Academic Journal
Q1IEEE Transactions on Industrial Informatics
About IEEE Transactions on Industrial Informatics
IEEE Transactions on Industrial Informatics is a scholarly journal published by IEEE Computer Society. SCImago 2025 places it in Q1 with an SJR of 2.983 and an H-index of 239.
Its listed coverage is 2005-2026 and its research categories include Computer Science Applications (Q1); Control and Systems Engineering (Q1); Electrical and Electronic Engineering (Q1); Information Systems (Q1). The 2025 dataset reports 965 documents and 39382 citations across the latest three-year reporting window.
About IEEE Transactions on Industrial Informatics
IEEE Transactions on Industrial Informatics is a leading peer-reviewed journal published by the Institute of Electrical and Electronics Engineers (IEEE), focused on cutting-edge research in the field of industrial informatics. This prestigious journal serves as a critical platform for scientists, researchers, and industry professionals who are shaping the future of smart manufacturing, cyber-physical systems, and the Industrial Internet of Things (IIoT).
What is Industrial Informatics?
Industrial informatics is an interdisciplinary domain that merges information technology with industrial engineering to enhance productivity, efficiency, and automation. It plays a vital role in the transformation of conventional industries into intelligent, interconnected systems, paving the way for Industry 4.0 and beyond.
Scope and Topics Covered
IEEE Transactions on Industrial Informatics publishes high-impact research articles, technical notes, and surveys covering a wide range of topics, including but not limited to:
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Industrial automation and control
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Cyber-physical systems (CPS)
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Intelligent and adaptive systems
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Industrial wireless sensor networks
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Embedded systems in industrial applications
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Data analytics and machine learning for industry
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Smart grids and energy informatics
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Human-machine interfaces
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Real-time systems and edge computing
By covering such diverse topics, the journal provides a comprehensive look into how informatics is revolutionizing modern industry.
Who Should Read This Journal?
This journal is essential reading for:
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Academic researchers in industrial engineering and computer science
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Engineers and professionals in manufacturing and automation
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Developers and architects of embedded and cyber-physical systems
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Policy makers and industry leaders exploring digital transformation
Whether you are conducting academic research or implementing advanced informatics solutions in an industrial setting, this journal provides authoritative insights and peer-reviewed studies to support innovation.
Importance in the Research Community
IEEE Transactions on Industrial Informatics is widely recognized for its high impact factor and global reach. It is indexed in leading scientific databases such as Scopus, Web of Science, and IEEE Xplore, making it a trusted source of information for institutions and professionals worldwide.
Its rigorous peer-review process ensures the publication of high-quality, original research that addresses real-world industrial challenges through intelligent systems and technologies.
Submission and Access
Researchers and scholars interested in publishing in the journal can find detailed submission guidelines on the IEEE Xplore digital library. Articles are available through open access and institutional subscriptions, facilitating wide dissemination and engagement.
Journal Metrics
Metrics can change by reporting year. Verify time-sensitive values with the publisher or indexing service.
Aims & Scope
Scope of IEEE Transactions on Industrial Informatics: Advancing the Frontier of Intelligent Industrial Systems
The IEEE Transactions on Industrial Informatics (TII) is a premier peer-reviewed journal that publishes cutting-edge research at the intersection of information technology and industrial applications. As industrial systems evolve into more intelligent, interconnected, and autonomous entities, the role of industrial informatics becomes increasingly vital. This journal serves as a leading platform for disseminating innovations that bridge the gap between theory and practice in industrial information technologies.
Core Focus Areas
The scope of IEEE Transactions on Industrial Informatics spans a wide range of topics essential to the next generation of industrial automation and intelligent systems. Key focus areas include:
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Industrial Cyber-Physical Systems (CPS): Integration of computational algorithms and physical components to create systems that can monitor, control, and optimize industrial processes in real-time.
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Internet of Things (IoT) in Industry: Applications of IoT technologies for smart manufacturing, predictive maintenance, asset tracking, and system optimization in industrial environments.
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Cloud and Edge Computing: Research related to scalable, low-latency computing architectures that support industrial processes with real-time decision-making and data analytics.
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Artificial Intelligence (AI) and Machine Learning (ML): Use of AI/ML techniques to enhance automation, process control, quality assurance, and fault detection in industrial systems.
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Data Analytics and Big Data in Industry: Methods for collecting, processing, and analyzing massive datasets to improve operational efficiency, supply chain management, and strategic planning.
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Human-Machine Interfaces (HMI) and Augmented Reality (AR): Development of advanced user interfaces that enhance human interaction with complex industrial systems.
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Security and Privacy in Industrial Systems: Research on safeguarding critical infrastructure from cyber threats, ensuring secure data transmission, and protecting sensitive industrial information.
Application Domains
The journal welcomes contributions from a broad array of industrial sectors, including but not limited to:
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Smart manufacturing and Industry 4.0
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Energy systems and smart grids
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Intelligent transportation systems
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Healthcare informatics in industrial settings
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Robotics and autonomous systems
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Industrial supply chains and logistics
Audience and Impact
With a strong emphasis on innovation, applicability, and theoretical foundations, IEEE Transactions on Industrial Informatics is an essential resource for researchers, engineers, and practitioners in both academia and industry. Its high impact factor and rigorous peer-review process underscore the journal’s commitment to quality and relevance in the evolving field of industrial informatics.
Why Publish in IEEE TII?
