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The integration of Embedded Edge-AI with System-on-Chip (SoC)-based Internet of Things (IoT) devices has emerged as a transformative approach to energy-efficient smart home automation. Rising energy demands and the need for real-time decision making have positioned this field at the forefront of innovation. This study systematically analyzed global research trends and collaborative patterns surrounding edge-AI-enabled smart homes using scientometric methods. A dataset of 529 publications (2015–2025) was retrieved from Scopus and complementary databases. Bibliometric techniques, including co-authorship, keyword co-occurrence, and institutional clustering, were applied using VOS viewer, CiteSpace, and Bibliometrix. The results show exponential growth in publications since 2023, with India, China, and Korea leading contributions, and keywords such as energy efficiency, smart homes, IoT, and reinforcement learning-dominated research clusters. Key journals include IEEE Internet of Things Journal and Future Generation Computer Systems, while collaborations show increasing cross-country networks. The findings highlight opportunities in demand response and predictive energy optimization, while addressing the challenges of interoperability and security. This study maps the field and offers insights into researchers and stakeholders to advance intelligent home automation.

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Embedded Edge-AI Architecture for Energy-Efficient Smart Home Automation Using SoC-Based IoT Devices: Scientometric Review

  • 会议论文

  • 发表日期：

    2026年

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  • R. Archana Reddy,
  • B. Sathyavani,
  • G. Swamy Reddy,
  • Shirisha Balle

摘要

The integration of Embedded Edge-AI with System-on-Chip (SoC)-based Internet of Things (IoT) devices has emerged as a transformative approach to energy-efficient smart home automation. Rising energy demands and the need for real-time decision making have positioned this field at the forefront of innovation. This study systematically analyzed global research trends and collaborative patterns surrounding edge-AI-enabled smart homes using scientometric methods. A dataset of 529 publications (2015–2025) was retrieved from Scopus and complementary databases. Bibliometric techniques, including co-authorship, keyword co-occurrence, and institutional clustering, were applied using VOS viewer, CiteSpace, and Bibliometrix. The results show exponential growth in publications since 2023, with India, China, and Korea leading contributions, and keywords such as energy efficiency, smart homes, IoT, and reinforcement learning-dominated research clusters. Key journals include IEEE Internet of Things Journal and Future Generation Computer Systems, while collaborations show increasing cross-country networks. The findings highlight opportunities in demand response and predictive energy optimization, while addressing the challenges of interoperability and security. This study maps the field and offers insights into researchers and stakeholders to advance intelligent home automation.