Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.6
Journals
Electronics
Special Issues
Feature Papers in Networks: 2025–2026 Edition
share announcement

Feature Papers in Networks: 2025–2026 Edition

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

Deadline for manuscript submissions: 15 May 2026 | Viewed by 3280

Special Issue Editors

Dr. Jorge Bernal Bernabe

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain
Interests: IoT; privacy preservation; cybersecurity; threat/risk analysis; trust management; distributed systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pietro Manzoni

E-Mail Website
Guest Editor
Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain
Interests: mobile networks and systems; Internet of Things (IoT); edge–cloud communication; low-power network
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nurul Sarkar

E-Mail Website
Guest Editor
Department of Computer and Information Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: UAV networks; IoT; sensor networks; network protocols; wireless communication netwok; 5G and beyond; edge and fog computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce the launch of this new Special Issue entitled “Feature Papers in Networks: 2025–2026 Edition.” Networks, as a dynamic and interdisciplinary field, represents a critical backbone bridging information technology, computer science, communication engineering, and various industry sectors. It plays an indispensable role in enabling seamless data transmission, supporting intelligent system operations, and driving digital transformation across domains such as smart cities, healthcare, transportation, and industrial manufacturing. With the rapid evolution of technologies like 6G, edge computing, and artificial intelligence-empowered network management, research on network architectures, protocols, security, and optimization is advancing at an unprecedented pace, offering innovative solutions to address complex challenges in connectivity, efficiency, and reliability. This Special Issue is dedicated to showcasing the most significant, high-quality cutting-edge contributions across the field of networks research.

In this Special Issue, we welcome original research articles and reviews that include—but are not limited to—the following topics:

  • Design and optimization of next-generation network architectures (e.g., 6G networks, satellite-terrestrial integrated networks, edge–cloud collaborative networks)​;
  • advanced network protocols for enhanced data transmission efficiency, latency reduction, and resource allocation​;
  • network security and privacy protection technologies, including intrusion detection, encryption algorithms, and trust management mechanisms​;
  • artificial intelligence and machine learning applications in network management, traffic prediction, and fault diagnosis​;
  • network support for emerging technologies (e.g., Internet of Things, virtual reality/augmented reality, quantum communication)​;
  • green and energy-efficient network design and operation strategies​;
  • network performance evaluation, modeling, and simulation methodologies​.

We look forward to receiving your contributions.

Dr. Jorge Bernal Bernabe
Prof. Dr. Pietro Manzoni
Prof. Dr. Nurul Sarkar
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

  • wireless communication and systems
  • computer networks
  • Internet of Things and smart cities
  • pervasive computing and smart spaces
  • distributed systems networking, cloudification and services
  • connected and autonomous vehicles
  • mobile networking and computing
  • quality of service and quality of experience in wired and wireless systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 3630 KB  
Article
Improving Object Detection in Generalized Foggy Conditions of Insulator Defect Detection Based on Drone Images
by Abdulrahman Kariri and Khaled Elleithy
Electronics 2026, 15(5), 979; https://doi.org/10.3390/electronics15050979 - 27 Feb 2026
Viewed by 199
Abstract
Routine evaluation of insulator performance is important for maintaining the reliability and safety of power system operations. The use of unmanned aerial vehicles (UAVs) has been a significant advancement in transmission line monitoring, effectively replacing traditional manual inspection methods. With the rapid advancement [...] Read more.
Routine evaluation of insulator performance is important for maintaining the reliability and safety of power system operations. The use of unmanned aerial vehicles (UAVs) has been a significant advancement in transmission line monitoring, effectively replacing traditional manual inspection methods. With the rapid advancement of deep learning techniques, methods based on these models for detecting insulator defects have attracted increasing research interest and achieved notable advancements. Nevertheless, existing approaches primarily emphasize constructing sophisticated and intricate network architectures, which consequently lead to greater inference complexity when applied in practical scenarios. On the other hand, foggy scenarios pose challenges for learning algorithms due to difficulties in obtaining and labeling samples, as well as the poor performance of detectors trained on clear-weather samples. This study proposes adaptive enhancement based on YOLO, a framework that has robustness and domain generalization under fog-induced distribution shifts. It optimizes at multiple scales and enhances images as input to a detector in a single pipeline. Experimental results demonstrate improved performance on public UPID and SFID insulator defect datasets, improving insulator defect detection precision without increased computational complexity or inference resources, which is of great significance for advancing object detection in adverse weather. The proposed method achieves real-time performance, with an end-to-end inference speed exceeding 25 FPS and a model-only speed of approximately 38 FPS on 678 images from UPID, demonstrating both practical applicability and computational efficiency. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
►▼ Show Figures

