Cognitive radio enabled reliable transmission for optimal remote state estimation in multi-sensor industrial cyber-physical systems

Abstract

This paper investigates the remote state estimation for the multi-sensor industrial cyber-physical systems (ICPSs) with the help of cognitive radio (CR) technology. The remote state estimator estimates the system states based on the measurements received from multi-sensor via the unreliable communication media, therefore, the performance of remote state estimation is up to the transmission reliability. In this paper, the redundant transmission is adopted to improve the reliability. Furthermore, the CR technology, which can intelligently discover the available spectrum opportunities in licensed bands, is exploited to alleviate the spectrum over-crowd problem aggravated by the redundancy design to improve the state estimation performance by. Specifically, we firstly formulate two CR enabled sequential optimization problems to improve the accuracy of state estimation and enhance the understanding of industrial plant. The primary one is to minimize the CR enabled estimation error subjected to the limited resource. The secondary one is to maximize the CR enabled best-effort data transmission volume subjected to the primary one's solutions. Secondly, a new sequence is constructed to approximate the limit-form objective function of the primary optimization problem. Finally, the two optimization problems are transformed into convex programming with the Lagrangian relaxation and Lagrangian dual decomposition techniques to reduce the computational complexity. Numerical results demonstrate that the CR technology reduces the mean square error of state estimation by about 40% and increases the volume of the best-effort data transmission by about 320%.