- Manuscript Submission Deadline: November 22, 2023
- Notification to Authors: January 24, 2024
- Revision Deadline: February 28, 2024
Publication: May/June 2024
The concept of Web3.0 represents a significant advancement in the Internet’s evolution. Different from Web1.0 and Web2.0 based on “read” and “read-write” respectively, Web3.0, a distributed and user-centric internet, is proposed to empower users by enabling them to own and control their digital assets, and aiming to vest power from large corporations to individual users. As a distributed user-centric and semantic-dependent architecture, Web3.0 requires an efficient identity management and data processing system to support users’ requirements. Thus, an efficient and reliable approach to distributed network management is crucial in enhancing the service level and user experience of Web3.0. One promising technology in this regard is blockchain and distributed ledger technology, which have shown great potential for use in Web3.0 and demand further research and development.
The integration of edge computing and artificial intelligence known as Edge Intelligence has the potential to significantly enhance the management and operational capabilities of the Web3.0 architecture. By collaborating the distributed devices to achieve a system-wide resource utilization, users can enjoy a better experience and the service providers could achieve a management approach in a more load-balanced, low-cost, and high-service-quality manner. With conventional communication and network technologies, the features and value of distribution are hard to be made the most of, thus significantly restricting the communication efficiency and wasting the computing power of devices. To tackle these problems, edge computing can be applied in Web3.0 to enhance the utilization of idle edge computing resources. Furthermore, empowered by artificial intelligence, edge intelligence fosters the scheduling of task processing in the system-wide view by offloading tasks to other edge devices for acceleration and load balance, etc., which increases the efficiency, robustness, and robustness of management. Generally, mechanisms such as service offload, load balance, resource allocation, and content cache shall be optimized to refine the communication, computing, and storage of service in Web3.0. In order to further explore the combination of Web3.0 and edge intelligence and promote its progress, this feature topic solicits contributions of human-centric innovations and applications in the edge intelligence enabled Web3.0. Topics of interest include, but are not limited to:
- Incentive and consensus mechanisms based on edge intelligence for Web3.0
- Architecture and protocol design for edge computing-empowered Web3.0
- Federated learning, reinforcement learning, and other emerging technologies for intelligent Web3.0
- Design or implementation of hardware and infrastructure combing edge intelligence and Web3.0
- Fundamental studies and theoretic guidance for edge intelligence-driven Web3.0
- Semantic computing with edge intelligence in Web3.0
- Intelligent edge devices management approaches in Web3.0
- Information-Centric Networking for the edge intelligence in Web3.0
- Novel edge-based artificial intelligence application optimization in Web3.0
- Web3.0-driven edge intelligence applications
- Standardization for the edge intelligence in Web3.0
- Mobile Edge Computing Architectures and Designs to support Web 3.0 Services over 5G/6G Access Technologies
Only submissions that describe previously unpublished, Contemporary research and practice that are not currently under review by a conference or another journal will be considered. Extended versions of conference papers must be at least 30 percent different from the original conference works. Feature articles should be no longer than 4,200 words and have no more than 20 references (with tables and figures counting as 300 words each). Articles should be understandable by a broad audience of computer science and engineering professionals, avoiding unnecessary theory, mathematics, jargon, or abstract concepts. For author guidelines, see the Author Information page.
All manuscripts must be submitted to ScholarOne Manuscripts by the deadline, making sure that the specific special issue is selected in order to be considered for publication under this Call for Papers. Submissions are subject to peer review on both technical merit and relevance to IT Professional’s readership.
The use of artificial intelligence (AI)–generated text in an article should be disclosed in the acknowledgements section, while the sections of the paper that present AI-generated text verbatim should be quoted within quotation marks and provide a citation to the AI system used to generate the text.
IT Professional magazine is a hybrid publication, allowing either traditional manuscript submission or author-paid Open Access manuscript submission.
Contact the guest editors at firstname.lastname@example.org.
- Editorial Board Member: Georgia Sakellari, University of Greenwich, UK
- Muhammad Bilal, Hankuk University of Foreign Studies, South Korea
- Xiaolong Xu, Nanjing University of Information Science and Technology, China
- Ravishankar Ravindran, F5 Networks, United States