Joint Scheduling and Channel Allocation for Kalman Filtering Over Multihop WirelessHART Networks

Remote state estimation over a wireless network is of significant importance in many industrial applications, such as condition monitoring. In these cases, sensors deliver their data to remote estimators through wireless channels, which makes communication reliability a core issue. In this article, we propose an error-aware design to carry out network scheduling and channel allocation according to estimation error covariance and channel quality, with the aim of minimizing the total estimation error covariance. We develop multidimensional conflict graphs to model the interference and conflicts, and on this basis, a two-phase heuristic algorithm is further proposed to adaptively assign slots and channels at each superframe. Theoretical analysis and extensive simulations are given to show the effectiveness of our error-aware design in preventing the estimation error covariance from diverging, and hence able to improve the accuracy of remote estimation and monitoring.