Collaborative computing can effectively foster collaborations in a workforce, optimise the productivity, and, the most importantly, foster innovations by collaborative thinking and sharing applications. Collaborative computing can be implemented via a variety of tools. The cost of these tools is affordable nowadays because of the advancement of electronics, communications, and cloud computing, which further promotes the applications of collaborative computing. However, because of the nature of participation of multiple users and dynamics, to maintain high performance of these collaborative tools is still a challenge.
Artificial Intelligence (AI) has been witnessed as one of the fastest growing technologies in our life to optimise the resources and make our daily life more convenient. For example, face recognition based security check can enable a secure and fast security-control process at airports, Amazon Alexa can support the automation of our home, and various customer services supported by AI. AI is also supposed to be one of the most useful tools to optimise the performance for collaborative computing.
However, there are still several challenges for implementing AI algorithms in collaborative computing. Firstly, scheduling is still a challenge, such as tasks allocation and functions splitting to match the software logic and hardware distribution in such a complex scenario. Secondly, the training data and models are critical in applying AI algorithms. How we can make sure to always catch the correct information and modify the model based on the local variation trends is another challenge. At last, the data communications between sub-processes may become the bottleneck in complicated services, including network topology, network reliability, data size, and so on.
The aim of this special issue is to explore recent advances in AI technologies in the collaborative computing field, which can address the theoretical and practical challenges for modern services and applications. This special issue will bring together leading researchers and developers to present their latest research on AI algorithms design, system framework, collaborative network modeling, and architecture, as well as optimisation in different layers. Original research and review articles in this area are welcome.
- Topics of interest include, but are not limited to, the following scope:
- AI based services and applications in collaborative computing
- AI and big data in collaborative computing
- Deployments of AI middleware in collaborative computing
- System design, framework architecture related to AI in collaborative computing
- Optimization of AI in collaborative computing
- AI modelling and evolution in collaborative computing
- Testbeds, benchmarks, and public dataset of AI in collaborative computing
- AI system performance assessment and evaluation methods
- AI based privacy safety and personal data protection in collaborative computing
- AI related IoT, Edge computing, and CPS in collaborative computing
- AI driven software testing and verification for collaborative computing
Manuscript submission deadline: Sep 30 2020
Notification of acceptance: Dec 30 2020
Submission of final revised paper: March 30 2021
Publication of special issue (tentative): June 2021
Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through http://www.editorialmanager.com/mone/.
A copy of the manuscript should also be emailed to the Guest Editors at the following email address(es) email@example.com, firstname.lastname@example.org, and email@example.com
- Xinheng Wang, Xi’an Jiaotong-Liverpool University (XJTLU), China
- Honghao Gao, Shanghai University, China
- Kaizhu Huang, Xi’an Jiaotong-Liverpool University, China