A computational scheme for data scheduling in industrial enterprise network using linear mixed model approach

The fourth industrial revolution promotes the numerous applications of the industrial internet of things to transmit integrated information. Scheduling multi-data of a distributed network involves many complications in maintaining network performance. This paper introduces a linear mixed model approach to multi-data integration and transmission in an industrial enterprise network. This paper includes the computational scheme and its performance evaluation of the linear mixed model. The proposed computation scheme incorporates the following contributions: i) a simulation of an industrial enterprise network configured with distributed network domains of multi-data. ii) Development of a linear mixed model approach to integrated multi-data for transmission through the shared network links. iii) The performance evaluation of the proposed scheme using the R squared value of Bayesian linear regression. The proposed scheme realizes the type of data and their level of integration at shared network links of an industrial enterprise network. The expected outcome is to exhibit healthy throughput in scheduling both level 1 and level 2 integration of multiple data types promising to shape the path toward Industry 4.0 applications.