Toward Secure Data Fusion in Industrial IoT Using Transfer Learning

Abstract

As an emerging technology, the industrial Internet of Things (IIoT) can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. In IIoT, the improvement and progress of industrial production and applications are inseparable from data fusion, a process that realizes the collection, analysis, and processing of the massive IoT data generated by industrial equipment and applications. IIot demands a real-time, effective, and privacy-preserving data fusion process. However, the existing works need to train different learning models for data analysis, which cannot meet real-time requirements in IIoT. Meanwhile, the lack of defense against internal attacks and the difficulty to balance system performance and privacy protection hinder the effectiveness and privacy protection in the data fusion process. To solve the abovementioned problems, in this article, we propose a new transfer learning-based secure data fusion strategy (TSDF) for IIoT. The proposed TSDF consists of three parts, guidance based deep deterministic policy gradient (GDDPG) algorithm for task classification, transfer learning based GDDPG for grouping of task receivers, and a multiblockchain mechanism for privacy preservation. The experiment results show that TSDF can achieve high system throughput and low latency, providing privacy preservation in data fusion under various IIoT application environments.