Mobile Networks and Applications - SIA-IoT: Machine Learning and Big Data Analytics for IoT-connected Smart and Intelligent Applications
Overview:
Because of the expanding global population and the quickening rate of urbanization and mobility, there is a compelling need to improve cities on a worldwide scale. By 2050, it is anticipated that 67% of the world's population will live in cities. The efficient provision of services to citizens becomes increasingly important as urban population continues to rise due to increased demand in migration, mobility and interconnectivity. Every day, more and more people realize how crucial it is to have access to high-quality services and live in communities that are intelligent and smart. The advancement in Internet of Things (IoT) due to network connectivity and mobility has attracted the attention of the research community in recent years. These devices of IoT generate massive data that require specialized algorithms, tools and techniques to maintain the vast amounts of data generated by the devices. The growth of smart cities and various other applications of IoT is producing enormous amounts of data at an extraordinary rate. Unfortunately, the insufficiency of accepted standards of mobility and network connectivity methods causes the majority of produced data to be washed away without yielding meaningful knowledge and information. Additionally, in order to achieve analytics and learn in real-time, new machine learning and deep learning approaches must be flexible enough to deal with the dynamic nature of data generated by various applications of IoT.
The difficulty of underutilizing the Big Data produced by IoT applications is explored from a machine learning, deep learning and various other perspective in this special issue. To fit the nature of the data generated in the IoT-based applications, a learning framework is needed. It also needs to be scalable enough to meet the needs of the services provided by these applications.
The main goal and objective of this Special Issue is to invite the researchers of EAI BigIoT-EDU 2022 to provide software-based and hardware-based solutions for solving such problems. The connectivity, mobility and data management issues need to be handled in a sophisticated manner. The scope of the special issue mainly focus on IoT and similar applications such as Intelligent transportation systems, Wireless Sensor Networks, Cloud/Edge of Things and any other applications that involve wireless and mobile connectivity. Please note that this special issue will focus mainly on the research works of the authors whom presented their papers in EAI BigIoT-EDU 2022.
Topics
Topics of interest include, but are not limited to, the following scope:
- Data-driven mobility and network connectivity solutions for IoT applications
- Middleware solutions for big data analytics in IoT-connected mobile and static applications
- Machine Learning/deep learning approaches for big data analytics in IoT
- Machine learning/deep learning approaches for real-time data analytics of IoT data
- Data-driven mobility and network connectivity solutions for IoT applications
- Big data analytics/machine learning for IoT security
- Big data analytics/machine learning for smart and intelligent edge/fog applications
- Intelligent and agent-based algorithms for Edge of Things computing
- Big Data analytics in mitigating disasters, accidents, environmental pollution, etc. in IoT applications
- Trust, Privacy and Security of machine learning techniques in IoT applications
- Machine learning/deep learning techniques for data fusion in IoT applications
Important Dates:
- Manuscript submission deadline: 15 April 2023
- Notification of acceptance: 30 May 2023
- Submission of final revised paper: 30 June 2023
- Publication of special issue (tentative): 31 July 2023
Submission Procedure
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/ (this opens in a new tab).
A copy of the manuscript should also be emailed to the Guest Editors at the following email addresses:
zhangyinjun@gxstnu.edu.cn (this opens in a new tab) and 05064@hcnu.edu.cn (this opens in a new tab)
Guest Editors:
Prof. Yinjun Zhang (Lead Guest editor)
School of Computer Science and Engineering
Guangxi Science & Technology Normal University, China
Email: zhangyinjun@gxstnu.edu.cn (this opens in a new tab)
Associate Prof. Mengji Chen
School of Electrical Engineering and IT
Hechi University, China