A Trusted Cloud–Edge Decision Architecture Based on Blockchain and MLP for AIoT
Cheng Chi, Zihang Yin, Yang Liu, Senchun Chai
Abstract
With the continuous development of Internet of Things (IoT) technology and artificial intelligence (AI) technology, the demand for Artificial Intelligence of Things (AIoT) edge applications is increasing. However, there are challenges in AIoT edge applications, such as limited resources of edge devices, data privacy leakage, inconsistent model deployment, device authentication, and data sharing difficulties, which can affect the security and intelligence level of AIoT edge applications. Therefore, we propose a trusted cloud–edge decision architecture that ensures trustworthy authentication of terminal devices. We use lightweight deep neural network training technology to run multilayer perceptron (MLP) models on resource-limited edge devices, reducing the difficulty of model design and development. We also introduce blockchain technology to enhance the security and privacy of model and data processing. We describe the four-layer architecture and corresponding workflow details, and we introduce the main data models and focus on the core technologies of the architecture. Finally, we completed the simulation verification of the model using carbon emissions data as a sample, demonstrating the feasibility and effectiveness of the model.