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AI-Powered IoT (AIoT) Systems: Advancements in Security, Sustainability, and Intelligence
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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 2027 | Viewed by 3102

Editor

Dr. Yazan Otoum

E-Mail Website
Guest Editor
1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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 Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (3 papers)

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Research

23 pages, 705 KB  
Article
LLM-SGCF: A Robust Malware Detection Framework with Spatially Guided Convolution
by Lina Zhao, Hua Huang, Ning Li, Yunxiao Wang and Ming Li
Computers 2026, 15(6), 329; https://doi.org/10.3390/computers15060329 - 22 May 2026
Viewed by 469
Abstract
With the rapid evolution of cyberattack techniques, identifying dynamic behavioral intents from Application Programming Interface call sequences has become a fundamental modality for ensuring reliable malware detection and information security. However, existing detection methods face the dual challenges of semantic sparsity and inadequate [...] Read more.
With the rapid evolution of cyberattack techniques, identifying dynamic behavioral intents from Application Programming Interface call sequences has become a fundamental modality for ensuring reliable malware detection and information security. However, existing detection methods face the dual challenges of semantic sparsity and inadequate spatial dependency modeling when processing these sequences, which fundamentally undermines their stability against complex structural variations and in-the-wild evasive patterns. To address these critical vulnerabilities, we propose LLM-SGCF, a highly effective malware detection framework that jointly models deep behavioral semantics and spatial structures. Specifically, our framework leverages generative Large Language Models, which are subsequently encoded by BERT, to transform sparse API calls into rich and contextualized descriptions. Concurrently, it employs a novel Spatially Guided Convolution (SGC) module to localize critical malicious segments and extract cross-position dependencies in a two-dimensional semantic space. Extensive experiments on the public Aliyun and Catak datasets demonstrate that LLM-SGCF exhibits exceptional resilience to real-world structural complexity and significantly outperforms state-of-the-art baselines, achieving a peak binary-classification accuracy of 95.82%. Further ablation analyses confirm that the synergistic fusion of semantic enhancement driven by Large Language Models and spatial structural modeling dramatically improves the resilience of the framework against complex attack chains, providing a highly reliable paradigm for next-generation malware recognition systems. Full article
(This article belongs to the Special Issue AI-Powered IoT (AIoT) Systems: Advancements in Security, Sustainability, and Intelligence)
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31 pages, 7250 KB  
Article
Enhancing IoT Network Security: A BPSO-Optimized Attention-GRU Deep Learning Framework for Intrusion Detection
by Abdallah Elayan and Michel Kadoch
Computers 2026, 15(5), 266; https://doi.org/10.3390/computers15050266 - 23 Apr 2026
Cited by 1 | Viewed by 526
Abstract
The exponential expansion of computer networks, alongside the rapid development of the Internet of Things (IoT), has significantly increased the volume and complexity of transmitted data, emphasizing the need for robust network security measures to secure sensitive data and prevent unauthorized access or [...] Read more.
The exponential expansion of computer networks, alongside the rapid development of the Internet of Things (IoT), has significantly increased the volume and complexity of transmitted data, emphasizing the need for robust network security measures to secure sensitive data and prevent unauthorized access or breaches. Intrusion Detection Systems (IDSs) have emerged as a vital tool for protecting networks and IoT environments from threats. Various IDSs have been proposed in the literature; however, the lack of optimal feature learning, computational efficiency, and reliance on obsolete datasets poses significant challenges, limiting their effectiveness against evolving cyber threats. Moreover, traditional IDSs struggle to efficiently manage the high-dimensional and imbalanced nature of IoT network traffic data. To address these challenges, this research proposes a hybrid deep learning (DL)-based IDS integrating Binary Particle Swarm Optimization (BPSO), MultiHead Attention mechanisms (MHA), and a deep Gated Recurrent Unit (GRU) architecture, improving detection effectiveness while reducing computational overhead. Our proposed approach also utilizes a Target Sampling strategy to balance class distributions, enhancing the model’s ability to accurately identify minority attacks. The BPSO algorithm is employed to identify the most influential features from the high-dimensional network traffic datasets, enhancing model interpretability and supporting more efficient learning. This optimized feature subset is then fed into a GRU-based DL architecture augmented with MHA, which performs sequence processing and attention-based learning for intrusion detection. The performance of the proposed model is evaluated utilizing the BoT-IoT and the CIC-IDS2017 benchmark datasets, ensuring a comprehensive assessment of anomaly detection capabilities. Extensive experimental results demonstrate the superior performance of the proposed model, achieving a recall of 98.42% and 99.76%, with F1-score of 98.94% and 99.76% for binary classification and a recall of 99.79% and 98.69%, with F1-score of 99.89% and 98.04% for multiclass classification on the BoT-IoT and CIC-IDS2017 datasets, respectively, highlighting the effectiveness of our model in enhancing threat detection for computer networks and IoT environments in comparison to recent state-of-the-art IDSs. Full article
(This article belongs to the Special Issue AI-Powered IoT (AIoT) Systems: Advancements in Security, Sustainability, and Intelligence)
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24 pages, 4670 KB  
Article
X-HEM: An Explainable and Trustworthy AI-Based Framework for Intelligent Healthcare Diagnostics
by Mohammad F. Al-Hammouri, Bandi Vamsi, Islam T. Almalkawi and Ali Al Bataineh
Computers 2026, 15(1), 33; https://doi.org/10.3390/computers15010033 - 7 Jan 2026
Viewed by 1445
Abstract
Intracranial Hemorrhage (ICH) remains a critical life-threatening condition where timely and accurate diagnosis using non-contrast Computed Tomography (CT) scans is vital to reduce mortality and long-term disability. Deep learning methods have shown strong potential for automated hemorrhage detection, yet most existing approaches lack [...] Read more.
Intracranial Hemorrhage (ICH) remains a critical life-threatening condition where timely and accurate diagnosis using non-contrast Computed Tomography (CT) scans is vital to reduce mortality and long-term disability. Deep learning methods have shown strong potential for automated hemorrhage detection, yet most existing approaches lack confidence quantification and clinical interpretability, which limits their adoption in high-stakes care. This study presents X-HEM, an explainable hemorrhage ensemble model for reliable detection of Intracranial Hemorrhage (ICH) on non-contrast head CT scans. The aim is to improve diagnostic accuracy, interpretability, and confidence for real-time clinical decision support. X-HEM integrates three convolutional backbones (VGG16, ResNet50, DenseNet121) through soft voting. Bayesian uncertainty is estimated using Monte Carlo Dropout, while Grad-CAM++ and SHAP provide spatial and global interpretability. Training and validation were conducted on the RSNA ICH dataset, with external testing on CQ500. The model achieved AUCs of 0.96 (RSNA) and 0.94 (CQ500), demonstrated well-calibrated confidence (low Brier/ECE), and provided explanations that aligned with radiologist-marked regions. The integration of ensemble learning, Bayesian uncertainty, and dual explainability enables X-HEM to deliver confidence-aware, interpretable ICH predictions suitable for clinical use. Full article
(This article belongs to the Special Issue AI-Powered IoT (AIoT) Systems: Advancements in Security, Sustainability, and Intelligence)
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