Secure Cloud-Edge Collaborative Method for Dynamic Industrial Process Monitoring Using Self-Updating Dictionary Learning

Abstract

Modern industrial systems possess the capacity to accumulate substantial data, thereby enabling data-driven process monitoring. However, the dynamic industrial processes give rise to new working conditions continuously. Traditional methods require substantial new data for updates, resulting in considerable delays. Furthermore, edge devices face resource limitations, which complicates the ability to meet the increasing demands for storage and computing power. On the other hand, the frequent transmission of data between the cloud and edge introduces potential security risks. To address these challenges, this paper proposes a Secure Monitoring Method based on Self-Updating Dictionary Learning (SUDL-SM) within a cloud-edge collaboration framework. Specifically, to tackle the issue of poor model adaptability caused by limited data of new modes, this paper first proposes a dictionary learning method based on multi-task hardness evaluation. By evaluating the multi-dimensional contributions of samples, those with strong generalization are extracted, and the dictionary is updated online accordingly, ensuring adaptability to both new and historical conditions. Subsequently, due to the resource constraints inherent in edge devices, a dictionary distillation compression method has been proposed. This method aims to maximize dictionary compression while preserving the original monitoring performance, thereby ensuring efficient and accurate inference on edge devices. Finally, a hybrid encryption-based cloud and edge data transmission protocol is designed to effectively address malicious activities such as data theft and tampering by ensuring reliable interaction between the cloud and the edge. Extensive experiments verified the effectiveness and superiority of the proposed method. Note to Practitioners—The continual emergence of new working conditions in actual industrial processes leads to serious model mismatch issues. However, resource limitations affect real-time updating and monitoring. This paper aims to address critical issues in practical industrial applications, particularly focusing on effective dynamic industrial processes in resource-constrained environments and ensuring data privacy protection within the industrial Internet. By combining cloud edge collaboration, dictionary self-updating and dictionary distillation compression technology, it can adapt to the complex and constantly changing operating environment while ensuring the monitoring accuracy. In addition, the cloud-edge data transmission protocol based on hybrid encryption effectively guarantees a secure data transmission process, thereby providing a more efficient and reliable monitoring solution for industrial production. Compared with the conventional method, the proposed method overcomes the inherent resource limitations on the edge devices and the information security problems between the cloud and edge. Overall, it is suitable for actual dynamic industrial systems.