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Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition

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

Deadline for manuscript submissions: closed (15 June 2026) | Viewed by 19897

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Editors

Prof. Dr. Namgi Kim

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Guest Editor

School of Computer Science and Engineering, Kyonggi University, Suwon-si 16227, Gyeonggi-do, Republic of Korea
Interests: mobile networks; networks protocols; intelligent systems
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Dr. Ahyoung Lee

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Guest Editor

Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: SDN; sensor networks
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Special Issue Information

Dear Colleagues,

The era of ubiquitous computing and smart systems has already begun. With the growing trend in the IoT and sensor devices, many smart applications are already enriching our lives. However, there are still many issues that require their further widespread deployment.

Therefore, this Special Issue focuses on discussing the future trends and challenges of ubiquitous computers and smart systems. Potential topics of interest include, but are not limited to:

Ubiquitous computing;
Smart intelligent systems;
Advanced networks;
Big data systems;
Computational intelligence;
Smart pattern recognition;
Sensors, IoT and IioT;
Smart image processing;
Machine learning;
Multimedia systems.

Dr. Namgi Kim
Dr. Ahyoung Lee
Guest Editors

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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. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

ubiquitous computing
smart intelligent systems
advanced networks
big data systems
computational intelligence
smart pattern recognition
sensors, IoT and IioT
smart image processing
machine learning
multimedia systems

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Related Special Issue

Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 3rd Edition in Electronics

Published Papers (9 papers)

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Research

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22 pages, 12911 KB

Open AccessArticle

Distribution-Preserving Latent Image Steganography via Conditional Optimal Transport and Theoretical Target Synthesis

by Kamil Woźniak, Marek R. Ogiela and Lidia Ogiela

Electronics 2026, 15(6), 1321; https://doi.org/10.3390/electronics15061321 - 22 Mar 2026

Viewed by 582

Abstract

We propose Distribution-Preserving Latent Steganography via Conditional Optimal Transport (DPL-COT), a coverless image steganography framework for latent diffusion models. Unlike classical cover-modifying schemes, DPL-COT embeds a bitstream directly into the initialization noise latent

z_{T} \sim N (0, I)

without [...] Read more.

z_{T} \sim N (0, I)

without model retraining. Our primary objective is high recoverability and a low bit error rate (BER) under deterministic inversion, which is inherently imperfect due to numerical discretization and VAE nonlinearity. To maximize decoding stability, we restrict embedding to the natural tails of the latent prior by selecting the largest-magnitude coordinates, thereby increasing the sign decision margin against inversion drift. To preserve distributional stealth, per-bit target values are analytically derived from truncated Gaussians matching the marginal distribution of the selected coordinates. Conditional 1D optimal transport is applied independently for each bit class, mapping every coordinate to its target value while preserving rank order. We generate 5000 stego images using a pretrained diffusion model and demonstrate a favorable capacity–reliability trade-off (e.g., 4916 bits/image with 0.473% mean BER) and strong robustness to JPEG compression (sub-1% mean BER at

Q = 60

). Compared with LDStega, a recent LDM-based scheme reporting 99.28% clean-channel accuracy, DPL-COT achieves 99.53% at a comparable operating point and sustains above-99% accuracy under all tested JPEG quality factors. Latent-space tests further confirm negligible cover–stego distribution shift (mean

{KS}_{2} < 0.003

, mean

W_{1} < 0.003

), a property not formally addressed by prior methods. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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50 pages, 3712 KB

Open AccessArticle

Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review

by Carlos Álvarez-López, Alfonso González-Briones and Tiancheng Li

Electronics 2026, 15(2), 385; https://doi.org/10.3390/electronics15020385 - 15 Jan 2026

