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Machine Learning Theory and Practice, 2026, 6(1); doi: 10.38007/ML.2026.060108.

Analysis of the Current Status of IoT Technology Evolution and Typical Application Scenarios for Smart Healthcare and Industrial Interconnection

Author(s)

Dexi Chen

Corresponding Author:
Dexi Chen
Affiliation(s)

School of Computer and Big Data, Jining Normal University, Ulanqab 012000, Inner Mongolia, China

Abstract

The Internet of Things (IoT) is a crucial infrastructure connecting the physical world and digital systems, evolving from one-way sensing and remote monitoring to comprehensive sensing, transmission, computing, decision-making, and services. This paper, focusing on the current state of IoT technology development and typical application scenarios, further narrows its scope to the technological evolution, architectural bottlenecks, and application implementation of smart healthcare and the Industrial Internet. After reviewing English literature and publicly available statistics from the past three years, this paper systematically discusses the latest developments in IoT in terms of connection scale, architecture, edge intelligence, 5G integration, and industry penetration. Key issues are raised from the perspectives of heterogeneous access, real-time limitations, data governance, security and privacy, standard fragmentation, and operational complexity, and an application strategy for various scenarios is proposed. The research indicates that future IoT development should not only focus on increasing the number of connections but also on trusted interconnection, edge-cloud collaboration, scenario closed loops, and value computability to form a high-quality development model.

Keywords

Internet of Things; Smart Healthcare; Industrial Internet; Edge Computing; 5G Convergence; Data Governance

Cite This Paper

Dexi Chen. Analysis of the Current Status of IoT Technology Evolution and Typical Application Scenarios for Smart Healthcare and Industrial Interconnection. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 68-76. https://doi.org/10.38007/ML.2026.060108.

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