Intelligent Modeling, Learning, and Optimization for Next-Generation IoT, Edge, and Wireless Networks

Dear Colleagues,

The rapid advancement of the Internet of Things (IoT), edge computing, and wireless communication technologies is fundamentally transforming the architecture of modern connected systems. As these networks continue to grow in scale, heterogeneity, and density, conventional modeling and optimization techniques face increasing limitations in capturing the complex interactions among devices, communication links, and network services.

To address these challenges, learning-based and data-driven methodologies have emerged as key enablers of intelligent network design and operation. These approaches enable systems to dynamically adapt to evolving environments, thereby supporting more efficient, scalable, and robust solutions. In addition, intelligent modeling techniques provide structured representations of network behavior, facilitating deeper insights and more effective decision-making across distributed and heterogeneous network infrastructures.

Learning, modeling, and optimization each offer significant opportunities to enhance network performance and efficiency. Data-driven learning enables adaptive and context-aware decision-making, while intelligent modeling captures complex network dynamics. In parallel, advanced optimization techniques improve resource allocation, traffic management, routing, and quality of service in ultra-dense and complex network environments.

This Special Issue aims to bring together recent advances in intelligent modeling, learning, and optimization for IoT, edge, and wireless networks. We invite high-quality and original contributions presenting novel theories, methodologies, algorithms, and practical applications that address key challenges in next-generation IoT, edge computing, and wireless networks. Topics of interest include, but are not limited to, the following areas:

• Learning-based methods for network design and optimization;
• Data-driven and AI-enabled network intelligence;
• Intelligent resource allocation and traffic management;
• Edge intelligence and distributed learning frameworks;
• Network modeling and performance analysis;
• Real-world implementations and experimental studies.

Prof. Dr. Illsoo Sohn
Dr. Gilang Raka Rayuda Dewa
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 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. Telecom 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 1400 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.

• intelligent network
• modeling
• learning-based technique
• optimization
• internet of things (IoT)
• edge computing
• wireless networks