去中心化AI物联网架构实现疫情重症监护中隐私与低延迟兼顾
Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios
- 结合联邦学习、区块链与边缘计算构建去中心化系统
- 交易延迟、能耗和吞吐量较云端方案降低数个数量级
- 适合高敏感性医疗场景下的实时监测与隐私保护
当前集中式医疗架构在数据隐私、延迟和安全方面存在诸多问题。本文提出一种AI驱动的去中心化物联网架构，适用于疫情及重症监护场景。该架构融合联邦学习、区块链与边缘计算，显著提升数据隐私保护能力，降低延迟，并优化系统整体性能。实验结果表明，相较于现有云解决方案，本方案在交易延迟、能耗和数据吞吐量方面均实现数量级降低，有效支撑实时患者监控需求。
原文摘要 · Abstract (English)
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.
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