Figure 1

24 pages, 2125 KB  
Article
MIC-SSO: A Two-Stage Hybrid Feature Selection Approach for Tabular Data
by Wei-Chang Yeh, Yunzhi Jiang, Hsin-Jung Hsu and Chia-Ling Huang
Electronics 2026, 15(4), 856; https://doi.org/10.3390/electronics15040856 - 18 Feb 2026
Viewed by 242
Abstract
High-dimensional structured datasets are common in fields such as semiconductor manufacturing, healthcare, and finance, where redundant and irrelevant features often increase computational cost and reduce predictive accuracy. Feature selection mitigates these issues by identifying a compact, informative subset of features, enhancing model efficiency, [...] Read more.
High-dimensional structured datasets are common in fields such as semiconductor manufacturing, healthcare, and finance, where redundant and irrelevant features often increase computational cost and reduce predictive accuracy. Feature selection mitigates these issues by identifying a compact, informative subset of features, enhancing model efficiency, performance, and interpretability. This study proposes Maximal Information Coefficient–Simplified Swarm Optimization (MIC-SSO), a two-stage hybrid feature selection method that combines the MIC as a filter with SSO as a wrapper. In Stage 1, MIC ranks feature relevance and removes low-contribution features; in Stage 2, SSO searches for an optimal subset from the reduced feature space using a fitness function that integrates the Matthews Correlation Coefficient (MCC) and feature reduction rate to balance accuracy and compactness. Experiments on five public datasets compare MIC-SSO with multiple hybrid, heuristic, and literature-reported methods, with results showing superior predictive accuracy and feature compression. The method’s ability to outperform existing approaches in terms of predictive accuracy and feature compression underscores its broader significance, offering a powerful tool for data analysis in fields like healthcare, finance, and semiconductor manufacturing. Statistical tests further confirm significant improvements over competing approaches, demonstrating the method’s effectiveness in integrating the efficiency of filters with the precision of wrappers for high-dimensional tabular data analysis. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
►▼ Show Figures

