Cross-Layer Optimization for Industrial Internet of Things in Real Scene Digital Twins

Abstract

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.