Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework

TL;DR

This paper develops an optimal sensor scheduling strategy for complex networked systems to improve distributed state estimation, considering communication delays and packet loss, with theoretical guarantees and computational simplifications.

Contribution

It reformulates sensor scheduling as a Markov decision process with coupled rewards and establishes conditions for optimal policies, including threshold-based solutions.

Findings

Optimal policies have a threshold structure.

Conditions for existence of stationary optimal policies are provided.

Simulation results demonstrate the effectiveness of the proposed approach.

Abstract

This paper investigates sensor scheduling for state estimation of complex networks over shared transmission channels. For a complex network of dynamical systems, referred to as nodes, a sensor network is adopted to measure and estimate the system states in a distributed way, where a sensor is used to measure a node. The estimates are transmitted from sensors to the associated nodes, in the presence of one-step time delay and subject to packet loss. Due to limited transmission capability, only a portion of sensors are allowed to send information at each time step. The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors. Under a distributed state estimation framework, this problem is reformulated as a Markov decision process, where the one-stage reward for each node is strongly coupled. The feasibility of the problem reformulation is ensured. In addition, an easy-to-check condition is established to guarantee the existence of an optimal deterministic and stationary policy. Moreover, it is found that the optimal policies have a threshold, which can be used to reduce the computational complexity in obtaining these policies. Finally, the effectiveness of the theoretical results is illustrated by several simulation examples.