Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios

TL;DR

This paper proposes a decentralized AI-driven IoT architecture for healthcare that enhances privacy and reduces latency during pandemics and critical care, outperforming traditional cloud solutions.

Contribution

It introduces a novel decentralized architecture integrating federated learning, blockchain, and edge computing for healthcare IoT applications.

Findings

Lower transaction latency compared to cloud solutions

Reduced energy consumption in data processing

Higher data throughput in decentralized setup

Abstract

AI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture that can address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximizing data privacy, minimizing latency, and improving other general system metrics. Experimental results demonstrate transaction latency, energy consumption, and data throughput orders of magnitude lower than competitive cloud solutions.