Cited by 11 | Viewed by 3331

Abstract

This paper reviews Artificial Intelligence techniques for distributed energy management, focusing on integrating machine learning, reinforcement learning, and multi-agent systems within IoT-Edge-Cloud architectures. As energy infrastructures become increasingly decentralized and heterogeneous, AI must operate under strict latency, privacy, and resource constraints while remaining transparent and auditable. The study examines predictive models ranging from statistical time series approaches to machine learning regressors and deep neural architectures, assessing their suitability for embedded deployment and federated learning. Optimization methods—including heuristic strategies, metaheuristics, model predictive control, and reinforcement learning—are analyzed in terms of computational feasibility and real-time responsiveness. Explainability is treated as a fundamental requirement, supported by model-agnostic techniques that enable trust, regulatory compliance, and interpretable coordination in multi-agent environments. The review synthesizes advances in MARL for decentralized control, communication protocols enabling interoperability, and hardware-aware design for low-power edge devices. Benchmarking guidelines and key performance indicators are introduced to evaluate accuracy, latency, robustness, and transparency across distributed deployments. Key challenges remain in stabilizing explanations for RL policies, balancing model complexity with latency budgets, and ensuring scalable, privacy-preserving learning under non-stationary conditions. The paper concludes by outlining a conceptual framework for explainable, distributed energy intelligence and identifying research opportunities to build resilient, transparent smart energy ecosystems. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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15 pages, 1048 KB

Open AccessArticle

Synthetic-Digital Twin Assisted Federated Graph Learning for Edge-Based Anomaly Detection in Autonomous IoT Systems

by Manuel J. C. S. Reis, Carlos Serôdio and Frederico Branco

Electronics 2026, 15(2), 364; https://doi.org/10.3390/electronics15020364 - 14 Jan 2026

Cited by 1 | Viewed by 893

Abstract

Federated Graph Neural Networks (FedGNNs) have emerged as a promising paradigm for decentralized graph learning across distributed data silos. However, the influence of underlying communication topologies on model accuracy and efficiency remains underexplored. This study presents a topology-aware benchmarking framework for federated GNNs, systematically evaluating the impact of network structure and aggregation strategy on performance and communication overhead. The proposed framework functions as a synthetic, communication-level digital twin that emulates Federated Learning interactions and topology-dependent dynamics under controlled conditions. Four learning schemes—Centralized, Local, FedAvg, and FedAvg-Fedadam—were assessed across three representative topologies: Barabási–Albert (BA), Watts–Strogatz (WS), and Erdős–Rényi (ER). Results demonstrate that centralized training achieved the highest mean ROC-AUC (0.63), while FedAvg-Fedadam attained the best F1-score (0.038), balancing local adaptation and global convergence. Among topologies, BA and WS yielded higher average AUC values (approximately 0.57 and 0.56, respectively) than ER (approximately 0.39). Communication analysis revealed FedAvg as the most efficient strategy, requiring only approximately 3.8 × 10⁵ bytes cumulatively. These findings highlight key trade-offs between accuracy, robustness, and communication efficiency in federated graph learning and provide empirical guidance for topology-aware optimization of distributed GNNs. While the experiments rely on representative synthetic topologies, the insights offer indicative relevance and potential applicability to Internet-of-Things (IoT), vehicular, and cyber-physical networks, where communication structure and bandwidth constraints critically influence collaborative intelligence. By modeling canonical connectivity patterns and releasing our code and data, the proposed benchmarking framework offers a reproducible basis for comparing emerging federated graph architectures under constrained communication conditions. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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19 pages, 3438 KB

Open AccessEditor’s ChoiceArticle

Geometry-Aware Cross-Modal Translation with Temporal Consistency for Robust Multi-Sensor Fusion in Autonomous Driving

by Zhengyi Lu, Jinxiang Pang and Zhehai Zhou

Electronics 2025, 14(23), 4663; https://doi.org/10.3390/electronics14234663 - 27 Nov 2025

