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Robust Online Modeling of Counts in Agent Networks
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SYSNO ASEP 0570900 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Robust Online Modeling of Counts in Agent Networks Author(s) Žemlička, R. (CZ)
Dedecius, Kamil (UTIA-B) RID, ORCIDNumber of authors 2 Source Title IEEE Transactions on Signal and Information Processing over Networks - ISSN 2373-776X
Roč. 9, č. 1 (2023), s. 217-228Number of pages 12 s. Publication form Online - E Language eng - English Country US - United States Keywords Diffusion ; Distributed estimation ; Poisson regression Subject RIV IN - Informatics, Computer Science OECD category Automation and control systems Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000975099100002 EID SCOPUS 85153355712 DOI 10.1109/TSIPN.2023.3264990 Annotation Many real-world processes of interest produce nonnegative integer values standing for counts. For instance, we count packets in computer networks, people in monitored areas, or particles incident on detectors. Often, the ultimate goal is the modeling of these counts. However, standard techniques are computationally demanding and sensitive to the amount of available information. In our quest to solve the objective, we consider two prominent features of the contemporary world: online processing of streaming data, and the rapidly evolving ad-hoc agent networks. We propose a novel algorithm for a collaborative online estimation of the zero-inflated Poisson mixture models in diffusion networks. Its main features are low memory and computational requirements, and the capability of running in inhomogeneous networks. There, the agents possibly observe different processes, and locally decide which of their neighbors provide useful information. Two simulation examples demonstrate that the algorithm attains good stability and estimation performance even under slowly varying parameters. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2024 Electronic address https://ieeexplore.ieee.org/document/10093992
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