The Industrial Internet of Things (IIoT) has expanded worldwide rapidly, which brings key devices and applications of IIoT under a trustworthy umbrella that reinforces secure and safe IIoT services have never been more important. However, there are few effective methods for assessing the trustworthiness of IIoT networks and services, which may lead to a compromised system and massive decreases in productivity, or even catastrophic consequences. Complex networks have emerged to be a promising method to assess the trustworthiness of IIoT because they can reveal the latent features of networks and services. Enlightened by the potential of complex networks, a cloud-fog-edge computing paradigm for IIoT is presented and mapped to multilayer networks. Furthermore, we propose a Trustworthiness Assessment with Entropy (TAE) method, which quantitatively analyzes the topological characteristics of the IIoT networks and services. Experimental results on synthetic and real-world datasets present a comprehensive assessment of IIoT trustworthiness with the qualitative and quantitative analysis of von Neumann entropy, which proves the feasibility and robustness of the proposed method.
Scopus Subject Areas
- Industrial Internet of Things
- Multilayer networks
- Von Neumann entropy