Cited by 1 | Viewed by 1409

Abstract

Intelligent Transportation Systems (ITSs), particularly autonomous driving, face critical challenges when sensor modalities fail due to adverse conditions or hardware malfunctions, causing severe perception degradation that threatens system-wide reliability. We present a unified geometry-aware cross-modal translation framework that synthesizes missing sensor data while maintaining temporal consistency and quantifying uncertainty. Our pipeline enforces 92.7% frame-to-frame stability via an optical-flow-guided spatio-temporal module with smoothness regularization, preserves fine-grained 3D geometry through pyramid-level multi-scale alignment constrained by the Chamfer distance, surface normals, and edge consistency, and ultimately delivers dropout-tolerant perception by adaptively fusing multi-modal cues according to pixel-wise uncertainty estimates. Extensive evaluation on KITTI-360, nuScenes, and a newly collected Real-World Sensor Failure dataset demonstrates state-of-the-art performance: 35% reduction in Chamfer distance, 5% improvement in BEV (bird’s eye view) segmentation mIoU (mean Intersection over Union) (79.3%), and robust operation maintaining mIoU under complete sensor loss for 45+ s. The framework achieves real-time performance at 17 fps with 57% fewer parameters than competing methods, enabling deployment-ready sensor-agnostic perception for safety-critical autonomous driving applications. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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14 pages, 2283 KB

Open AccessArticle

Effects of Scale Regularization in Fraud Detection Graphs

by Janggun Jeon, Junho Ahn and Namgi Kim

Electronics 2025, 14(18), 3660; https://doi.org/10.3390/electronics14183660 - 16 Sep 2025

Cited by 2 | Viewed by 1343

Abstract

With the growth of e-commerce platforms, the number of fraud reviews has rapidly increased, making fraud detection for platform data critically important. To detect fraud reviews in platform data, recent approaches have leveraged graph neural networks that model users as nodes in a heterogeneous multi-relational graph structure. This approach represents platform users as nodes in a graph, and the relationships among users who share commonalities in their reviews or products as edges, in order to identify fraudulent reviewers. However, existing graph-based fraud detection methods may suffer from the unstable training of the classifier networks that process embedding vectors. In this paper, we identify that this issue arises from the excessive deviation and scale expansion caused by some of the aggregated values from adjacent nodes in conventional node embeddings, and we propose a scale regularization method to mitigate this. To verify the effectiveness of the proposed method, we conduct validation on the Amazon-Fraud dataset, which is a multi-relational graph dataset constructed from review data of Amazon E-Commerce. The experimental results show that the proposed scale regularization achieves superior performance compared to previous verified graph fraud detection models. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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25 pages, 4415 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Multi-Scale Dual Discriminator Generative Adversarial Network for Gas Leakage Detection

by Saif H. A. Al-Khazraji, Hafsa Iqbal, Jesús Belmar Rubio, Fernando García and Abdulla Al-Kaff

Electronics 2025, 14(17), 3564; https://doi.org/10.3390/electronics14173564 - 8 Sep 2025

Cited by 1 | Viewed by 1530

Abstract

Gas leakages pose significant safety risks in urban environments and industrial sectors like the Oil and Gas Industry (OGI), leading to accidents, fatalities, and economic losses. This paper introduces a novel generative AI framework, the Multi-Scale Dual Discriminator Generative Adversarial Network (MSDD-GAN), designed to detect and localize gas leaks by generating thermal images from RGB input images. The proposed method integrates three key innovations: (1) Attention-Guided Masking (AttMask) for precise gas leakage localization using saliency maps and a circular Region of Interest (ROI), enabling pixel-level validation; (2) Multi-scale input processing to enhance feature learning with limited data; and (3) Dual Discriminator to validate the thermal image realism and leakage localization accuracy. A comprehensive dataset from laboratory and industrial environment has been collected using a FLIR thermal camera. The MSDD-GAN demonstrated robust performance by generating thermal images with the gas leakage indications at a mean accuracy of

81.6 %

, outperforming baseline cGANs by leveraging a multi-scale generator and dual adversarial losses. By correlating ice formation in RGB images with the leakage indications in thermal images, the model addresses critical challenges of OGI applications, including data scarcity and validation reliability, offering a robust solution for continuous gas leak monitoring in pipeline. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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23 pages, 5983 KB

