On Stochastic Sensor Network Scheduling for Multiple Processes

TL;DR

This paper introduces a stochastic event-based sensor scheduling method for multiple processes sharing a communication channel, improving estimation accuracy over traditional time-based schedules by utilizing real-time information and solving an MDP for optimal parameters.

Contribution

The paper proposes a novel stochastic event-based scheduling approach for multi-process sensor networks, with explicit MMSE estimation and a low-complexity greedy algorithm for parameter optimization.

Findings

Outperforms time-based scheduling in estimation quality.

Provides an explicit MMSE estimator for the proposed schedule.

Develops a low-complexity greedy algorithm for practical parameter tuning.

Abstract

We consider the problem of multiple sensor scheduling for remote state estimation of multiple process over a shared link. In this problem, a set of sensors monitor mutually independent dynamical systems in parallel but only one sensor can access the shared channel at each time to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling in which each sensor makes transmission decisions based on both channel accessibility and distributed event-triggering conditions. The corresponding minimum mean squared error (MMSE) estimator is explicitly given. Considering information patterns accessed by sensor schedulers, time-based ones can be treated as a special case of the proposed one. By ultilizing realtime information, the proposed schedule outperforms the time-based ones in terms of the estimation quality. Resorting to solving an Markov decision process (MDP) problem with average cost criterion, we can find optimal parameters for the proposed schedule. As for practical use, a greedy algorithm is devised for parameter design, which has rather low computational complexity. We also provide a method to quantify the performance gap between the schedule optimized via MDP and any other schedules.