A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks

Autor(es)

Tiglea, Daniel G. ^[1] ; Candido, Renato ^[1] ; Silva, Magno T. M. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Elect Syst Engn Dept, Escola Politecn, BR-05508010 Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE TRANSACTIONS ON SIGNAL PROCESSING; v. 69, p. 58-72, 2021.
• Citações Web of Science: 0

Resumo

Distributed signal processing has attracted widespread attention in the scientific community due to its several advantages over centralized approaches. Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area. Both in the classical framework and in graph signal processing, sampling and censoring techniques have been topics of intense research, since the cost associated with measuring, processing and/or transmitting data throughout the entire network may be prohibitive in certain applications. In this paper, we propose a low-cost adaptive mechanism for sampling and censoring over diffusion networks that uses information from more nodes when the error in the network is high and from less nodes otherwise. It presents fast convergence during transient and a significant reduction in computational cost and energy consumption in steady state. As a censoring technique, we show that it is able to noticeably outperform other solutions. We also present a theoretical analysis to give insights about its operation, and to help the choice of suitable values for its parameters. (AU)

• Processo FAPESP: 17/20378-9 - Filtros adaptativos e aprendizagem de máquina: aplicações em imagens, comunicações e voz
• Beneficiário: Magno Teófilo Madeira da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Regular