Figure 1

28 pages, 1318 KB  
Article
Lexicographic A*: Hierarchical Distance and Turn Optimization for Mobile Robots
by Wei-Chang Yeh, Jiun-Yu Tu, Tsung-Yan Huang, Yi-Zhen Liao and Chia-Ling Huang
Electronics 2026, 15(3), 599; https://doi.org/10.3390/electronics15030599 - 29 Jan 2026
Viewed by 301
Abstract
Autonomous mobile robots require efficient path planning algorithms for navigation in grid-based environments. While the A* algorithm guarantees optimally short paths using admissible heuristics, it exhibits path degeneracy: multiple geometrically distinct paths often share identical length. Classical A* arbitrarily selects among these equal-cost [...] Read more.
Autonomous mobile robots require efficient path planning algorithms for navigation in grid-based environments. While the A* algorithm guarantees optimally short paths using admissible heuristics, it exhibits path degeneracy: multiple geometrically distinct paths often share identical length. Classical A* arbitrarily selects among these equal-cost candidates, frequently producing trajectories with excessive directional changes. Each turn induces deceleration–acceleration cycles that degrade energy efficiency and accelerate mechanical wear. To address this, we propose Turn-Minimizing A* (TM-A*), a lexicographic optimization approach that maintains distance optimality while minimizing cumulative heading changes. Unlike weighted-cost methods that require parameter calibration, TM-A* applies a dual-objective framework: distance takes strict priority, with turn count serving as a tie-breaker among equal-length paths. A key contribution of this work is the explicit guarantee that the generated path has the minimum number of turns among all shortest paths. By formulating path planning as a lexicographic optimization problem, TM-A* strictly prioritizes path length optimality and deterministically selects, among all equal-length candidates, the one with the fewest directional changes. Unlike classical A*, which arbitrarily resolves path degeneracy, TM-A* provably eliminates this ambiguity. As a result, the method ensures globally shortest paths with minimal turning, directly improving trajectory smoothness and operational efficiency. We prove that TM-A* preserves the O(|E|log|V|) time complexity of classical A*. Validation across 30 independent Monte Carlo trials at resolutions from 200 × 200 to 1000 × 1000 demonstrates that TM-A* reduces turn count by 39–43% relative to baseline A* (p < 0.001). Although the inclusion of orientation expands the search space four-fold, the computation time increases by only a factor of approximately 3 (≈200%), indicating efficient scalability relative to problem complexity. With absolute latency remaining below 3300 ms for 1000 × 1000 grids, the approach is highly suitable for static global planning. Consequently, TM-A* provides a deterministic and scalable solution for generating smooth trajectories in industrial mobile robot applications. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
►▼ Show Figures

Figure 1

24 pages, 1253 KB  
Article
Re-Evaluating Android Malware Detection: Tabular Features, Vision Models, and Ensembles
by Prajwal Hosahalli Dayananda and Zesheng Chen
Electronics 2026, 15(3), 544; https://doi.org/10.3390/electronics15030544 - 27 Jan 2026
Viewed by 468
Abstract
Static, machine learning-based malware detection is widely used in Android security products, where even small increases in false-positive rates can impose significant burdens on analysts and cause unacceptable disruptions for end users. Both tabular features and image-based representations have been explored for Android [...] Read more.
Static, machine learning-based malware detection is widely used in Android security products, where even small increases in false-positive rates can impose significant burdens on analysts and cause unacceptable disruptions for end users. Both tabular features and image-based representations have been explored for Android malware detection. However, existing public benchmark datasets do not provide paired tabular and image representations for the same samples, limiting direct comparisons between tabular models and vision-based models. This work investigates whether carefully engineered, domain-specific tabular features can match or surpass the performance of state-of-the-art deep vision models under strict false-positive-rate constraints, and whether ensemble approaches justify their additional complexity. To enable this analysis, we construct a large corpus of Android applications with paired static representations and evaluate six popular machine learning models on the exact same samples: two tabular models using EMBER features, two tabular models using extended EMBER features, and two vision-based models using malware images. Our results show that a LightGBM model trained on extended EMBER features outperforms all other evaluated models, as well as a state-of-the-art approach trained on a much larger dataset. Furthermore, we develop an ensemble model combining both tabular and vision-based detectors, which yields a modest performance improvement but at the cost of substantial additional computational and engineering overhead. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
►▼ Show Figures

Figure 1

29 pages, 2616 KB  
Article
The Manhattan δ-Corridor: A Maximal Connectivity-Preserving Framework for Scalable Robot Navigation
by Wei-Chang Yeh, Jiun-Yu Tu, Hao-Jen Kuan, Sheng-Yun Chen and Chia-Ling Huang
Electronics 2026, 15(2), 306; https://doi.org/10.3390/electronics15020306 - 10 Jan 2026
Viewed by 475
Abstract
Balancing safety with computational speed is a persistent challenge in autonomous navigation. While optimal pathfinders like A* are efficient, they fail to define the navigable “buffer” zone required for safe motion. Existing corridor generation methods attempt to bridge this gap but often suffer [...] Read more.
Balancing safety with computational speed is a persistent challenge in autonomous navigation. While optimal pathfinders like A* are efficient, they fail to define the navigable “buffer” zone required for safe motion. Existing corridor generation methods attempt to bridge this gap but often suffer from heavy computational overhead or geometric instability. This paper introduces the Manhattan d-corridor, a framework that constructs strictly bounded, collision-free regions around a reference path. By combining systematic expansion with topological pruning, the algorithm guarantees structural minimality without sacrificing coverage. Experiments confirmed that the method is over two orders of magnitude faster than standard baselines. Crucially, while traditional methods suffered geometric collapse at high resolutions and dropped to unsafe collision ratios, the d-corridor maintained invariant safety (1.0) across all tests. This establishes the framework as a highly robust, real-time solution for resource-constrained robotics. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
►▼ Show Figures

