Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios
This addresses privacy and latency issues in healthcare IoT for pandemic and critical care scenarios, though it appears incremental as it builds on existing federated learning, blockchain, and edge computing approaches.
The paper tackles the problem of data privacy, delay, and security in centralized healthcare IoT systems by proposing a decentralized AI-driven architecture that combines federated learning, blockchain, and edge computing. Experimental results show transaction latency, energy consumption, and data throughput orders of magnitude lower than competitive cloud solutions.
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.