ViSPAC: NEWS2-Guided adaptive prioritization and compression in an Edge–Fog–Cloud cycle evaluated on AWS

• Mateus Roveda Unisinos
• Rodrigo da Rosa Righi Unisinos

DOI: https://doi.org/10.5753/pesquisanuvem.2026.21287

Abstract

Remote monitoring of vital signs generates continuous data streams that frequently overload low-cost edge devices and networks. This paper presents ViSPAC, a closed-loop Edge–Fog–Cloud model that uses the National Early Warning Score 2 (NEWS2) to dynamically adapt sampling and compression parameters at the edge based on patient risk. Evaluated in a distributed Amazon Web Services (AWS) environment, ViSPAC reduced transmissions by 96.7%, achieved an 81.6% average compression rate, preserved clinical fidelity (global Percent Root-mean-square Difference – PRD 1.16%), and kept the feedback-loop latency around 1.05 s.

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