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Symmetry
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Symmetry and Asymmetry in Embedded Systems
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Symmetry and Asymmetry in Embedded Systems

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 4741

Special Issue Editors

Dr. Cheng Ji

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: data storage; embedded AI; artificial intelligence; mobile system; storage architecture
Dr. Chao Wu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: mobile system; artificial intelligence acceleration; file system; storage architecture
Dr. Geng Yuan

E-Mail Website
Guest Editor
School of Computing, University of Georgia, Athens, GA 30602, USA
Interests: AI systems; including energy-efficient deep learning with embedded systems; efficient training; model compression; hardware-software co-design for DNN architectures; emerging deep learning systems (e.g., superconducting, ReRAM)

Special Issue Information

Dear Colleagues,

With the rise of 5G, IoT, and smart manufacturing, edge computing has emerged as a paradigm where tasks are processed closer to the data source rather than in the cloud. As multi-modal data grow and the demand for intelligent services increases, traditional cloud computing struggles to handle the scale and maintain quality of service (QoS). Edge computing, with its low latency, reliability, and privacy benefits, enables “edge intelligence” by integrating networking, computing, storage, and applications.

We are pleased to invite you to submit your manuscript on edge computing. This technology powers smart manufacturing, personalized services, and applications like AR/VR, drones, healthcare, and transportation. Key challenges include limited resources, symmetric system, network constraints, and balancing privacy with efficiency, driving ongoing research to unlock the full potential of edge intelligence.

This Special Issue calls for submissions in a two-column format, following the MDPI proceedings specifications and the classification system detailed at https://www.mdpi.com/authors.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Framework for cloud–edge–end converged computing in edge-intelligent systems.
  • Symmetric design, including the following:
    • Unified and scalable architecture where communication and service provisions between cloud, edge, and end devices are balanced and efficient.
    • Similar data processing strategies or protocols at different levels to ensure the coordination and consistency of the system.
    • Symmetry in network topology to simplify routing algorithms and improve data transmission efficiency.
    • Symmetric data distribution patterns to balance the load and optimize the use of storage resources.
  • Heterogeneous challenges of edge-intelligence systems (computation, storage, and communication).
  • Design of low-power and energy-efficient technologies for edge-intelligence systems.
  • Model and architecture design for computer offloading, resource management, and task scheduling in edge-intelligence systems.
  • Research on federated learning, transfer learning and lifelong learning frameworks in edge-intelligence systems.
  • Distributed AI model training and inference acceleration in edge-intelligence systems.
  • Training optimization for multi-modal, heterogeneous, and small-scale data in edge-intelligence systems.
  • Privacy-preserving techniques in edge-intelligence systems (secure multi-party computing, homomorphic encryption, secret sharing techniques, differential privacy).

We look forward to receiving your contributions.

Dr. Cheng Ji
Dr. Chao Wu
Dr. Geng Yuan
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Symmetry 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 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

  • edge computing
  • symmetric system
  • cloud–edge converged computing
  • artificial intelligence
  • power efficiency
  • training optimization
  • privacy-preserving
  • federated learning
  • transfer learning
  • lifelong learning

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

Published Papers (7 papers)

