, Iran’s rail freight industry has not been involved in, yet. Related research and experiment has not been identified in Iran, as well. The aim of this survey is to identify the effects of the implementation of Internet of Things in the Rail Freight Industry in Iran. To gather the data, the Delphi method was selected, and the Snowball technique was used for organizing a Panel including twenty experts. To evaluate the outcomes, IQR, Binomial tests, and Mean were calculated. Several statements were identified and there was broad consensus on most of them, which approved that their implementation affects the Iranian rail freight industry, but in different ranks. Finally, the results formed in the Balanced Scorecard’s format. The internal business process has been affected more than the other aspects by the approved statements. Eleven recognized elements are affected in different ranks, including Internal Business Process, Financial, Learning, and Growth, Customers. The Financial perspective remarked as least consensus and the Internal Business Process has received the extreme consensus. The research outcomes can be used to improve the strategic planning of the Iranian rail freight industry by applying the achievements of information technology in practice. Manuscript profile
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2 - Improvement of intrusion detection system on Industrial Internet of Things based on deep learning using metaheuristic algorithms
mohammadreza zeraatkarmoghaddam majid ghayori
20.1001.1.27170411.1402.15.57.12.7

Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or i More

Due to the increasing use of industrial Internet of Things (IIoT) systems, one of the most widely used security mechanisms is intrusion detection system (IDS) in the IIoT. In these systems, deep learning techniques are increasingly used to detect attacks, anomalies or intrusions. In deep learning, the most important challenge for training neural networks is determining the hyperparameters in these networks. To overcome this challenge, we have presented a hybrid approach to automate hyperparameter tuning in deep learning architecture by eliminating the human factor. In this article, an IDS in IIoT based on convolutional neural networks (CNN) and recurrent neural network based on short-term memory (LSTM) using metaheuristic algorithms of particle swarm optimization (PSO) and Whale (WOA) is used. This system uses a hybrid method based on neural networks and metaheuristic algorithms to improve neural network performance and increase detection rate and reduce neural network training time. In our method, considering the PSO-WOA algorithm, the hyperparameters of the neural network are determined automatically without the intervention of human agent. In this paper, UNSW-NB15 dataset is used for training and testing. In this research, the PSO-WOA algorithm has use optimized the hyperparameters of the neural network by limiting the search space, and the CNN-LSTM neural network has been trained with this the determined hyperparameters. The results of the implementation indicate that in addition to automating the determination of hyperparameters of the neural network, the detection rate of are method improve 98.5, which is a good improvement compared to other methods. Manuscript profile

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List of Articles Industrial Internet of Things