An Efficient Approach to Sharing Edge Knowledge in 5G-Enabled Industrial Internet of Things

Abstract

Thanks to the booming development of artificial intelligence, 5G technology, and intelligent manufacturing technology, numerous intelligent edge devices contained in the industrial Internet of Things (IIoT) are endowed with the ability to mine knowledge from perceived massive data. Knowledge-driven IIoT plays an unprecedented role in application fields such as cyber-physical systems and Industry 4.0. However, knowledge is generally scattered across the distributed edge devices of IIoT. Therefore, in order to further achieve the edge intelligence in IIoT, it is very important to explore an efficient edge knowledge sharing method. In this article, we establish a decentralized knowledge sharing platform in IIoT. First, for public knowledge, a dynamics model that can quantitatively describe its sharing process is established by using the system dynamics theory. Furthermore, a control method for maximizing public knowledge sharing under constraints based on the optimal control theory is presented. Second, for private knowledge, a trusted transaction control method based on blockchain technology is proposed. By developing both smart contract and lightweight consensus mechanism, the efficient peer-to-peer sharing of private knowledge is realized, and the integrity of knowledge and the privacy of participants are protected. The results of extensive experiments show that the proposed method can eliminate the obstacles of knowledge sharing among edge devices in IIoT, and further promote the development of edge intelligence empowered 5G-enabled IIoT applications.