Special Issue Information
With a massive amount of data being generated by an increasing number of applications, future systems can learn to be more intelligent and to guarantee better services for users. The techniques of artificial intelligence (AI) and machine learning (ML) can help in building such systems, as they deliver an excellent advantage for studying the essential characteristics of the system as well as data collected from it. More recently, intelligent IoT systems have emerged as a topic that considers the use of AI/ML to enhance performance, enable faster customization, and optimize user experience. This is an important step in the context of autonomic and cognitive system management.
This Special Issue targets research results in the above area of intelligent systems or networks. Contributions should focus on topics such as artificial intelligence techniques and models for IoT network and service management. This also includes personalization, smart service orchestration and delivery, dynamic service function chaining, intent- and policy-based management, and centralized vs. distributed control of SDN/NFV-based networks. Moreover, articles on analytics and big data approaches, knowledge creation, and decision-making are particularly welcome. This Special Issue aims to provide a comprehensive overview of the state-of-the-art development in IoT systems; it also aims to explore novel concepts and practices with a long-term goal of fully automated systems via the technological advances of AI/ML in a wide range of applications.
We invite authors from both industry and academia working on applying methods and techniques of AI/ML to computer systems to submit original research or review articles that cover design, implementation, and optimization with a specific focus on models, protocols, and optimization algorithms.
Potential topics include, but are not limited to, the following:
- AI/ML for software-defined IoT;
- AI/ML for network optimization in IoT;
- Deep and reinforcement learning for IoT data;
- Data mining and big data analytics in IoT networks;
- AI/ML for network management and orchestration for IoT;
- Machine learning for user behavior modeling and prediction in IoT;
- Innovative architectures and infrastructures for intelligent IoT systems;
- Self-learning and adaptive protocols and algorithms for intelligent IoT systems.
Dr. Kandaraj Piamrat
Dr. Hyunhee Park
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 papers will be 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. Electronics 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 1500 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.
Special Issue Editors
Interests: wireless networks; multimedia streaming; MAC design; QoE; vehicular networks; artificial intelligence; machine learning
Interests: wireless communications; MAC protocol design; big data analysis; self-driving vehicles; security systems