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Electronics
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Edge-Intelligent Sustainable Cyber-Physical Systems
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Edge-Intelligent Sustainable Cyber-Physical Systems

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

Deadline for manuscript submissions: 15 August 2026 | Viewed by 2512

Special Issue Editor

Dr. Jun-Pyo Hong

E-Mail Website
Guest Editor
School of Electronics and Electrical Engineering, Hongik University, Seoul 04066, Republic of Korea
Interests: artificial intelligence; machine learning; signal processing; communications; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the Special Issue “Edge-Intelligent Sustainable Cyber-Physical Systems”, which seeks contributions that push the frontiers of edge computing, intelligent perception, advanced electronics, high-performance communications, and energy-aware infrastructure. At the heart of this theme is the recognition that tomorrow’s cyber-physical ecosystems will rely on tightly coupled sensing, learning, communication, and power-management loops carried out in real time at—or near—the network edge. We welcome manuscripts that explore machine-learning techniques for rapid perception and decision support; report breakthroughs in semiconductor and thin-film devices capable of ultra-low-power sensing or on-device inference; describe high-frequency circuits and broadband links that guarantee reliable, low-latency connectivity; and present power-system interfaces able to provide ancillary services such as fast frequency response and virtual inertia. Both theoretical advances and large-scale experimental demonstrations are encouraged, provided they illuminate pathways toward scalable, resilient, and economically viable deployments.

This Special Issue distinguishes itself by emphasizing the integration of these diverse strands rather than treating them in isolation. While prior work has often focused on a single layer—for example, an image-recognition model, a mmWave transceiver, or a grid-support converter—our aim is to showcase studies that reveal how innovations in one domain interact with and magnify progress in the others. By gathering such cross-disciplinary research in one venue, we intend to offer the community a cohesive reference that complements existing topic-specific literature and accelerates the translation of component-level breakthroughs into end-to-end edge-intelligent systems.

Dr. Jun-Pyo Hong
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-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

  • artificial intelligence
  • autonomous driving
  • semiconductor devices
  • thin-film transistor
  • 6G communications
  • mmWave integrated circuits
  • HEMT
  • power systems
  • cyber-physical systems
  • sustainable systems

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Published Papers (4 papers)