Open AccessArticle

Fuzzy Logic Control for Adaptive Braking Systems in Proximity Sensor Applications

by Adnan Shaout and Luis Castaneda-Trejo

Electronics 2025, 14(14), 2858; https://doi.org/10.3390/electronics14142858 - 17 Jul 2025

Cited by 5 | Viewed by 2151

Abstract

This paper details the design and implementation of a fuzzy logic control system for an advanced driver-assistance system (ADAS) that adjusts brake force based on proximity sensing, vehicle speed, and road conditions. By employing a cost-effective ultrasonic sensor (HC-SR04) and an STM32 microcontroller, the system facilitates real-time adjustments to braking force, enhancing both vehicle safety and driver comfort. The fuzzy logic controller processes three inputs to deliver a smooth and adaptive brake response, thus addressing the shortcomings of traditional binary systems that can lead to abrupt and unsafe braking actions. The effectiveness of the system is validated through several test cases, demonstrating improved responsiveness and safety across various driving scenarios. This paper presents a cost-effective model for a straightforward braking system using fuzzy logic, laying the groundwork for the development of more advanced systems in emerging technologies. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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24 pages, 9593 KB

Open AccessArticle

Deep Learning Approaches for Skin Lesion Detection

by Jonathan Vieira, Fábio Mendonça and Fernando Morgado-Dias

Electronics 2025, 14(14), 2785; https://doi.org/10.3390/electronics14142785 - 10 Jul 2025

Cited by 10 | Viewed by 6398

Abstract

Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated skin lesion classification. A total of 38 CNN architectures from ten families (ConvNeXt, DenseNet, EfficientNet, Inception, InceptionResNet, MobileNet, NASNet, ResNet, VGG, and Xception) were evaluated using transfer learning on the HAM10000 dataset for seven-class skin lesion classification, namely, actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi, and vascular lesions. The comparative analysis used standardized training conditions, with all models utilizing frozen pre-trained weights. Cross-database validation was then conducted using the ISIC 2019 dataset to assess generalizability across different data distributions. The ConvNeXtXLarge architecture achieved the best performance, despite having one of the lowest performance-to-number-of-parameters ratios, with 87.62% overall accuracy and 76.15% F1 score on the test set, demonstrating competitive results within the established performance range of existing HAM10000-based studies. A proof-of-concept multiplatform mobile application was also implemented using a client–server architecture with encrypted image transmission, demonstrating the viability of integrating high-performing models into healthcare screening tools. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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Review

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35 pages, 1938 KB

Open AccessReview

Ubiquitous Computing and Smart Systems in the Treatment of Psychiatric and Neurological Disorders—A Narrative Review

by Dariusz Mikołajewski, Emilia Mikołajewska, Jolanta Masiak, Ewelina Panas and Urszula Rogalla-Ładniak

Electronics 2026, 15(8), 1627; https://doi.org/10.3390/electronics15081627 - 14 Apr 2026

Viewed by 848

Abstract

This bibliometric study examines the role of ubiquitous computing and intelligent systems in the treatment of mental and neurological disorders. Ubiquitous computing integrates computational intelligence into everyday environments, enabling seamless monitoring and support of patients. Intelligent systems, including wearable devices, environmental sensors, and mobile health applications, collect real-time data on behavior, physiology, and environmental factors. These systems support early detection of symptom changes, adherence to treatment, and crisis prediction through context-aware analysis. Artificial intelligence (AI) processes the collected data to generate personalized therapeutic feedback and notify healthcare providers when intervention is needed. In mental health care, intelligent environments can monitor mood, sleep, and social interaction patterns, providing valuable objective information about mental health status. In the case of neurological conditions such as Parkinson’s disease or epilepsy, intelligent systems facilitate movement tracking, seizure detection, and cognitive assessment outside of the clinical setting. Integration with electronic health records and telemedicine platforms ensures coordinated and responsive care. Ethical design, privacy protection, and patient consent remain key to successful implementation. In this way, ubiquitous computing is transforming care models by increasing autonomy, precision, and continuity in the treatment of complex neurodegenerative diseases, including those related to neurodegeneration in aging. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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