Special Issue on Next-Generation Artificial Intelligence for Edge Computing and Industrial Internet of Things
Next-generation artificial intelligence (NGAI) such as machine-learning, deep-learning and beyond is projected to be very complicated and dynamic. Moreover, in industrial internet of things (IIoT), the rise of ultra-dense heterogeneous network deployment, high data rates, and new applications that necessitate a new wireless radio technology paradigm may also offer numerous important problems for network administration, operation, planning, and troubleshooting. In recent years, mobile edge computing and the internet of things (IoT) have been utilized in mobile networks that offer a barrier to new technology demands. The major emphasis of technological development is data transmission and decreasing throughput and network stress. The network focused on growing demands for data storage, computing, and low latency treatment in areas such as smart cities, transportation, smart grids, and many other sustainable settings. Blockchain is fundamentally a great companion to IoT, enabling trust formation, repository, and transfer due to enhanced interoperability, dependability, and scalability. The confluence of edge intelligence and blockchain has a natural benefit since they have similar demands for data analysis, security, and trust. However, the combination of blockchain and edge intelligence remains a mystery. The aim of this special issue is to disseminate the latest research and innovation in the fields of next-generation artificial intelligence, edge computing and industrial internet of things.
Topics of interest include, but are not limited to, the following areas:
- Next-generation artificial intelligence for optimizing the edge computing networks
- Parallel and distributed edge computing/networking
- Edge/IIoT based wireless connections using AI
- Machine/deep-learning for edge computing networks
- Federated learning based approaches for wireless communications
- Training scheme and modeling of edge computing using AI
- New AI based edge/fog computing methods
Manuscript preparation and submission
Computer Assisted Methods in Engineering and Science (CAMES) is a refereed international journal published quarterly and indexed by Scopus and EBSCO databases.
All manuscripts must be submitted through the journal website: https://cames.ippt.pan.pl/index.php/cames/about/submissions
Submission must be clearly marked in the system as “Next-Generation Artificial Intelligence for Edge Computing and Industrial IoT”. The publication in this Special Issue is free of charge for the Authors and the published manuscript will be freely available for the Readers through the Journal website (https://cames.ippt.pan.pl).
Submission Deadline: October 30, 2022
Dr. Gaurav Dhiman (Corresponding Guest Editor), Department of Computer Science, Government Bikram College of Commerce and University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, India
Prof. Atulya Nagar, Pro Vice-Chancellor (Research), Liverpool Hope University, United Kingdom
Prof. Seifedine Kadry, Professor of Data Science, Department of Applied Data Science, Noroff University College, Norway
Prof. Michał Kleiber – Past ECCOMAS President, Institute of Fundamental Technological Research, Warsaw, Poland
Prof. Tadeusz Burczyński – Chairman of TC on Computational Solids & Structural Mechanics of ECCOMAS, Director of Institute of Fundamental Technological Research, Warsaw, Poland