The adoption of decentralized architectures for health data management offers benefits including patient data sovereignty and elimination of single points of failure, but introduces questions about network overhead compared to traditional centralized systems. This paper presents a network overhead analysis comparing Firebase Real-Time Database with IPFS-based storage via Pinata for mobile health data transmission. We implemented an Android application that collects physiological data from wearable devices and transmits this information to both backends using REST APIs. Our experimental evaluation across eight transmission scales reveals that Firebase demonstrates lower fixed overhead and latency for small payloads, while Pinata exhibits superior scaling characteristics for larger data volumes. A crossover point occurs around 50 records per payload, beyond which the decentralized architecture transmits less total data than the centralized alternative. The results indicate that neither architecture maintains uniform efficiency across all operational scales, with architectural choice depending on expected transaction patterns in the deployment context.
Franco, F., Bogliolo, A., Montagna, S., Bedogni, L., Ferretti, S. (2026). Network Efficiency of Centralized and Decentralized Health Data Systems. 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/CCNC65079.2026.11366486].
Network Efficiency of Centralized and Decentralized Health Data Systems
Bogliolo A.;Montagna S.;Bedogni L.;Ferretti S.
2026
Abstract
The adoption of decentralized architectures for health data management offers benefits including patient data sovereignty and elimination of single points of failure, but introduces questions about network overhead compared to traditional centralized systems. This paper presents a network overhead analysis comparing Firebase Real-Time Database with IPFS-based storage via Pinata for mobile health data transmission. We implemented an Android application that collects physiological data from wearable devices and transmits this information to both backends using REST APIs. Our experimental evaluation across eight transmission scales reveals that Firebase demonstrates lower fixed overhead and latency for small payloads, while Pinata exhibits superior scaling characteristics for larger data volumes. A crossover point occurs around 50 records per payload, beyond which the decentralized architecture transmits less total data than the centralized alternative. The results indicate that neither architecture maintains uniform efficiency across all operational scales, with architectural choice depending on expected transaction patterns in the deployment context.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
