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.

本文研究了共享传输信道上复杂网络状态估计的传感器调度。对于动态系统的复杂网络，称为节点，采用传感器网络以分布式方式测量和估计系统状态，其中传感器用于测量节点。估计值从传感器传输到相关节点，存在一步时间延迟并且可能会丢失数据包. 由于传输能力有限，每个时间步长只允许一部分传感器发送信息。本文的目标是寻求一种最优的传感器调度策略，以最小化整体估计误差。在分布式状态估计框架下，这个问题被重新表述为马尔可夫决策过程，其中每个节点的单阶段奖励是强耦合的。保证了问题重构的可行性。此外，还建立了一个易于检查的条件来保证最优确定性和平稳策略的存在。此外，还发现最优策略有一个阈值，可用于降低获取这些策略的计算复杂度。最后通过几个仿真实例说明了理论结果的有效性。