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Electronics
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AI-Driven Edge Intelligence for Smart Cities, Healthcare, and Autonomous Systems
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AI-Driven Edge Intelligence for Smart Cities, Healthcare, and Autonomous Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 2778

Special Issue Editors

Dr. Ahmad Ali

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Interests: AI-driven edge computing; smart cities; healthcare informatics; autonomous systems; spatio-temporal analytics; federated learning; fog and cloud-edge collaboration; sustainable computing; intelligent transportation systems; real-time decision-making, cybersecurity and resilience
Dr. Xiaoshan Bai

E-Mail Website
Guest Editor
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
Interests: multi-robot/vehicle systems; multi-robot/vehicle task assignment; path planning; motion planning; operational research; heuristic algorithms; optimal control
Dr. Lanqing Yang

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: Internet of Things; intelligent sensing; large language models; mobile computing

Special Issue Information

Dear Colleagues,

The convergence of artificial intelligence (AI) and edge computing is reshaping the landscape of next-generation intelligent systems by enabling real-time, secure, and efficient data processing closer to data sources. Traditional cloud-centric solutions face limitations in terms of latency, scalability, privacy, and energy efficiency, which makes them less suitable for mission-critical applications in smart cities, healthcare, and autonomous systems. By integrating AI into edge and edge–cloud–fog ecosystems, researchers and practitioners can develop sustainable, adaptive, and context-aware solutions to address these challenges.

This Special Issue aims to gather cutting-edge research contributions on AI-driven edge intelligence that enhances decision-making, optimizes resource utilization, and supports resilient and secure infrastructures. In smart cities, AI-powered edge systems can revolutionize traffic management, energy distribution, and public safety. In healthcare, edge intelligence offers efficient diagnostics, continuous monitoring, and privacy-preserving solutions. For autonomous systems, it enables low-latency decision-making and real-time control in highly dynamic environments. We welcome original research articles, surveys, and case studies that explore novel algorithms, system architectures, optimization frameworks, and applications, with the ultimate goal of advancing intelligent, sustainable, and human-centric technologies.

Dr. Ahmad Ali
Dr. Xiaoshan Bai
Dr. Lanqing Yang
Guest Editors

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • AI-driven edge computing
  • smart cities
  • healthcare informatics
  • autonomous systems
  • spatio-temporal analytics
  • federated learning
  • fog and cloud-edge collaboration
  • multi-robot system, sustainable computing
  • intelligent transportation systems
  • real-time decision-making, cybersecurity and resilience

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Published Papers (3 papers)

