In this paper we carry out a stability analysis of a distributed consensus algorithm in presence of link failures. The algorithm combines a new broadcast version of a Push-Sum algorithm, specifically designed for handling link failures, with a new recursive consensus filter. The analysis is based on the properties of random Laplacian matrices and random sub-graphs and it may also be relevant for other distributed estimation problems. We characterize the convergence speed, the minimum number of consensus steps needed and the impact of link failures and for both the broadcast Push-Sum and the recursive consensus algorithms. Numerical simulations validate the theoretical analysis.In this paper we carry out a stability analysis of a distributed consensus algorithm in presence of link failures. The algorithm combines a new broadcast version of a Push-Sum algorithm, specifically designed for handling link failures, with a new recursive consensus filter. The analysis is based on the properties of random Laplacian matrices and random sub-graphs and it may also be relevant for other distributed estimation problems. We characterize the convergence speed, the minimum number of consensus steps needed and the impact of link failures and for both the broadcast Push-Sum and the recursive consensus algorithms. Numerical simulations validate the theoretical analysis.

Consensus analysis of random sub-graphs for distributed filtering with link failures / Battilotti, Stefano; Cacace, Filippo; D'Angelo, Massimiliano. - In: IEEE TRANSACTIONS ON AUTOMATIC CONTROL. - ISSN 0018-9286. - 69:4(2024), pp. 2476-2483. [10.1109/TAC.2023.3311709]

Consensus analysis of random sub-graphs for distributed filtering with link failures

Stefano Battilotti
;
2024

Abstract

In this paper we carry out a stability analysis of a distributed consensus algorithm in presence of link failures. The algorithm combines a new broadcast version of a Push-Sum algorithm, specifically designed for handling link failures, with a new recursive consensus filter. The analysis is based on the properties of random Laplacian matrices and random sub-graphs and it may also be relevant for other distributed estimation problems. We characterize the convergence speed, the minimum number of consensus steps needed and the impact of link failures and for both the broadcast Push-Sum and the recursive consensus algorithms. Numerical simulations validate the theoretical analysis.In this paper we carry out a stability analysis of a distributed consensus algorithm in presence of link failures. The algorithm combines a new broadcast version of a Push-Sum algorithm, specifically designed for handling link failures, with a new recursive consensus filter. The analysis is based on the properties of random Laplacian matrices and random sub-graphs and it may also be relevant for other distributed estimation problems. We characterize the convergence speed, the minimum number of consensus steps needed and the impact of link failures and for both the broadcast Push-Sum and the recursive consensus algorithms. Numerical simulations validate the theoretical analysis.
2024
Filtering; Network analysis; Stochastic systems; random graphs
01 Pubblicazione su rivista::01a Articolo in rivista
Consensus analysis of random sub-graphs for distributed filtering with link failures / Battilotti, Stefano; Cacace, Filippo; D'Angelo, Massimiliano. - In: IEEE TRANSACTIONS ON AUTOMATIC CONTROL. - ISSN 0018-9286. - 69:4(2024), pp. 2476-2483. [10.1109/TAC.2023.3311709]
File allegati a questo prodotto
File Dimensione Formato  
Battilotti_Consensus_2023.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 696.55 kB
Formato Adobe PDF
696.55 kB Adobe PDF   Contatta l'autore
Battilotti_preprint_Consensu_2023.pdf

accesso aperto

Note: DOI: 10.1109/TAC.2023.3311709
Tipologia: Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza: Creative commons
Dimensione 657.87 kB
Formato Adobe PDF
657.87 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1687392
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact