RaSEC: An Intelligent Framework for Reliable and Secure Multi-Level Edge Computing in Industrial Environments

Abstract

Industrial applications generate big data with redundant information that are transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework for Reliable and Secure multi-level Edge Computing (RaSEC) in industrial environments. This framework operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data, but also helps in preserving the privacy of data sources. In the second phase, a multi-step process is used to register Level-Two Edge Devices (LTEDs) with High-Level Edge Devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data are uploaded to the cloud servers for further analysis otherwise the data are discarded which minimizes the use of computational resources on cloud computing platforms. Simulation results show that our proposed framework is highly resilient against security and privacy threats. The proposed framework also helps in increasing the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications.

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