Secure Data Storage and Searching for Industrial IoT by Integrating Fog Computing and Cloud Computing

TLDR

Industrial IoT generates vast amounts of data, yet local device storage is limited and devices are vulnerable, necessitating external storage solutions. This study investigates challenges in data processing, secure storage, efficient retrieval, and dynamic collection within IIoT. The authors design a fog–cloud hybrid framework that preprocesses raw data at the edge, stores time‑sensitive information locally, forwards non‑time‑sensitive data to the cloud, and evaluates performance through experiments and simulations. Results demonstrate that the framework markedly improves the efficiency and security of data storage and retrieval in IIoT.

Abstract

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices' energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.