Privacy-Preserving Database Assisted Spectrum Access for Industrial Internet of Things: A Distributed Learning Approach

Abstract

Industrial Internet of Things (IIoT) has been shown to be of great value to the deployment of smart industrial environment. With the immense growth of Internet of Things (IoT) devices, dynamic spectrum sharing is introduced, envisaged as a promising solution to the spectrum shortage in IIoT. Meanwhile, cyber–physical safety issue remains to be a great concern for the reliable operation of IIoT system. In this article, we consider the dynamic spectrum access in IIoT under a received signal strength-based adversarial localization attack. We employ a practical and effective power perturbation approach to mitigate the localization threat on the IoT devices and cast the privacy-preserving spectrum sharing problem as a stochastic channel selection game. To address the randomness induced by the power perturbation approach, we develop a two-timescale distributed learning algorithm that converges almost surely to the set of correlated equilibria of the game. The numerical results show the convergence of the algorithm and corroborate that the design of two-timescale learning process effectively alleviates the network throughput degradation brought by the power perturbation procedure.