Publishing in IEEE Transactions on Industrial Informatics offers significant visibility, with a global readership and a strong citation record. The journal is indexed in major databases such as Scopus, Web of Science, and IEEE Xplore, making it a powerful platform to disseminate impactful research.
Recent Research Articles
Latest publications matched automatically by ISSN.
Distributed Predefined-Time Control for Islanded AC Microgrid Under FDI Attacks
Houjun Liang, Bo Li, Xiaowei Jiang, Shun Xu et al.
2026-09 · DOI: 10.1109/tii.2026.3693673Dyadic Conflicting Control Design for Cyber-Physical Systems: Applications to Variable Cycle Engines
Sirong Lu, Muxuan Pan, Ye-Hwa Chen, Ying Han et al.
2026-09 · DOI: 10.1109/tii.2026.3691803Multigranularity Information Fusion for Multimodal Retrieval in Prefabricated Construction
Yinyi Wei, Xiao Li, Yanchen Liu, Ziyang Lin et al.
2026-09 · DOI: 10.1109/tii.2026.3692747From Signals to Semantics: A Multilabel Power Quality Disturbance Identification Framework Using Large Language Models
Lixian Shi, Qiushi Cui, Yigong Zhang, Lida Shi et al.
2026-09 · DOI: 10.1109/tii.2026.3692514Prompt-Guided Disentanglement and Fusion Framework for Cross-Domain Fault Diagnosis With Class Mismatch
Zuoyi Chen, Jun Wu, Hong-Zhong Huang
2026-09 · DOI: 10.1109/tii.2026.3690427Boosting Trimmed Tree With Inner Boundary for Network Resilience-Oriented Connectivity Improvement
Wei Wei, Hui Guo, Peng Li, Tao Ma et al.
2026-09 · DOI: 10.1109/tii.2026.3690809Multiview Graph Learning for Mental Stress Recognition in Human–Machine Interaction
Qunli Yao, Heng Gu, Shaodi Wang, Xiaoli Li et al.
2026-09 · DOI: 10.1109/tii.2026.3692209Quantifying Predictive Uncertainty in Hybrid Deep Learning Architectures for Multi-Sensor Forecasting of Nuclear Reactor Cooling Systems
Ibnu Hajar, Murizah Kassim, Mohd Sabri Minhat, Syamsir Abduh et al.
2026-09 · DOI: 10.1109/tii.2026.3693452Generative AI for Open-Set Event Detection and Classification in Power Systems Using Synchrophasor Data
Yi Hu, Zheyuan Cheng
2026-09 · DOI: 10.1109/tii.2026.3690931PoCo: Joint Optimization of Power and Cooling for Low-Carbon CCHP-Integrated Data Centers
Yongyi Ran, Huisheng Liu, Shuangwu Chen, Jiangtao Luo et al.
2026-09 · DOI: 10.1109/tii.2026.3692684Memristor-Based Immune Neural Network Circuit With Neuromorphic Learning and Self-Repairing and its Application in Industrial Fault Diagnosis
Kefan Tao, Yanfeng Wang, Junwei Sun
2026-09 · DOI: 10.1109/tii.2026.3689313Safe Optimal Formation of Continuous-Time Multiagent Systems via Maximum Entropy Adaptive Dynamic Programming
Lulu Zhang, Huaguang Zhang, Tianbiao Wang, Jiawei Ma et al.
2026-09 · DOI: 10.1109/tii.2026.3688810Spatio-Temporal Decay Factor Driven Robust Ant Colony Optimization Algorithm for Time-Dependent Industrial Product Green Vehicle Routing Problem With Time Windows
Ying Hou, Haoyang Jiao, Honggui Han, Yongping Du et al.
2026-09 · DOI: 10.1109/tii.2026.3693788A Dynamic Game Chain Linkage Strategy for Trusted Resource Sharing With Interval Matching
Wei Zhou, Jingang Lai, Zhigang Zeng
2026-09 · DOI: 10.1109/tii.2026.3689928DMMCNet: Dynamic Multiscale and Multilevel Contrastive Point Cloud Feature Extraction Network for Industrial Defect Detection
Wenhui Chen, Jirui Liu, Zhenyan Ji, Yuezeng Song et al.
2026-09 · DOI: 10.1109/tii.2026.3689660A Physics-Informed Residual Learning Method for Real-Time 5-DoF Magnetic Localization in Capsule Endoscopy
Miaozhang Shen, Shuxiang Guo, Zixu Wang, Yuyue Yang et al.
2026-09 · DOI: 10.1109/tii.2026.3688686Type-2 Fuzzy Broad Echo State Learning System for Carbon Efficiency Prediction in Iron Ore Sintering Process
Jie Hu, Xinyu Qiu, Fan Yang, Min Wu et al.
2026-09 · DOI: 10.1109/tii.2026.3689110IEEE Transactions on Industrial Informatics Information for Authors
2026-09 · DOI: 10.1109/tii.2026.3723627Polynomial-Empowered Secure Gain-Scheduling Scheme for Minecart Active Suspension Systems Under Physical-Layer Authentication
Cong Wei, Xiang-Peng Xie, Ju H. Park
2026-09 · DOI: 10.1109/tii.2026.3693160Power Resource Planning for Cellular Base Stations in Distribution Networks Toward Renewable Transitions
Pei Yong, Zhifang Yang
2026-09 · DOI: 10.1109/tii.2026.3694024Reviews
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April 22, 2025 at 3:55 am
April 22, 2025