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Mathematics
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New Advances in Distributed Systems, Edge Intelligence, and Artificial Intelligence
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New Advances in Distributed Systems, Edge Intelligence, and Artificial Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 13787

Special Issue Editors

Dr. Xiaoming Yuan

E-Mail Website
Guest Editor
Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
Interests: artificial intelligence; edge intelligence; resource allocation; Internet of Vehicles; wireless networking
Dr. Lei Liu

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: intelligent transportation systems; internet of vehicles; distributed computing
Special Issues, Collections and Topics in MDPI journals
Dr. Yulan Gao

E-Mail Website
Guest Editor
KTH Royal Institute of Technology, Division of Information Science and Engineering, School of Electronic, Stockholm, Sweden
Interests: device-to-device communication; telecommunication systems; quality of service

Special Issue Information

Dear Colleagues,

This Special Issue intends to inform readers of the latest advances in distributed systems, edge intelligence, and artificial intelligence with a mathematical lens, highlighting the transformative influence that these technologies exert on the rapidly changing digital landscape of today. As concerns distributed systems, we focus on the latest advancements in architectural optimization, fault tolerance and resource management. These advancements rely heavily on sophisticated mathematical models to enable efficient large-scale data processing and service delivery. Moreover, we explore emerging patterns, high-performance computing technologies and innovative deployment and optimization strategies. Edge intelligence integrates cloud computing and IoT, leveraging advanced mathematical models to facilitate real-time data processing and analysis. These models significantly reduce latency and enhance user experiences in diverse applications such as smart cities, autonomous driving and remote healthcare. Artificial intelligence continues to make significant strides in algorithm optimization, model training and knowledge graph construction, contributing to positive changes in various industries. We explore the algorithmic innovations, intelligent services, and ethical considerations within distributed and edge computing environments. We invite researchers and practitioners to contribute their original research and review articles, sharing their insights and experiences in relation to these exciting and rapidly evolving fields. 

Dr. Xiaoming Yuan
Dr. Lei Liu
Dr. Yulan Gao
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. Mathematics 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 2600 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

  • distributed computing frameworks
  • distributed systems
  • system optimization
  • control optimization
  • mathematical optimization methods
  • edge intelligence
  • graph theory
  • distributed storage
  • computational methods
  • quality of service (QoS) optimization
  • Internet of Things (IoT)
  • deep learning
  • federated learning
  • security and privacy protection
  • generated artificial intelligence
  • prediction models
  • digital twin technologies
  • distributed multiple resource management

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Published Papers (4 papers)

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Research

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31 pages, 1841 KB  
Article
Joint Scheduling and Placement for Vehicular Intelligent Applications Under QoS Constraints: A PPO-Based Precedence-Preserving Approach
by Wei Shi and Bo Chen
Mathematics 2025, 13(19), 3130; https://doi.org/10.3390/math13193130 - 30 Sep 2025
Viewed by 304
Abstract
The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering [...] Read more.
The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering while allowing concurrency. We propose a task offloading framework that decomposes applications into precedence-constrained subtasks and formulates the joint scheduling and offloading problem as a Markov Decision Process (MDP) to capture the latency–energy trade-off. The system state incorporates vehicle positions, wireless link quality, server load, and task-buffer status. To address the high dimensionality and sequential nature of scheduling, we introduce DepSchedPPO, a dependency-aware sequence-to-sequence policy that processes subtasks in topological order and generates placement decisions using action masking to ensure partial-order feasibility. This policy is trained using Proximal Policy Optimization (PPO) with clipped surrogates, ensuring stable and sample-efficient learning under dynamic task dependencies. Extensive simulations show that our approach consistently reduces task latency, energy consumption and QOS compared to conventional heuristic and DRL-based methods. The proposed solution demonstrates strong applicability to real-time vehicular scenarios such as autonomous navigation, cooperative sensing, and edge-based perception. Full article
(This article belongs to the Special Issue New Advances in Distributed Systems, Edge Intelligence, and Artificial Intelligence)
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21 pages, 1207 KB  
Article
Flash-Attention-Enhanced Multi-Agent Deep Deterministic Policy Gradient for Mobile Edge Computing in Digital Twin-Powered Internet of Things
by Yuzhe Gao, Xiaoming Yuan, Songyu Wang, Lixin Chen, Zheng Zhang and Tianran Wang
Mathematics 2025, 13(13), 2164; https://doi.org/10.3390/math13132164 - 2 Jul 2025
Viewed by 956
Abstract
Offloading decisions and resource allocation problems in mobile edge computing (MEC) emerge as key challenges as they directly impact system performance and user experience in dynamic and resource-constrained Internet of Things (IoT) environments. This paper constructs a comprehensive and layered digital twin (DT) [...] Read more.
Offloading decisions and resource allocation problems in mobile edge computing (MEC) emerge as key challenges as they directly impact system performance and user experience in dynamic and resource-constrained Internet of Things (IoT) environments. This paper constructs a comprehensive and layered digital twin (DT) model for MEC, enabling real-time cooperation with the physical world and intelligent decision making. Within this model, a novel Flash-Attention-enhanced Multi-Agent Deep Deterministic Policy Gradient (FA-MADDPG) algorithm is proposed to effectively tackle MEC problems. It enhances the model by arming a critic network with attention to provide a high-quality decision. It also changes a matrix operation in a mathematical way to speed up the training process. Experiments are performed in our proposed DT environment, and results demonstrate that FA-MADDPG has good convergence. Compared with other algorithms, it achieves excellent performance in delay and energy consumption under various settings, with high time efficiency. Full article
(This article belongs to the Special Issue New Advances in Distributed Systems, Edge Intelligence, and Artificial Intelligence)
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Review

