AI-Powered IoT (AIoT) Systems: Advancements in Security, Sustainability, and Intelligence
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 27
Special Issue Editor
2. School of Computer Science and Technology, Algoma University, Sault Ste. Marie, ON P6A 2G4, Canada
Interests: artificial intelligence of things (AIoT); generative internet of things (GIoT); cybersecurity; federated learning; internet of medical things (IoMT); healthcare security
Special Issue Information
Dear Colleagues,
The rapid convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionizing how connected systems sense, learn, and act autonomously across diverse environments. This Special Issue (SI) highlights the latest research and advancements in AI-powered IoT (AIoT) systems that enable intelligent decision-making, real-time analytics, and adaptive automation. It welcomes contributions that address the integration of machine learning, deep learning, and generative AI with IoT infrastructures to improve scalability, resilience, and energy efficiency. In addition, this SI underscores the growing importance of Responsible AI and Explainable AI (XAI) to ensure fairness, transparency, accountability, and trustworthiness within AIoT ecosystems. Submissions that explore how ethical frameworks, interpretable models, and human-in-the-loop approaches can guide secure, privacy-preserving, and sustainable IoT deployments are particularly encouraged. Overall, the goal is to provide a comprehensive view of how AI-driven intelligence can transform IoT networks into adaptive, ethical, and context-aware systems that benefit society and industry alike. The topics can include, but are not limited to, the following:
- Federated and distributed learning for IoT devices;
- Generative AI for adaptive IoT security and optimization;
- Responsible and explainable AI frameworks for IoT applications;
- Human-in-the-loop and interpretable decision systems for IoT;
- AI-driven energy management in IoT and smart grids;
- Privacy-preserving data aggregation and edge intelligence;
- Reinforcement learning for autonomous IoT systems;
- AI-enabled predictive maintenance in Industry 4.0;
- Trust, authentication, and blockchain integration in AIoT;
- Lightweight deep learning models for constrained IoT devices;
- LLM-based context understanding in smart environments;
- Ethical and sustainable AIoT system design.
Dr. Yazan Otoum
Guest Editor
Manuscript Submission Information
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Keywords
- AIoT
- internet of things
- artificial intelligence
- edge computing
- federated learning
- responsible AI
- explainable AI
- smart systems
- cybersecurity
- privacy preservation
- deep learning
- generative AI
- edge–cloud collaboration
- ethical AI
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