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Research

24 pages, 6691 KB  
Article
GISLC: Gated-Inception Model for Skin Lesion Classification
by Tamam Alsarhan, Mohammad Kamal Abdulaziz, Ahmad Ali, Ayoub Alsarhan, Sami Aziz Alshammari, Rahaf R. Alshammari, Nayef H. Alshammari and Khalid Hamad Alnafisah
Electronics 2026, 15(4), 861; https://doi.org/10.3390/electronics15040861 - 18 Feb 2026
Viewed by 218
Abstract
Skin-lesion recognition from clinical photographs is clinically valuable yet computationally challenging due to large intra-class variation, subtle inter-class boundaries, class imbalance, and heterogeneous acquisition conditions. To address these constraints under realistic compute budgets, we investigate Inception-family convolutional baselines and propose GISLC—a Gated-Inception model [...] Read more.
Skin-lesion recognition from clinical photographs is clinically valuable yet computationally challenging due to large intra-class variation, subtle inter-class boundaries, class imbalance, and heterogeneous acquisition conditions. To address these constraints under realistic compute budgets, we investigate Inception-family convolutional baselines and propose GISLC—a Gated-Inception model that augments a GoogLeNet/Inception-V1 backbone with a lightweight, spatial gating head inspired by ConvLSTM. Unlike static fusion (concatenation/summation) of multi-branch features, the proposed gated head performs per-location, learnable regulation of feature flow across branches, prioritizing diagnostically salient patterns while suppressing redundant activations. Experiments were conducted on the clinical-images subset of the Multimodal Augmented Skin Lesion Dataset (MASLD), an augmented derivative of HAM10000, using stratified train/validation/test splits, clinically motivated augmentation, and class-weighted optimization to mitigate skewed label frequencies. A controlled ablation study evaluates backbone choices and optimization settings and isolates the contribution of gated fusion relative to standard Inception heads. Across runs, the gated fusion strategy improves discriminative performance while remaining parameter-efficient, supporting the view that spatially adaptive regulation can enhance robustness on non-dermatoscopic clinical imagery. We further outline practical steps for calibration analysis and compression-aware deployment in clinical and edge settings. Full article
(This article belongs to the Special Issue AI-Driven Edge Intelligence for Smart Cities, Healthcare, and Autonomous Systems)
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47 pages, 12434 KB  
Article
AI-Driven Blockchain and Federated Learning for Secure Electronic Health Records Sharing
by Muhammad Saeed Javed, Ali Hennache, Muhammad Imran and Muhammad Kamran Khan
Electronics 2025, 14(23), 4774; https://doi.org/10.3390/electronics14234774 - 4 Dec 2025
Viewed by 1042
Abstract
The proliferation of electronic health records necessitates secure and privacy-preserving data sharing frameworks to combat escalating cybersecurity threats in healthcare. Current systems face critical limitations including centralized data repositories vulnerable to breaches, static consent mechanisms, and inadequate audit capabilities. This paper introduces an [...] Read more.
The proliferation of electronic health records necessitates secure and privacy-preserving data sharing frameworks to combat escalating cybersecurity threats in healthcare. Current systems face critical limitations including centralized data repositories vulnerable to breaches, static consent mechanisms, and inadequate audit capabilities. This paper introduces an integrated blockchain and federated learning framework that enables privacy-preserving collaborative AI across healthcare institutions without centralized data pooling. The proposed approach combines federated distillation for heterogeneous model collaboration with dynamic differential privacy that adapts noise injection to data sensitivity levels. A novel threshold key-sharing protocol ensures decentralized access control, while a dual-layer Quorum blockchain establishes immutable audit trails for all data sharing transactions. Experimental evaluation on clinical datasets (Mortality Prediction and Clinical Deterioration from eICU-CRD) demonstrates that our framework maintains diagnostic accuracy within 3.6% of centralized approaches while reducing communication overhead by 71% and providing formal privacy guarantees. For Clinical Deterioration prediction, the framework achieves 96.9% absolute accuracy on the Clinical Deterioration task with FD-DP at ϵ = 1.0, representing only 0.14% degradation from centralized performance. The solution supports HIPAA-aligned technical safeguards, mitigates inference and membership attacks, and enables secure cross-institutional data sharing with real-time auditability. This work establishes a new paradigm for privacy-preserving healthcare AI that balances data utility, regulatory requirements, and protection against emerging threats in distributed clinical environments. Full article
(This article belongs to the Special Issue AI-Driven Edge Intelligence for Smart Cities, Healthcare, and Autonomous Systems)
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21 pages, 1247 KB  
Article
PriFed-IDS: A Privacy-Preserving Federated Reinforcement Learning Framework for Secure and Intelligent Intrusion Detection in Digital Health Systems
by Siyao Fu, Haoyu Xu, Asif Ali and Saba Sajid
Electronics 2025, 14(23), 4590; https://doi.org/10.3390/electronics14234590 - 23 Nov 2025
Viewed by 731
Abstract
The Internet of Medical Things (IoMT) integrates sensors, medical devices, and Internet of Things (IoT) technologies to provide data-driven healthcare systems. The systems facilitate medical monitoring and decision-making; however, there are significant concerns about data leakage and patient consent. Additionally, a shortage of [...] Read more.
The Internet of Medical Things (IoMT) integrates sensors, medical devices, and Internet of Things (IoT) technologies to provide data-driven healthcare systems. The systems facilitate medical monitoring and decision-making; however, there are significant concerns about data leakage and patient consent. Additionally, a shortage of large, high-quality IoMT datasets to study the surrounding issues is problematic. Federated learning (FL) is a decentralized machine learning approach that potentially offers substantial amounts of capacity, so that compound Smart Healthcare Systems (SHSs) can further personalize and contextualize the secrecy of data and strong system structures. Additionally, to protect against advanced and shifting computational intelligence-based cyber threats, especially in operational health environments, the use of Intruder Detection Systems (IDSs) is quite essential. However, traditional approaches to implementing IDSs are usually computationally costly and inappropriate for the narrow contours of deploying medical IoT devices. To address these challenges, the proposed study introduces PriFed-IDS, a novel, privacy-preserving FL-based IDS framework based on FL and reinforcement learning. The proposed model leverages reinforcement learning to uncover latent patterns in medical data, enabling accurate anomaly detection. A dynamic federation and aggregation strategy is implemented to optimize model performance while minimizing communication overhead by adaptively engaging clients in the training process. Experimental evaluations and theoretical analysis demonstrate that PriFed-IDS significantly outperforms existing benchmark IDS models in terms of detection accuracy and efficiency, underscoring its practical applicability for securing real-world IoMT networks. Full article
(This article belongs to the Special Issue AI-Driven Edge Intelligence for Smart Cities, Healthcare, and Autonomous Systems)
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