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44 pages, 1049 KB  
Review
Toward Intelligent AIoT: A Comprehensive Survey on Digital Twin and Multimodal Generative AI Integration
by Xiaoyi Luo, Aiwen Wang, Xinling Zhang, Kunda Huang, Songyu Wang, Lixin Chen and Yejia Cui
Mathematics 2025, 13(21), 3382; https://doi.org/10.3390/math13213382 - 23 Oct 2025
Viewed by 670
Abstract
The Artificial Intelligence of Things (AIoT) is rapidly evolving from basic connectivity to intelligent perception, reasoning, and decision making across domains such as healthcare, manufacturing, transportation, and smart cities. Multimodal generative AI (GAI) and digital twins (DTs) provide complementary solutions. DTs deliver high-fidelity [...] Read more.
The Artificial Intelligence of Things (AIoT) is rapidly evolving from basic connectivity to intelligent perception, reasoning, and decision making across domains such as healthcare, manufacturing, transportation, and smart cities. Multimodal generative AI (GAI) and digital twins (DTs) provide complementary solutions. DTs deliver high-fidelity virtual replicas for real-time monitoring, simulation, and optimization with GAI enhancing cognition, cross-modal understanding, and the generation of synthetic data. This survey presents a comprehensive overview of DT–GAI integration in the AIoT. We review the foundations of DTs and multimodal GAI and highlight their complementary roles. We further introduce the Sense–Map–Generate–Act (SMGA) framework, illustrating their interaction through the SMGA loop. We discuss key enabling technologies, including multimodal data fusion, dynamic DT evolution, and cloud–edge–end collaboration. Representative application scenarios, including smart manufacturing, smart cities, autonomous driving, and healthcare, are examined to demonstrate their practical impact. Finally, we outline open challenges, including efficiency, reliability, privacy, and standardization, and we provide directions for future research toward sustainable, trustworthy, and intelligent AIoT systems. Full article
(This article belongs to the Special Issue New Advances in Distributed Systems, Edge Intelligence, and Artificial Intelligence)
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21 pages, 3197 KB  
Review
Deploying AI on Edge: Advancement and Challenges in Edge Intelligence
by Tianyu Wang, Jinyang Guo, Bowen Zhang, Ge Yang and Dong Li
Mathematics 2025, 13(11), 1878; https://doi.org/10.3390/math13111878 - 4 Jun 2025
Cited by 8 | Viewed by 11227
Abstract
In recent years, artificial intelligence (AI) has achieved significant progress and remarkable advancements across various disciplines, including biology, computer science, and industry. However, the increasing complexity of AI network structures and the vast number of associated parameters impose substantial computational and storage demands, [...] Read more.
In recent years, artificial intelligence (AI) has achieved significant progress and remarkable advancements across various disciplines, including biology, computer science, and industry. However, the increasing complexity of AI network structures and the vast number of associated parameters impose substantial computational and storage demands, severely limiting the practical deployment of these models on resource-constrained edge devices. Although edge intelligence methods have been proposed to alleviate the computational and storage burdens, they still face multiple persistent challenges, such as large-scale model deployment, poor interpretability, privacy and security vulnerabilities, and energy efficiency constraints. This article systematically reviews the current advancements in edge intelligence technologies, highlights key enabling techniques including model sparsity, quantization, knowledge distillation, neural architecture search, and federated learning, and explores their applications in industrial, automotive, healthcare, and consumer domains. Furthermore, this paper presents a comparative analysis of these techniques, summarizes major trade-offs, and proposes decision frameworks to guide deployment strategies under different scenarios. Finally, it discusses future research directions to address the remaining technical bottlenecks and promote the practical and sustainable development of edge intelligence. Standing at the threshold of an exciting new era, we believe edge intelligence will play an increasingly critical role in transforming industries and enabling ubiquitous intelligent services. Full article
(This article belongs to the Special Issue New Advances in Distributed Systems, Edge Intelligence, and Artificial Intelligence)
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