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Research

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24 pages, 3453 KiB  
Article
Improving Domain Transfer with Consistency-Regularized Joint Distribution Alignment for Medical Image Classification
by Jiacheng Zhang, Rui Li, Cheng Liu and Xiang Ji
Symmetry 2025, 17(4), 515; https://doi.org/10.3390/sym17040515 - 28 Mar 2025
Viewed by 278
Abstract
Background: Domain transfer plays a vital role in medical image analysis. It mitigates the challenges posed by variations in imaging equipment, protocols, and patient demographics, ultimately improving model performance across different domains or edge-intelligence devices; Methods: This paper introduces a new unsupervised domain [...] Read more.
Background: Domain transfer plays a vital role in medical image analysis. It mitigates the challenges posed by variations in imaging equipment, protocols, and patient demographics, ultimately improving model performance across different domains or edge-intelligence devices; Methods: This paper introduces a new unsupervised domain adaptation approach, named Consistency-regularized Joint Distribution Alignment (C-JDA). Specifically, our method leverages Convolutional Neural Networks (CNNs) to align the joint distributions of source and target domains in the feature space, which involves the pseudo-labels of the target data for computing the relative chi-square divergence to measure the distribution relationship or asymmetry. Compared with traditional alignment methods with complex architectures or adversarial training, our model can be solved with a close-form equation, which is convenient for transferring among various scenarios. Additionally, we further propose symmetric consistency regularization to improve the robustness of the pseudo-label generation with diverse data augmentation strategies, where the augmented data are symmetric to their original data and should share the same predictions. Therefore, both components between distribution alignment and pseudo-label generation can be mutually improved for better performance. Results: Extensive experiments on multiple public medical image benchmarks demonstrate that C-JDA consistently outperforms both traditional domain adaptation methods and deep learning-based approaches. For the colon disease classification task, C-JDA achieved an accuracy of 87.41%, outperforming existing methods by 3.31%, with an F1 score of 87.26% and an improvement of 2.99%. For the Diabetic Retinopathy (DR) classification task, our method attained an accuracy and F1 score of 96.93%, surpassing state-of-the-art methods by 2.4%. Additionally, ablation studies validated the effectiveness of both the joint distribution alignment and symmetric consistency regularization components. Conclusions: Our C-JDA can significantly outperform existing domain adaptation methods by achieving state-of-the-art performance via improved joint distribution alignment with symmetric consistency regularization. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)
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27 pages, 1340 KiB  
Article
Asymmetric Training and Symmetric Fusion for Image Denoising in Edge Computing
by Yupeng Zhang and Xiaofeng Liao
Symmetry 2025, 17(3), 424; https://doi.org/10.3390/sym17030424 - 12 Mar 2025
Viewed by 473
Abstract
Effectively handling mixed noise types and varying intensities is crucial for accurate information extraction and analysis, particularly in resource-limited edge computing scenarios. Conventional image denoising approaches struggle with unseen noise distributions, limiting their effectiveness in real-world applications such as object detection, classification, and [...] Read more.
Effectively handling mixed noise types and varying intensities is crucial for accurate information extraction and analysis, particularly in resource-limited edge computing scenarios. Conventional image denoising approaches struggle with unseen noise distributions, limiting their effectiveness in real-world applications such as object detection, classification, and change detection. To address these challenges, we introduce a novel image denoising framework that integrates asymmetric learning with symmetric fusion. It leverages a pretrained model trained only on clean images to provide semantic priors, while a supervised module learns direct noise-to-clean mappings using paired noisy–clean data. The asymmetry in our approach stems from its dual training objectives: a pretrained encoder extracts semantic priors from noise-free data, while a supervised module learns noise-to-clean mappings. The symmetry is achieved through a structured fusion of pretrained priors and supervised features, enhancing generalization across diverse noise distributions, including those in edge computing environments. Extensive evaluations across multiple noise types and intensities, including real-world remote sensing data, demonstrate the superior robustness of our approach. Our method achieves state-of-the-art performance in both in-distribution and out-of-distribution noise scenarios, significantly enhancing image quality for downstream tasks such as environmental monitoring and disaster response. Future work may explore extending this framework to specialized applications like hyperspectral imaging and nighttime analysis while further refining the interplay between symmetry and asymmetry in deep-learning-based image restoration. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)
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21 pages, 2125 KiB  
Article
VGGNet and Attention Mechanism-Based Image Quality Assessment Algorithm in Symmetry Edge Intelligence Systems
by Fanfan Shen, Haipeng Liu, Chao Xu, Lei Ouyang, Jun Zhang, Yong Chen and Yanxiang He
Symmetry 2025, 17(3), 331; https://doi.org/10.3390/sym17030331 - 22 Feb 2025
Viewed by 487
Abstract
With the rapid development of Internet of Things (IoT) technology, the number of devices connected to the network is exploding. How to improve the performance of edge devices has become an important challenge. Research on quality evaluation algorithms for brain tumor images remains [...] Read more.
With the rapid development of Internet of Things (IoT) technology, the number of devices connected to the network is exploding. How to improve the performance of edge devices has become an important challenge. Research on quality evaluation algorithms for brain tumor images remains scarce within symmetry edge intelligence systems. Additionally, the data volume in brain tumor datasets is frequently inadequate to support the training of neural network models. Most existing non-reference image quality assessment methods are based on natural statistical laws or construct a single-network model without considering visual perception characteristics, resulting in significant differences between the final evaluation results and subjective perception. To address these issues, we propose the AM-VGG-IQA (Attention Module Visual Geometry Group Image Quality Assessment) algorithm and extend the brain tumor MRI dataset. Visual saliency features with attention mechanism modules are integrated into AM-VGG-IQA. The integration of visual saliency features brings the evaluation outcomes of the model more in line with human perception. Meanwhile, the attention mechanism module cuts down on network parameters and expedites the training speed. For the brain tumor MRI dataset, our model achieves 85% accuracy, enabling it to effectively accomplish the task of evaluating brain tumor images in edge intelligence systems. Additionally, we carry out cross-dataset experiments. It is worth noting that, under varying training and testing ratios, the performance of AM-VGG-IQA remains relatively stable, which effectively demonstrates its remarkable robustness for edge applications. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)
