Stochastic sensor scheduling for multiple dynamical processes over a shared channel

We consider the problem of multiple sensor scheduling for remote state estimation over a shared link. A number of sensors monitor different dynamical processes simultaneously but only one sensor can access the shared channel at each time instant to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling framework in which each sensor makes transmission decisions based on both the channel accessibility and the self event-triggering condition. The corresponding optimal estimator is explicitly given. By ultilizing the realtime information, the proposed schedule is shown to be a generalization of the time-based ones and outperform the time-based ones in terms of the estimation quality. By formulating an Markov decision process (MDP) problem with average cost criterion, we can find the optimal parameters for the event-based schedule. For practical use, we also design a simple suboptimal schedule to mitigate the computational complexity of solving an MDP problem. We also propose a method to quantify the optimality gap for any suboptimal schedules.