Aims and scope

Mobile Networks and Applications' technical scope covers mobility solutions that provide communication technologies and mobile services, which enables users to access resources and share information freely, anytime anywhere. The emerging symbiosis of wireless communication, the ever more powerful mobile devices, with the back-end resources of the cloud, making the user fully location independent.

The journal addresses the convergence of mobility, computing and information organization, services and management. In approving Special Issues, the Journal places an equal emphasis on the various areas of nomadic computing, data management, related software and hardware technologies, and mobile user services, alongside more `classical' topics in wireless and mobile networking. The journal documents practical and theoretical results which make a fundamental contribution.

Special Issue on Cloud-Edge Intelligence Collaborative Computing: Software, Communication and Human
提出日: 2024-12-25

With the development of 5G communications and the Internet of Things, IoT devices are becoming the primary smart devices for billions of users worldwide. It will result in amount of data generated by billions of sensors and devices. It is of great significance to mine the characteristics and values of massive data efficiently and safely, for the practical industrial scenarios, such as finance, medicine, natural language processing, etc. Distributed machine learning technology has become a key method to efficiently process and explore the value of massive data, as it can make use of the computing capacity of multiple computing devices and has the self-learning, pre-training and fast reasoning abilities. Recent advancement in computational power at the cloud server and edge devices, have made it possible to execute machine learning models by the cooperation of cloud server and edge devices. However, the research of distributed machine learning on Cloud-Edge computing is still in its early stages and there are still many problems to be solved. For instance, how to manage and schedule cloud-edge heterogeneous resources? How to ensure data security and efficient transmission with limited network? How to conduct distributed high-performance training and inference? Therefore, distributed training and inference system or framework, data security, network transmission, resource management and scheduling, and algorithms for AI in cloud-edge computing should be researched in depth, and needs a special communication for the recent advances of the next generation distributed machine learning system. The focus for this special issue is on advances in Cloud-Edge Intelligence Collaborative Computing. Researchers from academic fields and industries worldwide are encouraged to submit high quality unpublished original research articles as well as review articles in broad areas relevant to theories, technologies, and emerging applications.