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Research

16 pages, 3976 KB  
Article
Spiking Feature-Driven Event Simulation with Movement-Aware Polarity Integration
by Jiwoong Oh, Byeongjun Kang, Hyungsik Shin and Dongwoo Kang
Electronics 2026, 15(7), 1420; https://doi.org/10.3390/electronics15071420 - 29 Mar 2026
Viewed by 382
Abstract
Event-based face detection has attracted significant interest due to the unique advantages of event cameras, including high temporal resolution, high dynamic range, and low power consumption. However, the lack of annotated public datasets remains a major challenge for training effective event-based face detection [...] Read more.
Event-based face detection has attracted significant interest due to the unique advantages of event cameras, including high temporal resolution, high dynamic range, and low power consumption. However, the lack of annotated public datasets remains a major challenge for training effective event-based face detection models. In this paper, we propose a spiking feature-driven synthetic event generation framework that utilizes a spiking neural network (SNN) in conjunction with a pretrained convolutional backbone to generate synthetic event representations from a single RGB image. To incorporate motion-induced ON/OFF polarity information, we introduce a movement-aware polarity integration (MPI) module that assumes four directional facial movements. An event-similarity score is further employed to select representations most consistent with real event data for training. Unlike conventional approaches relying on video-based simulators, our method enables efficient synthetic event dataset construction without requiring video inputs or additional simulation training. Experimental results on the N-Caltech101 dataset demonstrate a face detection accuracy of 99.91%, outperforming existing event-based face detection methods. Full article
(This article belongs to the Special Issue Edge-Intelligent Sustainable Cyber-Physical Systems)
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16 pages, 1372 KB  
Article
Spatio-Temporal Deep Learning-Assisted Multi-Period AC Optimal Power Flow
by Jihun Kim, Sojin Park, Dongwoo Kang and Hunyoung Shin
Electronics 2026, 15(4), 761; https://doi.org/10.3390/electronics15040761 - 11 Feb 2026
Viewed by 493
Abstract
The increasing penetration of renewable energy resources has amplified variability and uncertainty in power systems, reducing the effectiveness of conventional single-period Optimal Power Flow (OPF) strategies. Multi-period AC-OPF offers a more comprehensive framework by incorporating inter-temporal constraints and resource flexibility, but its high [...] Read more.
The increasing penetration of renewable energy resources has amplified variability and uncertainty in power systems, reducing the effectiveness of conventional single-period Optimal Power Flow (OPF) strategies. Multi-period AC-OPF offers a more comprehensive framework by incorporating inter-temporal constraints and resource flexibility, but its high computational complexity and strong temporal coupling make large-scale applications challenging, often causing scalability issues and convergence difficulties in conventional solvers. We address these issues with a spatio-temporal deep learning model that combines a Graph Attention Network (GAT) for topology-aware feature learning with a Temporal Convolutional Network (TCN) for multi-period temporal modeling. The proposed model is trained on large-scale 500-bus and 1354-bus systems under both 8-period and 24-period settings, and it achieves robust scalability with consistently high prediction accuracy. Using the model’s predictions, we construct an initial solution and provide it to a conventional OPF solver, which improves convergence performance and demonstrates the model’s effectiveness as an auxiliary tool for complex MP-ACOPF problems. Full article
(This article belongs to the Special Issue Edge-Intelligent Sustainable Cyber-Physical Systems)
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15 pages, 817 KB  
Article
Design of a DetNet Framework in a 3GPP 5G System
by Jaehyun Kim, Kyeongjun Ko, Seung-Chan Lim, Joon-Seok Kim, Jaeho Im and Jungtai Kim
Electronics 2026, 15(3), 664; https://doi.org/10.3390/electronics15030664 - 3 Feb 2026
Viewed by 557
Abstract
Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can [...] Read more.
Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can provide low-latency, highly reliable, and deterministic communications over Ethernet, whereas IETF deterministic networking (DetNet) seeks to provide a complementary network layer to support ultra-low latency communications. DetNet, as standardized in the IETF, provides time-sensitive characteristics that assure extremely low packet loss and latency for ultra-reliable low-latency communications. This study develops a novel framework to enable 3GPP support for DetNet functionalities. First, the proposed framework seeks to support IP-based DetNet traffic and urgent data transmission in the network overload conditions of 3GPP 5G systems. Additionally, the proposed design supports DetNet service connectivity between non-DetNet and DetNet service areas. Based on simulation results, the proposed framework can guarantee deterministic latency requirements and critical data transmission for DetNet compared with conventional approaches. The proposed scheme can achieve more effective performance for moving DetNet devices. Full article
(This article belongs to the Special Issue Edge-Intelligent Sustainable Cyber-Physical Systems)
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13 pages, 811 KB  
Article
Communication-Constrained UAV Pickup and Delivery for Continuous Operations
by Jun-Pyo Hong, Jaeho Im, Joon-Seok Kim, Kyeongjun Ko and Seung-Chan Lim
Electronics 2025, 14(23), 4638; https://doi.org/10.3390/electronics14234638 - 25 Nov 2025
Viewed by 573
Abstract
This paper investigates a communication-constrained unmanned aerial vehicle (UAV) pickup and delivery system for continuous multi-period operations. To ensure real-time control updates between UAVs and the ground server, a minimum communication rate requirement is imposed throughout each mission. The objective is to minimize [...] Read more.
This paper investigates a communication-constrained unmanned aerial vehicle (UAV) pickup and delivery system for continuous multi-period operations. To ensure real-time control updates between UAVs and the ground server, a minimum communication rate requirement is imposed throughout each mission. The objective is to minimize the average mission completion time of multiple rotary-wing UAVs while satisfying mobility, payload, safety, and communication constraints. The resulting mixed-integer nonlinear programming problem, involving binary pickup/drop-off decisions, trajectories, and variable time-slot durations, is mathematically intractable. To address this, a successive convex approximation framework combined with a penalty convex–concave procedure is developed, enabling iterative convex reformulation and convergence to a near-optimal binary-feasible solution. Simulation results demonstrate that the proposed algorithm efficiently generates collision-free trajectories and adaptive flight paths that maintain reliable communication links, outperforming baseline strategies in terms of completion time and coordination efficiency under communication constraints. Full article
(This article belongs to the Special Issue Edge-Intelligent Sustainable Cyber-Physical Systems)
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