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: 30 September 2025 | Viewed by 659
Special Issue Editors
Interests: artificial intelligence; edge intelligence; resource allocation; Internet of Vehicles; wireless networking
Interests: intelligent transportation systems; Internet of Vehicles; distributed computing
Special Issues, Collections and Topics in MDPI journals
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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