Ling Lyu

State Estimation Oriented Wireless Transmission for Ubiquitous Monitoring in Industrial Cyber-Physical Systems

State estimation over wireless sensor networks (WSNs) plays an important role for the ubiquitous monitoring in industrial cyber-physical systems (ICPSs). However, the unreliable wireless channels lead to the transmitted measurements arriving at the remote estimator intermittently, which will deteriorate the estimation performance. Question of how to improve the transmission reliability in the hostile industrial environment to guarantee the pre-defined estimation performance for ICPSs is largely unexplored. This paper is concerned with a redundant transmission strategy to meet the reliability requirement for state estimation. This strategy incorporates the ISM channels with the opportunistically harvested channels to provide adequate spectrum opportunities for redundant transmissions. First, we explore the relationship between the estimation performance and the transmission reliability, based on which a joint optimization of channel allocation and power control is then developed to guarantee the estimation performance and maximize the sum rate of the WSN. Second, we formulate the optimization into a mix-integer nonlinear programming problem, which is solved efficiently by decomposing it into the channel allocation and power control subproblems. Ultimately, simulation study demonstrates that the proposed strategy not only ensures the required state estimation performance, but also increases the sum rate of the WSN.

An optimal reputation-based detection against SSDF attacks in industrial cognitive radio network

With the increasing development of industrial wireless technologies and standards, the scarce spectrum in the industrial, scientific, and medical (ISM) band has been extremely overcrowded, which can be mitigated by harvesting more spectrum in licensed bands with the emerging cognitive radio technology. In industrial cognitive radio networks (ICRNs), security is one of the most important problems. Spectrum sensing data falsification (SSDF) attacks is one of major challenges for cooperative spectrum sensing (CSS) in ICRNs. Malicious users send fake decisions to mislead the fusion center for exploiting the spectrum source. And smart malicious users launch attacks with small probability which is difficult to be detected by existing detection scheme. To address this issue, we propose an optimal reputation-based detection scheme with considerations of the attack probability. Moreover, the threshold of reputation value is specially designed to adapt to the varying attack probability which can be estimated by past performance. To estimate the attack probability, we use the maximum likelihood estimation method in certain CSS rounds. Numerical results reveal that the proposed detection scheme performs better than existing reputation-based detection schemes.

Transmission reliability enhancement for multi-sensor state estimation in industrial CPSs

This paper investigates the multi-sensor state estimation over lossy wireless channels for industrial CPSs. We firstly discover the relationship between the multi-sensor state estimation error and the measurement arrival rate, based on which a cognitive radio enabled redundant transmission strategy is proposed to improve the transmission reliability. Then the transmission strategy is set by solving a mixed integer nonlinear programming problem, whose objective is to reduce the estimation error and the energy consumption. Finally, the simulations show the effectiveness and advantages of the proposed strategy.

State Estimation Oriented Reliability Enhancement with Cooperative Transmission in Industrial CPSs

In industrial cyber-physical systems (ICPSs), state estimation provides the best possible approximation for the unmeasurable system state based on the received measurements from sensors via lossy wireless channels. As a result, the estimation performance heavily depends on the transmission reliability. In this paper, a cognitive radio assisted cooperative transmission scheme is proposed to improve the accuracy of state estimation by delivering necessary redundant measurements to the remote estimator. The relationship between the accuracy of multi- sensor state estimation and the arrival rate of measurements is explored. Based on this, an optimization problem is formulated to minimize the state estimation error by jointly allocating the harvested licensed channels and the ISM channels with power control and admission control. A sub-optimal decomposition scheme is proposed to solve this intractable problem efficiently. Numerical results demonstrate that the proposed scheme significantly outperforms existing schemes by reducing more than 73% packet loss rate and 56% estimation errors.

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

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 ones 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%.