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16 pages, 8432 KiB  
Article
Evaluating Partitions in Packet Classification with the Asymmetric Metric of Disassortative Modularity
by Jinshui Wang, Yao Xin, Can Lu, Chengjun Jia and Yiming Ding
Symmetry 2025, 17(1), 37; https://doi.org/10.3390/sym17010037 - 28 Dec 2024
Viewed by 616
Abstract
At present, the method of using rule set partitioning technology to assist in constructing multiple decision trees for packet classification has been widely recognized. Rule set partitioning demonstrates a unique symmetry-breaking mechanism, systematically transforming the initial overlapping rule space into a more structured [...] Read more.
At present, the method of using rule set partitioning technology to assist in constructing multiple decision trees for packet classification has been widely recognized. Rule set partitioning demonstrates a unique symmetry-breaking mechanism, systematically transforming the initial overlapping rule space into a more structured and balanced configuration. By separating overlapping rules in the initial stage, this method significantly reduces rule replication within trees, thereby improving the algorithm’s classification performance. The asymmetric characteristics of this partitioning process are particularly noteworthy: through the strategic disruption of the initial rule set’s symmetric distribution, it creates asymmetric subspaces with enhanced computational efficiency. However, existing research lacks standardized metrics for evaluating the effectiveness of rule set partitioning schemes. The purpose of this paper is to investigate the impact of partitioning on algorithm performance. Based on community structure theory, we construct a weighted graph model for rule sets and propose a disassortative modularity metric to evaluate the effectiveness of rule set partitioning. This metric not only examines intra-community connections but also emphasizes the asymmetric connections between communities. By quantifying these structural features, it provides a novel perspective on rule set partitioning strategies. The experimental results demonstrate a significant positive correlation between disassortative modularity and classification throughput. This metric offers valuable guidance for packet classification partitioning techniques, highlighting the practical significance of symmetry and asymmetry in algorithm design. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)
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18 pages, 563 KiB  
Article
Energy-Efficient Connectivity Algorithm for Directional Sensor Networks in Edge Intelligence Systems
by Dingcheng Wu, Xueyong Xu, Chang Lu and Dapeng Mu
Symmetry 2025, 17(1), 20; https://doi.org/10.3390/sym17010020 - 26 Dec 2024
Viewed by 657
Abstract
The proliferation of edge intelligence systems necessitates efficient and reliable connectivity for sensor networks deployed at the edge. This paper proposes a novel energy-efficient connectivity algorithm called Constrained Angle-aware Connectivity Optimization (CA-Opt), designed for directional sensor networks to address the challenges of limited [...] Read more.
The proliferation of edge intelligence systems necessitates efficient and reliable connectivity for sensor networks deployed at the edge. This paper proposes a novel energy-efficient connectivity algorithm called Constrained Angle-aware Connectivity Optimization (CA-Opt), designed for directional sensor networks to address the challenges of limited resources and asymmetric network constraints in edge environments. CA-Opt constructs a hop-constrained, degree-bounded network topology while considering the directional coverage of sensor nodes. The algorithm incorporates an angle-aware child selection strategy to optimize the energy consumption by minimizing the number of active links and the total communication distance. Extensive simulations demonstrated that CA-Opt achieved comparable connectivity to the traditional Breadth-First Search (BFS) algorithms while significantly reducing the energy consumption. Furthermore, the impact of key parameters, such as the communication range, node density, maximum degree, and directional coverage angle, on CA-Opt’s performance was analyzed. The results underscore the potential of CA-Opt to balance asymmetry-driven connectivity control with energy-efficient operation, making it particularly suitable for resource-constrained edge applications, such as smart manufacturing, environmental monitoring, and intelligent transportation systems. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)
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24 pages, 5869 KiB  
Article
Offloading Strategy for Forest Monitoring Network Based on Improved Beetle Optimization Algorithm
by Xiaohui Cheng, Xiangang Lu, Yun Deng, Qiu Lu, Yanping Kang, Jian Tang, Yuanyuan Shi and Junyu Zhao
Symmetry 2024, 16(12), 1569; https://doi.org/10.3390/sym16121569 - 23 Nov 2024
Cited by 1 | Viewed by 679
Abstract
In forest monitoring networks, the computational capabilities of sensors cannot meet the latency requirements for complex tasks, and the limited battery capacity of these sensors hinders the long-term execution of monitoring tasks. Mobile edge computing (MEC) acts as an effective solution for this [...] Read more.
In forest monitoring networks, the computational capabilities of sensors cannot meet the latency requirements for complex tasks, and the limited battery capacity of these sensors hinders the long-term execution of monitoring tasks. Mobile edge computing (MEC) acts as an effective solution for this issue by offloading tasks to edge servers, significantly reducing both task latency and energy consumption. However, the computational capacity of MEC servers and the bandwidth in the system are limited, and the communication environment in forested areas is complex. To simulate the complexity of the forest communication environment, we incorporate empirical path loss and multipath fading into the calculation of signal transmission rates. The computational offloading problem is then converted into a minimum-cost optimization problem with multiple constraints related to energy consumption and latency, which we formulate as an NP-hard problem. We propose a dung beetle optimization (DBO) strategy for computational offloading, enhancing it with an improved circle chaotic mapping, a dimension decomposition strategy, and Cauchy disturbance. This algorithm has the beauty of symmetry in the search range, and the symmetrical features can comprehensively search for existing solutions. Experimental results demonstrate that the improved dung beetle optimization algorithm (IDBO) achieves better convergence, lower complexity, and superior optimization outcomes compared to local offloading strategies and other metaheuristic algorithms, confirming the effectiveness of the proposed algorithm and ensuring the service quality of the forest monitoring network. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)
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Review

