Bibliometric analysis of secure IoT for quantum computing

초록

The convergence of Quantum Machine Learning (QML) and Blockchain is emerging as a transformative paradigm to address escalating security and scalability challenges in 6G-enabled Industrial Internet of Things (IIoT) networks. This study presents the first comprehensive bibliometric and meta-analysis of this nascent interdisciplinary field. We analyzed 159 peer-reviewed publications (indexed from January 2022 through December 22, 2024) from Scopus, employing a systematic Kitchenham-based methodology for literature selection and VOSviewer for science mapping. Our analysis reveals a 75% annual growth rate since 2022, with India (37.7%), the USA (12.6%), and South Korea (12.6%) as the leading contributors. Keyword co-occurrence analysis identified four dominant thematic clusters: "6G Network Security," "Quantum Computing and AI," "Blockchain and Decentralization," and "IIoT Applications." The study's novelty lies in synthesizing bibliometric insights with a proposed five-layer QML-Blockchain integration framework and a comparative analysis against existing reviews. Quantitative performance metrics indicate that QML can improve anomaly detection accuracy by 5-9% over classical models, while advanced consensus mechanisms like PoA2 can reduce transaction latency by 35%. However, significant challenges persist, including quantum hardware limitations (e.g., qubit coherence <100 mu s), scalability challenges in achieving consensus across massive IIoT device densities, and a critical lack of empirical testbeds. This research provides a foundational roadmap, emphasizing the urgent need for standardized benchmarks, hybrid orchestration models, and quantum-resistant cryptography to realize secure, intelligent, and autonomous IIoT ecosystems in the 6G era.

키워드