Publications

Abstract : Recent advancements in Industrial Internet of Things (IIoT) enhance industrial productivity through digitalization and improved network efficiency. IIoT is based on adaptive perception and intelligent decision-making which are tedious to accomplish because of the heterogeneous and complex industrial environment. Digital twin is a promising digitalization technology that offers an effective solution for capturing the difficult industrial environment and creating virtual models of physical objects. But the provisioning of network resources in IIoT is highly challenging because of resource limited devices, stochastic processes, and resource heterogeneity. Therefore, metaheuristic optimization algorithms can be designed to resolve the resource allocation issue in the IIoT environment. We propose a hybrid bird mating optimization-based resource allocation scheme (HBMO-RAS) for the digital twin Driven IIoT environment. The HBMO-RAS model effectively monitors, handles, and distributes the resources available. HBMO-RAS uses a hybrid bird mating optimizer (BMO) with differential evolution (DE) algorithm wherein the DE is applied to update the society of the BMO algorithm and improve the solution quality. To demonstrate the performance of the HBMO-RAS model, we performed a comprehensive performance analysis. The results obtained show the superior performance of the HBMO-RAS model over recent approaches.