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25 pages, 476 KiB  
Review
Quality of Experience-Oriented Cloud-Edge Dynamic Adaptive Streaming: Recent Advances, Challenges, and Opportunities
by Wei Wang, Xuekai Wei, Wei Tao, Mingliang Zhou and Cheng Ji
Symmetry 2025, 17(2), 194; https://doi.org/10.3390/sym17020194 - 26 Jan 2025
Viewed by 808
Abstract
The widespread adoption of dynamic adaptive streaming (DAS) has revolutionized the delivery of high-quality internet multimedia content by enabling dynamic streaming quality adjustments based on network conditions and playback capabilities. While numerous reviews have explored DAS technologies, this study differentiates itself by focusing [...] Read more.
The widespread adoption of dynamic adaptive streaming (DAS) has revolutionized the delivery of high-quality internet multimedia content by enabling dynamic streaming quality adjustments based on network conditions and playback capabilities. While numerous reviews have explored DAS technologies, this study differentiates itself by focusing on Quality of Experience (QoE)-oriented optimization in cloud-edge collaborative environments. Traditional DAS optimization often overlooks the asymmetry between cloud and edge nodes, where edge resources are typically constrained. This review emphasizes the importance of dynamic task and traffic allocation between cloud and edge nodes to optimize resource utilization and maintain system efficiency, ultimately improving QoE for end users. This comprehensive analysis explores recent advances in QoE-driven DAS optimization strategies, including streaming models, implementation mechanisms, and the integration of machine learning (ML) techniques. By contrasting ML-based DAS approaches with traditional methods, this study highlights the added value of intelligent algorithms in addressing modern streaming challenges. Furthermore, the review identifies emerging research directions, such as adaptive resource allocation and hybrid cloud-edge solutions, and underscores potential application areas for DAS in evolving multimedia systems. With the aim of serving as a valuable resource for researchers, practitioners, and decision-makers in addressing the challenges of resource-constrained edge environments and the need for QoE-centric solutions, this comprehensive analysis endeavors to promote the development, implementation, and application of DAS optimization. Acknowledging the crucial role of DAS optimization in improving the overall QoE for the end users, we hope to facilitate the continued advancement of video streaming experiences in the cloud-edge collaborated environment. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Embedded Systems)
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