Mission Critical Control Systems Research Articles

Unsourced Multiple Access for Mission-Critical Control Systems in Industrial Internet of Things

Unsourced Multiple Access for Mission-Critical Control Systems in Industrial Internet of Things

Proactive fault-tolerant wireless mesh networks for mission-critical control systems

Although wireless networks are becoming a fundamental infrastructure for various control applications, they are inherently exposed to network faults such as lossy links and node failures in environments such as mining, outdoor monitoring, and chemical process control. In this paper, we propose a proactive fault-tolerant mechanism to protect the wireless network against temporal faults without any explicit network state information for mission-critical control systems. Specifically, the proposed mechanism optimizes the multiple routing paths, link scheduling, and traffic generation rate such that it meets the control stability demands even if it experiences multiple link faults and node faults. The proactive network relies on a constrained optimization problem, where the objective function is the network robustness, and the main constraints are the set of the traffic demand, link, and routing layer requirements. To analyze the robustness, we propose a novel performance metric called stability margin ratio, based on the network performance and the stability boundary. Our numerical and experimental performance evaluation shows that the traffic generation rate and the delay of wireless networks are found as critical as the network reliability to guarantee the stability of control systems. Furthermore, the proposed proactive network provides more robust performance than practical state-of-the-art solutions while maintaining high energy efficiency.

Optimal Control-Aware Transmission for Mission-Critical M2M Communications Under Bandwidth Cost Constraints

In this paper, we consider a mission-critical control system, where a dynamic plant is monitored by a mobile device (MD), and the monitored signal is transmitted to a remote controller via heterogeneous cellular and Wi-Fi networks. We propose an optimal control-aware machine-to-machine (M2M) transmission strategy for mission-critical control applications, in which control performance is measured by remote estimation error and system stability while limited by bandwidth cost. Specifically, the problem of minimizing estimation error, subject to the constraints of cellular usage costs and system stability, is formulated as an infinite-horizon constrained Markov decision process (CMDP), where the MD has options to transmit through Wi-Fi or cellular, or to stay idle. We solve the problem by utilizing the Lagrange multiplier approach, and prove that the optimal strategy is a randomized mixture of two threshold structure strategies. Furthermore, to estimate the structured optimal strategy, we present an algorithm called simultaneous perturbation stochastic approximation (SPSA), in which the complexity is $O(|\mathcal {A}|)$ lower than a non-structured one with $|\mathcal {A}|$ being the number of the actions.

Decentralized State-Driven Multiple Access and Information Fusion of Mission-Critical IoT Sensors for 5G Wireless Networks

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by multiple distributed IoT sensors over a wireless communication network with shared common spectrum. To reduce the complexity of Kalman filtering, we consider a constant gain filter at the remote controller. We propose a decentralized dynamic scheduling and information fusion of the IoT sensors to stabilize the unstable dynamic plant. The proposed scheme has a $\textit {state-driven}$ multiple access structure, where a large state estimation MSE (high $\textit {transmission urgency}$ ) and good wireless channel conditions (good $\textit {transmission opportunities}$ ) promote the active mode of the sensors. Using the Lyapunov techniques, we provide the closed-form sufficient condition for stability and closed-form characterizations on the trade-off between the state estimation MSE and average power consumption of the sensors. We also propose a design guideline for the constant filter gain via minimizing the state estimation MSE. The proposed scheme is also compared with various representative literature and we show that significant performance gains can be achieved.

Event-Driven Sensor Scheduling for Mission-Critical Control Applications

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by a number of distributed sensors connected to the controller over the wireless fading channels. We focus on the dynamic sensor scheduling to stabilize the unstable dynamic plant. The dynamic sensor scheduling is modeled as a non-convex drift-plus-penalty minimization problem. To improve the scheduling efficiency, the proposed scheme adapts to both the fading channel state as well as the dynamic plant state. To overcome the non-convexity in the minimization problem, we propose a novel transformation technique for the scheduling variables and the objective function (using the Lyapunov drift). Based on that, we can derive a low complexity dynamic sensor scheduling scheme and also obtain a closed-form stability analysis (despite the non-convexity) of the mission-critical control system via a randomized state-independent policy. Compared with various baselines, the proposed scheme has higher power efficiency and superior scalability performance.

Modulation-Free M2M Communications for Mission-Critical Applications

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by a number of distributed sensors. We propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contention-free</i> machine-to-machine (M2M) communication protocol for mission-critical control applications. Using modulation-free transmission, the proposed solution fundamentally exploits the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">additive properties</i> of the physical wireless channel. In contrast to all the existing digital-based M2M communications that avoid collision between the multiple sensors, the proposed protocol utilizes collision to enhance the system performance. Using the Lyapunov drift analysis approach, we further establish closed-form sufficient requirements on the communication resources needed to achieve stabilization of the mission-critical control system. The proposed scheme is also compared with various baselines and we show that significant performance gains can be achieved.