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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15859 |
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| _version_ | 1866918100952154112 |
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| author | Sammangi, Harsha Jagatha, Aditya Bojja, Giridhar Reddy Liu, Jun |
| author_facet | Sammangi, Harsha Jagatha, Aditya Bojja, Giridhar Reddy Liu, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios Sammangi, Harsha Jagatha, Aditya Bojja, Giridhar Reddy Liu, Jun Cryptography and Security Artificial Intelligence 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. |
| title | Decentralized AI-driven IoT Architecture for Privacy-Preserving and Latency-Optimized Healthcare in Pandemic and Critical Care Scenarios |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15859 |