English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper investigates sensor scheduling for state estimation of complexnetworks over shared transmission channels. For a complex network of dynamicalsystems, referred to as nodes, a sensor network is adopted to measure andestimate the system states in a distributed way, where a sensor is used tomeasure a node. The estimates are transmitted from sensors to the associatednodes, in the presence of one-step time delay and subject to packet loss. Dueto limited transmission capability, only a portion of sensors are allowed tosend information at each time step. The goal of this paper is to seek anoptimal sensor scheduling policy minimizing the overall estimation errors.Under a distributed state estimation framework, this problem is reformulated asa Markov decision process, where the one-stage reward for each node is stronglycoupled. The feasibility of the problem reformulation is ensured. In addition,an easy-to-check condition is established to guarantee the existence of anoptimal deterministic and stationary policy. Moreover, it is found that theoptimal policies have a threshold, which can be used to reduce thecomputational complexity in obtaining these policies. Finally, theeffectiveness of the theoretical results is illustrated by several simulationexamples.

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