Robust Online Modeling of Counts in Agent Networks

0570900 - ÚTIA 2024 RIV US eng J - Journal Article
Žemlička, R. - Dedecius, Kamil
Robust Online Modeling of Counts in Agent Networks.
IEEE Transactions on Signal and Information Processing over Networks. Roč. 9, č. 1 (2023), s. 217-228. ISSN 2373-776X. E-ISSN 2373-776X
Institutional support: RVO:67985556
Keywords : Diffusion * Distributed estimation * Poisson regression
OECD category: Automation and control systems
Impact factor: 3.2, year: 2022
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2023/AS/dedecius-0570900.pdf https://ieeexplore.ieee.org/document/10093992

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.
Permanent Link: https://hdl.handle.net/11104/0342468