Figure 1

20 pages, 8026 KB  
Article
HyFLM: A Hypernetwork-Based Federated Learning with Multidimensional Trajectory Optimization on Diffusion Paths
by Ho-jun Song and Young-Joo Suh
Electronics 2025, 14(23), 4704; https://doi.org/10.3390/electronics14234704 - 28 Nov 2025
Viewed by 482
Abstract
The effective training of large-scale distributed deep learning models has become an active and emerging research area in recent years. Federated learning (FL) can address those challenges by training global models through parameter exchange of client models rather than raw data sharing, thereby [...] Read more.
The effective training of large-scale distributed deep learning models has become an active and emerging research area in recent years. Federated learning (FL) can address those challenges by training global models through parameter exchange of client models rather than raw data sharing, thereby preserving security and communication efficiency. However, conventional linear aggregation approaches in FL neglect heterogeneous client models and non-IID data. This often results in inter-layer information imbalance and feature-space misalignment, leading to low overall accuracy and unstable training. To overcome these limitations, we propose HyFLM, a personalized federated learning framework that maximizes performance with Multidimensional Trajectory Optimization theory (MTO) on diffusion paths. HyFLM extends a diffusion-based FL framework by encoding client–parameter dependencies with a diffusion model and precisely controlling dimension-specific paths, thereby generating personalized weights that reflect both the data complexity and the resource constraints of each client. In addition, a lightweight hypernetwork generates client-specific adapters or weights to further enhance personalization. Extensive experiments on multiple benchmarks demonstrate that HyFLM consistently outperforms major baselines in terms of both accuracy and communication efficiency, achieving faster convergence and higher accuracy. Furthermore, ablation studies verify the contribution of MAC to convergence acceleration, confirming that HyFLM is an effective and practical personalized FL paradigm for heterogeneous client models. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
►▼ Show Figures

Figure 1

28 pages, 3209 KB  
Article
Energy Efficiency Optimization in Heterogeneous 5G Networks Using DUDe
by Chrysostomos-Athanasios Katsigiannis, Konstantinos Tsachrelias, Vasileios Kokkinos, Apostolos Gkamas, Christos Bouras and Philippos Pouyioutas
Electronics 2025, 14(23), 4641; https://doi.org/10.3390/electronics14234641 - 25 Nov 2025
Viewed by 611
Abstract
To meet the escalating data demands of 5G and beyond networks, densified Heterogeneous Networks (HetNets) provide a promising solution, deploying small base stations for improved spectral and energy efficiency. However, HetNets pose challenges, particularly in user association. This journal introduces the Downlink/Uplink Decoupling [...] Read more.
To meet the escalating data demands of 5G and beyond networks, densified Heterogeneous Networks (HetNets) provide a promising solution, deploying small base stations for improved spectral and energy efficiency. However, HetNets pose challenges, particularly in user association. This journal introduces the Downlink/Uplink Decoupling (DUDe) approach, which enhances uplink performance in HetNets by allowing different access points for uplink and downlink associations. We assess DUDe’s energy efficiency through extensive simulations across various scenarios, demonstrating substantial energy savings compared to centralized 5G systems. Our findings underscore the importance of energy-efficient design for reducing network operational costs and carbon footprint in 5G networks. In addition to energy efficiency gains, DUDe also offers improved resource allocation and network flexibility, making it a valuable solution for evolving wireless communication ecosystems. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop