Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 17, Pages 15618-15629Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3152634
Keywords
Industrial Internet of Things; Monitoring; Sensors; Gases; Optimization; Edge computing; Real-time systems; Cross-layer optimization; digital twins (DTs); hazardous gas tracing; Industrial Internet of Things (IIoT); parallel optimization framework
Categories
Funding
- National Natural Science Foundation of China [61902203]
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This work aims to explore the cross-layer optimization of digital twins in Industrial Internet of Things (IIoT) and applies it to the hazardous gas leakage boundary tracking in industries. By proposing an algorithm based on parallel optimization framework and establishing a distributed edge computing network, an effective industrial hazardous gas tracking algorithm has been developed. The results show that the algorithm can accurately track the gas boundary and reduce network energy consumption.
The development of the Industrial Internet of Things (IIoT) and digital twins (DTs) technology brings new opportunities and challenges to all walks of life. The work aims to study the cross-layer optimization of DTs in IIoT. The specific application scenarios of hazardous gas leakage boundary tracking in the industry is explored. The work proposes an industrial hazardous gas tracking algorithm based on a parallel optimization framework, establishes a three-layer network of distributed edge computing based on IIoT, and develops a two-stage industrial hazardous gas tracking algorithm based on a state transition model. The performance of different algorithms is analyzed. The results indicate that the tracking state transition and target wake-up module can effectively track the gas boundary and reduce the network energy consumption. The task success rate of the parallel optimization algorithm exceeds 0.9 in 5 s. When the number of network nodes in the state transition algorithm is N = 600, the energy consumption is only 2.11 J. The minimum tracking error is 0.31, which is at least 1.33 lower than that of the exact conditional tracking algorithm. Therefore, the three-layer network edge computing architecture proposed here has an excellent performance in industrial gas diffusion boundary tracking.
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