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Computers
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Mobile Fog and Edge Computing
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Mobile Fog and Edge Computing

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4867

Special Issue Editor

Dr. Guangsheng Feng

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Engineering University, Harbin, China
Interests: network technology; information security; group intelligence system; intelligent security; wireless indoor positioning technology; internet of things; edge intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The interconnection of AI devices marks a pivotal event in the transition of society from the information age to the era of intelligence. Therefore, with the widespread adoption and application of an increasing number of intelligent devices, particularly mobile smart devices, traditional computational paradigms are encountering significant challenges in deployment, testing, and implementation. More flexible, data-proximate, and highly real-time computing architectures have become a focal point of joint interest in both academia and industry.

 Accordingly, this Special Issue focuses on mobile fog and edge computing, aiming to foster innovative discussions on corresponding topics to advance the development of computing paradigms adapted to the next generation of networks.

Topics of interest include, but are not limited to, the following:

  1. Design, testing, performance evaluation, and security analysis of next-generation mobile fog computing and edge computing paradigms;
  2. The training and deployment of artificial intelligence models based on novel computing paradigms;
  3. Design and analysis of network communication protocols in mobile computing scenarios;
  4. Optimization of novel applications and algorithmic research in mobile computing contexts;
  5. Design and analysis of next-generation Internet of Things (IoT) network protocols;
  6. Autonomous collaborative optimization and decision-making among multiple intelligent agents in mobile computing scenarios;
  7. 5G/6G-enabled mobile computing scenarios;
  8. End-to-end LLMs applications in mobile computing scenarios;
  9. 9. Multimodal large model applications for mobile computing scenarios.

Dr. Guangsheng Feng
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. Computers 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 1800 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

  • mobile fog and edge computing
  • mobile AI paradigm
  • next-generation IoT
  • mobile autonomous collaborative
  • 5G/6G mobile computing

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Published Papers (5 papers)

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Research

18 pages, 1847 KB  
Article
A GNN-Based Log Anomaly Detection Framework with Prompt Learning for Edge Computing
by Xianlang Hu, Guangsheng Feng, Xinling Huang, Xiangying Kong and Hongwu Lv
Computers 2026, 15(5), 273; https://doi.org/10.3390/computers15050273 - 24 Apr 2026
Viewed by 277
Abstract
System logs have been critical for analyzing the operational status and abnormal behavior of highly distributed and heterogeneous edge computing nodes. In edge environments, logs exhibit cross-event and cross-field structural interactions, making it difficult to uncover potential anomaly patterns from isolated events. Moreover, [...] Read more.
System logs have been critical for analyzing the operational status and abnormal behavior of highly distributed and heterogeneous edge computing nodes. In edge environments, logs exhibit cross-event and cross-field structural interactions, making it difficult to uncover potential anomaly patterns from isolated events. Moreover, sparse annotations and varying log formats limit the effectiveness of existing methods. To address these challenges, we propose a graph neural network (GNN) anomaly detection framework with prompt learning. It leverages few-shot prompt learning to automatically extract key fields and constructs a weighted directed graph that jointly models semantic embeddings and temporal dependencies, fully representing the structural interactions and semantic associations across events and fields. Furthermore, the framework performs graph-level anomaly detection by jointly optimizing graph representation learning and classification objective within an enhanced one-class directed graph convolutional network, enabling effective identification of global structural anomaly patterns in log graphs. Experimental results demonstrate that the proposed method achieves an average F1-score of 93.3%, surpassing the current state-of-the-art (SOTA) methods by 6.93%. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing)
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28 pages, 2619 KB  
Article
A Dynamic Clustering Framework for Intelligent Task Orchestration in Mobile Edge Computing
by Mona Alghamdi, Atm S. Alam and Asma Cherif
Computers 2026, 15(4), 214; https://doi.org/10.3390/computers15040214 - 1 Apr 2026
Viewed by 518
Abstract
Mobile edge computing (MEC) enables resource-constrained mobile devices to execute delay-sensitive and compute-intensive applications by offloading tasks to nearby edge servers. However, task orchestration in MEC is challenged by the highly dynamic system conditions, unreliable networks, and distributed edge environments. Moreover, as the [...] Read more.
Mobile edge computing (MEC) enables resource-constrained mobile devices to execute delay-sensitive and compute-intensive applications by offloading tasks to nearby edge servers. However, task orchestration in MEC is challenged by the highly dynamic system conditions, unreliable networks, and distributed edge environments. Moreover, as the number of mobile users, tasks, and distributed computing resources (edge/cloud servers) increases, the task orchestration process becomes more complex due to the expanded decision space and the need to efficiently allocate heterogeneous resources under latency and capacity constraints. As the decision space grows, exhaustive-search-based orchestration becomes computationally infeasible. Clustering approaches often rely on proximity-only grouping, while learning-based solutions require extensive training and parameter tuning. To address these challenges, this paper proposes a Multi-Criteria Hierarchical Clustering-based Task Orchestrator (MCHC-TO), a novel framework that integrates multi-criteria decision making with divisive hierarchical clustering for preference-aware and adaptive workload orchestration. Edge servers are first evaluated using multiple decision criteria, and the resulting preference rankings are exploited to form hierarchical preference-based clusters. Incoming tasks are then assigned to the most suitable cluster based on task requirements, enabling efficient resource utilization and dynamic decision-making. Extensive simulations conducted using an edge computing simulator demonstrate that the proposed MCHC-TO framework consistently outperforms benchmark approaches, achieving reductions in average service delay and task failure rate of up to 48% and 92%, respectively. These results highlight the effectiveness of combining multi-criteria evaluation with hierarchical clustering for robust and dynamic task orchestration in MEC environments. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing)
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Graphical abstract

25 pages, 705 KB  
Article
Privacy-Preserving Set Intersection Protocol Based on SM2 Oblivious Transfer
by Zhibo Guan, Hai Huang, Haibo Yao, Qiong Jia, Kai Cheng, Mengmeng Ge, Bin Yu and Chao Ma
Computers 2026, 15(1), 44; https://doi.org/10.3390/computers15010044 - 10 Jan 2026
Viewed by 631
Abstract
Private Set Intersection (PSI) is a fundamental cryptographic primitive in privacy-preserving computation and has been widely applied in federated learning, secure data sharing, and privacy-aware data analytics. However, most existing PSI protocols rely on RSA or standard elliptic curve cryptography, which limits their [...] Read more.
Private Set Intersection (PSI) is a fundamental cryptographic primitive in privacy-preserving computation and has been widely applied in federated learning, secure data sharing, and privacy-aware data analytics. However, most existing PSI protocols rely on RSA or standard elliptic curve cryptography, which limits their applicability in scenarios requiring domestic cryptographic standards and often leads to high computational and communication overhead when processing large-scale datasets. In this paper, we propose a novel PSI protocol based on the Chinese commercial cryptographic standard SM2, referred to as SM2-OT-PSI. The proposed scheme constructs an oblivious transfer-based Oblivious Pseudorandom Function (OPRF) using SM2 public-key cryptography and the SM3 hash function, enabling efficient multi-point OPRF evaluation under the semi-honest adversary model. A formal security analysis demonstrates that the protocol satisfies privacy and correctness guarantees assuming the hardness of the Elliptic Curve Discrete Logarithm Problem. To further improve practical performance, we design a software–hardware co-design architecture that offloads SM2 scalar multiplication and SM3 hashing operations to a domestic reconfigurable cryptographic accelerator (RSP S20G). Experimental results show that, for datasets with up to millions of elements, the presented protocol significantly outperforms several representative PSI schemes in terms of execution time and communication efficiency, especially in medium and high-bandwidth network environments. The proposed SM2-OT-PSI protocol provides a practical and efficient solution for large-scale privacy-preserving set intersection under national cryptographic standards, making it suitable for deployment in real-world secure computing systems. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing)
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20 pages, 2958 KB  
Article
pFedKA: Personalized Federated Learning via Knowledge Distillation with Dual Attention Mechanism
by Yuanhao Jin, Kaiqi Zhang, Chao Ma, Xinxin Cheng, Luogang Zhang and Hongguo Zhang
Computers 2025, 14(12), 504; https://doi.org/10.3390/computers14120504 - 21 Nov 2025
Viewed by 1284
Abstract
Federated learning in heterogeneous data scenarios faces two key challenges. First, the conflict between global models and local personalization complicates knowledge transfer and leads to feature misalignment, hindering effective personalization for clients. Second, the lack of dynamic adaptation in standard federated learning makes [...] Read more.
Federated learning in heterogeneous data scenarios faces two key challenges. First, the conflict between global models and local personalization complicates knowledge transfer and leads to feature misalignment, hindering effective personalization for clients. Second, the lack of dynamic adaptation in standard federated learning makes it difficult to handle highly heterogeneous and changing client data, reducing the global model’s generalization ability. To address these issues, this paper proposes pFedKA, a personalized federated learning framework integrating knowledge distillation and a dual-attention mechanism. On the client-side, a cross-attention module dynamically aligns global and local feature spaces using adaptive temperature coefficients to mitigate feature misalignment. On the server-side, a Gated Recurrent Unit-based attention network adaptively adjusts aggregation weights using cross-round historical states, providing more robust aggregation than static averaging in heterogeneous settings. Experimental results on CIFAR-10, CIFAR-100, and Shakespeare datasets demonstrate that pFedKA converges faster and with greater stability in heterogeneous scenarios. Furthermore, it significantly improves personalization accuracy compared to state-of-the-art personalized federated learning methods. Additionally, we demonstrate privacy guarantees by integrating pFedKA with DP-SGD, showing comparable privacy protection to FedAvg while maintaining high personalization accuracy. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing)
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Graphical abstract

26 pages, 5143 KB  
Article
Research on the Application of Federated Learning Based on CG-WGAN in Gout Staging Prediction
by Junbo Wang, Kaiqi Zhang, Zhibo Guan, Zi Ye, Chao Ma and Hai Huang
Computers 2025, 14(11), 455; https://doi.org/10.3390/computers14110455 - 23 Oct 2025
Viewed by 1028
Abstract
Traditional federated learning frameworks face significant challenges posed by non-independent and identically distributed (non-IID) data in the healthcare domain, particularly in multi-institutional collaborative gout staging prediction. Differences in patient population characteristics, distributions of clinical indicators, and proportions of disease stages across hospitals lead [...] Read more.
Traditional federated learning frameworks face significant challenges posed by non-independent and identically distributed (non-IID) data in the healthcare domain, particularly in multi-institutional collaborative gout staging prediction. Differences in patient population characteristics, distributions of clinical indicators, and proportions of disease stages across hospitals lead to inefficient model training, increased category prediction bias, and heightened risks of privacy leakage. In the context of gout staging prediction, these issues result in decreased classification accuracy and recall, especially when dealing with minority classes. To address these challenges, this paper proposes FedCG-WGAN, a federated learning method based on conditional gradient penalization in Wasserstein GAN (CG-WGAN). By incorporating conditional information from gout staging labels and optimizing the gradient penalty mechanism, this method generates high-quality synthetic medical data, effectively mitigating the non-IID problem among clients. Building upon the synthetic data, a federated architecture is further introduced, which replaces traditional parameter aggregation with synthetic data sharing. This enables each client to design personalized prediction models tailored to their local data characteristics, thereby preserving the privacy of original data and avoiding the risk of information leakage caused by reverse engineering of model parameters. Experimental results on a real-world dataset comprising 51,127 medical records demonstrate that the proposed FedCG-WGAN significantly outperforms baseline models, achieving up to a 7.1% improvement in accuracy. Furthermore, by maintaining the composite quality score of the generated data between 0.85 and 0.88, the method achieves a favorable balance between privacy preservation and model utility. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing)
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