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 <inline-formula> <tex-math notation="LaTeX">$\textit {state-driven}$ </tex-math></inline-formula> multiple access structure, where a large state estimation MSE (high <inline-formula> <tex-math notation="LaTeX">$\textit {transmission urgency}$ </tex-math></inline-formula>) and good wireless channel conditions (good <inline-formula> <tex-math notation="LaTeX">$\textit {transmission opportunities}$ </tex-math></inline-formula>) 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.