Event-Triggered Distributed Estimation With Inter-Event Information Retrieval

Distributed estimation has attracted great attention in the last few decades. In the problem of distributed estimation, a set of nodes estimate some parameter from noisy measurements. To leverage joint effort, the nodes communicate with each other in the estimation process. The communications consume bandwidth and energy resources, and these resources are often limited in real-world applications. To cope with the resources constraints, the event-triggered mechanism is proposed and widely adopted. It only allows signals to be transmitted if they carry significant amount of information. Various criteria of determining whether the information is significant lead to different trigger rules. With these rules, the resources can be saved. However, in the meanwhile, some inter-event information, not that important but still of certain use, is unavailable to the neighbors. The absence of these inter-event information may affect the algorithm performance. Considering this, in this paper, we come up with an inter-event information retrieval scheme to recover certain untransmitted information, which is the first work doing so to the best of our knowledge. We design an approach for inter-event information retrieval, and formulate and solve an optimization problem which has a closed-form solution to acquire information. With more information at hand, the performance degeneration caused by the event-triggered mechanism can be alleviated. We derive sufficient conditions for convergence of the overall algorithm. We also demonstrate the advantages of the proposed scheme by simulation experiments.