A way forward towards a technology‐driven development of industry 4.0 using big data analytics in 5G‐enabled IIoT

Atharvan, Gannu; Krishnamoorthy, Sreelakshmi; Dua, Amit; Gupta, Shashank

doi:10.1002/dac.5014

Cited by 18 publications

(10 citation statements)

References 149 publications

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“…The Industrial Internet of Things (IIoT) is the incorporation of IoT technologies into industrial sectors, linking an extensive array of industrial sensors, devices, and user endpoints to generate significant volumes of industrial data [ 1 , 2 ]. The amount of data produced in the IIoT is directly linked to the growing number of internet-connected devices [ 3 , 4 , 5 ]. Typical application scenarios encompass smart manufacturing, logistics management, and remote equipment maintenance.…”

Section: Introductionmentioning

confidence: 99%

ASAP-IIOT: An Anonymous Secure Authentication Protocol for Industrial Internet of Things

Li,

Ma,

Wang

2024

Sensors

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With the increasing demand for a digital world, the Industrial Internet of Things (IIoT) is growing rapidly across various industries. In manufacturing, particularly in Industry 4.0, the IIoT assumes a vital role. It encompasses many devices such as sensing devices, application servers, users, and authentication servers within workshop settings. The security of the IIoT is a critical issue due to wireless networks’ open and dynamic nature. Therefore, designing secure protocols among those devices is an essential aspect of IIoT security functionality and poses a significant challenge to the IIoT systems. In this paper, we propose a lightweight anonymous authentication protocol to preserve privacy for IIoT users, enabling secure IIoT communication. The protocol has been validated to demonstrate its comprehensive ability to overcome various vulnerabilities and prevent malicious attacks. Finally, the performance evaluation confirms that the proposed protocol is more effective and efficient than the existing alternatives.

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Section: Introductionmentioning

confidence: 99%

ASAP-IIOT: An Anonymous Secure Authentication Protocol for Industrial Internet of Things

Li,

Ma,

Wang

2024

Sensors

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“…Production scheduling is a core part of industrial production management, which involves the arrangement of production tasks, allocation and optimization of resources, and control of production processes, etc. [3][4][5] Effective production scheduling can significantly improve production efficiency, reduce costs, and improve product quality. Dynamic programming algorithms are a kind of mathematical method for solving the optimization problem of the decision-making process under the given constraints.…”

Section: Introductionmentioning

confidence: 99%

Research on Production Scheduling of Industrial Big Data for Internet of Things Based on Dynamic Planning Algorithm

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, the optimized Dual Heuristic Dynamic Programming (DHP) algorithm is studied in depth, compared and analyzed with the other three algorithms through experimental simulation, and the DHP algorithm is applied to different industrial accurate scheduling tests to explore the loads of the industrial equipment during scheduling operation. The simulation results show that the dual-heuristic dynamic programming DHP algorithm has a significant decrease in both the mean waiting time (MWT) and the mean response time (MRT), where the highest peak value of the mean waiting time is only 0.06901 μs, and the highest peak value of the mean response time is 0.24493 μs. In industrial accurate production scheduling, the improved DHP algorithm performs the best for textile industry’’s task scheduling with the best performance. Specifically, the task scheduling completion time for the textile industry is 4522 seconds when the number of automated guided vehicles (AGVs) is the minimum value of 3. In comparison, the completion time is 8541 seconds when the number of AGVs is the maximum value of 15. The textile industry shows the fastest task completion time compared to the other industry types for different settings of the number of AGVs. In addition, the analysis of the operation of the equipment engines shows that although there are high and low torques during operation, the Torque of most of the engines stays within the range of 29 Nm to 30.7 Nm, which is generally stable and has little impact on the performance of the equipment. The improved dynamic programming algorithm proposed in this paper has effective application potential in industrial extensive data production scheduling, which can improve the efficiency and responsiveness of production scheduling. It has important practical significance for industrial production management.

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“…It can be seen that the construction of rural culture is the weakness and board of building a strong socialist modernization country [4][5]. The era of informationization has come to life in today's society, and emerging technologies have led to significant changes in the government governance system and governance structure [6]. Therefore, the key to implementing the rural revitalization strategy is to capitalize on the advantages of the digital economy.…”

Section: Introductionmentioning

confidence: 99%

Construction of a big data platform for infrastructure in the rural revitalisation strategy

Han,

Yu,

et al. 2023

Applied Mathematics and Nonlinear Sciences

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In the context of rural revitalization strategy, this paper uses an adaptive density-sensitive similarity measure method to improve based on a spectral clustering algorithm and combines with coordinate visualization technology to cluster rural infrastructure data for visualization. On this basis, a rural infrastructure big data platform is constructed by designing data management, data analysis and data visualization, and the overall performance of the rural infrastructure big data platform is tested in the configured test environment. The results show that when the visualization node is 50000, the running time is only 153.8s, the big data platform can output data stably, the proportion of failure is within 3%, and the corresponding time of each interface on the line is also 1000ms. Based on this research, the infrastructure for a big data platform in rural revitalization strategies can be successfully constructed.

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A way forward towards a technology‐driven development of industry 4.0 using big data analytics in 5G‐enabled IIoT

Cited by 18 publications

References 149 publications

ASAP-IIOT: An Anonymous Secure Authentication Protocol for Industrial Internet of Things

ASAP-IIOT: An Anonymous Secure Authentication Protocol for Industrial Internet of Things

Research on Production Scheduling of Industrial Big Data for Internet of Things Based on Dynamic Planning Algorithm

Construction of a big data platform for infrastructure in the rural revitalisation strategy

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