Compartir
Título
Convergence of Distributed Flooding and Its Application for Distributed Bayesian Filtering
Autor(es)
Materia
Computer Science
Fecha de publicación
2016
Editor
Institute of Electrical and Electronics Engineers (IEEE)
Citación
IEEE Transactions on Signal and Information Processing over Networks . Volumen PP (99), pp. 1-1. Institute of Electrical and Electronics Engineers (IEEE).
Resumen
Distributed flooding is a fundamental information sharing method to get network consensus via peer-to-peer communication. However, a unified consensus-oriented formulation of the algorithm and its convergence performance are not yet explicitly available in the literature. To fill this void in this paper, set-theoretic flooding rules are defined by encapsulating the information of interest in finite sets (one set per node), namely distributed set-theoretic information flooding (DSIF). This leads to a new type of consensus referred to as ”collecting consensus” which aims to ensure that all nodes get the same information. Convergence and optimality analyses are provided based on a consistent measure of the degree of consensus (DoC) of the network. Compared with the prevailing averaging consensus, the proposed DSIF protocol benefits from avoiding repeated use of any information and offering the highest converging efficiency for network consensus while being exposed to increasing node-storage requirements against communication iterations and higher communication load. The protocol has been advocated for distributed nonlinear Bayesian filtering, where each node operates a separate particle filter, and the collecting consensus is pursued on the sensor data alone or jointly with intermediate local estimates. Simulations are provided in detail to demonstrate the theoretical findings.
URI
ISSN
2373-776X
Colecciones
- BISITE. Artículos [290]
Ficheros en el ítem
Nombre:
Tamaño:
1.771Mb
Formato:
Adobe PDF