Dynamic Network Slicing Orchestration for Remote Adaptation and Configuration in Industrial IoT

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 6; pp. 4297 - 4307
• Main Authors: Ji, Luyue, He, Shibo, Wu, Wenjie, Gu, Chaojie, Bi, Jichao, Shi, Zhiguo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Adaptation; Algorithms; Artificial intelligence; Computer architecture; Configurations; Feasibility studies; Heuristic algorithms; Heuristic methods; Industrial applications; Industrial Internet of Things; industrial Internet of Things (IIoT); Intelligent manufacturing systems; Internet of Things; Network slicing; remote adaptation and configuration; Resource allocation; Resource management; Smart manufacturing; Software-defined networking; software-defined networking (SDN); Virtual networks
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3131355

As an emerging and prospective paradigm, the industrial Internet of Things (IIoT) enable intelligent manufacturing through the interconnection and interaction of industrial production elements. The traditional approach that transmits data in a single physical network is undesirable because such a scheme cannot meet the network requirements of different industrial applications. To address this problem, in this article, we propose a network slicing orchestration system for remote adaptation and configuration in smart factories. We exploit software-defined networking and network functions virtualization to slice the physical network into multiple virtual networks. Different applications can use a dedicated network that meets its requirements with limited network resources with this scheme. To optimize network resource allocation and adapt to the dynamic network environments, we propose two heuristic algorithms with the assistance of artificial intelligence and the theoretical analysis of the network slicing system. We conduct numerical simulations to learn the performance of the proposed algorithms. Our experimental results show the effectiveness and efficiency of our proposed algorithms when multiple network services are concurrently running in the IIoT. Finally, we use a case study to verify the feasibility of the proposed network slicing orchestration system on a real smart manufacturing testbed.