Intelligent Traffic-Service Mapping of Network for Advanced Industrial IoT Edge Computing

Document Type

Conference Proceeding

Publication Title

IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS

Abstract

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

DOI

10.1109/WFCS60972.2024.10540782

Publication Date

1-1-2024

Keywords

Explainable AI, Intelligent edge computing, Machine learning, Network service perception

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