A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things

Mian Wang, Cong’an Xu, Yun Lin, Zhiyi Lu, Jinlong Sun and Guan Gui ()
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Mian Wang: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Cong’an Xu: Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 100085, China
Yun Lin: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150009, China
Zhiyi Lu: Nanjing Great Information Technology Co., Ltd., Nanjing 210003, China
Jinlong Sun: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Guan Gui: College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Future Internet, 2023, vol. 15, issue 5, 1-17

Abstract: The Industrial Internet of Things (IIoT) refers to the application of the IoT in the industrial field. The development of fifth-generation (5G) communication technology has accelerated the world’s entry into the era of the industrial revolution and has also promoted the overall optimization of the IIoT. In the IIoT environment, challenges such as complex operating conditions and diverse data transmission have become increasingly prominent. Therefore, studying how to collect and process a large amount of real-time data from various devices in a timely, efficient, and reasonable manner is a significant problem. To address these issues, we propose a three-level networking model based on distributed sensor self-networking and cloud server platforms for networking. This model can collect monitoring data for a variety of industrial scenarios that require data collection. It enables the processing and storage of key information in a timely manner, reduces data transmission and storage costs, and improves data transmission reliability and efficiency. Additionally, we have designed a feature fusion network to further enhance the amount of feature information and improve the accuracy of industrial data recognition. The system also includes data preprocessing and data visualization capabilities. Finally, we discuss how to further preprocess and visualize the collected dataset and provide a specific algorithm analysis process using a large manipulator dataset as an example.

Keywords: data generation; Industrial Internet of Things (IIoT); data acquisition; distributed sensors; feature fusion (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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