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A Multidimensional Comparative Survey of Hybrid Cloud–IoT Architectures for Healthcare: Toward a Fog–Mesh Synergistic Framework
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AI
DOI：10.1109/jiot.2026.3698346.png)
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Abstract
En 中文
Hybrid cloud–Internet of Things (IoT) architectures are transforming healthcare by enhancing data management, improving patient outcomes, and enabling real-time decision-making. This article presents a comprehensive review of the current state and future prospects of hybrid cloud–IoT architectures in healthcare. Our study identifies key trends, such as the increasing adoption of edge computing to reduce latency, the integration of AI for predictive analytics, and the emphasis on robust security measures to protect sensitive patient data. We address significant challenges, including data interoperability, scalability issues, and privacy concerns, and offer potential solutions and best practices. Furthermore, we provide a taxonomy-driven comparative framework evaluating five architectural paradigms—edge, fog, mesh, hub-and-spoke, and serverless—using healthcare-specific performance indicators such as cost, scalability, latency, security, and data confidentiality. Unlike prior studies, we synthesize existing research to produce a design recommendation. We propose a hybrid fog–mesh model that targets low latency, security, and scalability for healthcare delivery. This study links architectural theory to deployment practice. It focuses on dynamic virtual machine (VM) selection and allocation, latency-aware computing, and data sovereignty. It also outlines key directions for future research to guide the implementation and optimization of hybrid cloud–IoT architectures in healthcare.
Keywords:
Artificial intelligence
cloud computing
cloud–Internet of Things (IoT)
healthcare
literature review
virtual machine (VM) selection
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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