Advanced Information Networking and Applications

Networks of today are going through a rapid evolution and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations are emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low-cost and high-volume applications. Several of such applications have been difficult to realize because of many interconnection problems. To fulfill their large range of applications different kinds of networks need to collaborate and wired and next generation wireless systems should be integrated in order to develop high performance computing solutions to problems arising from the complexities of these networks. This volume covers the theory, design and applications of computer networks, distributed computing and information systems.
The aim of the volume Advanced Information Networking and Applications is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications.

Presents recent research in Information Networking and Applications Gathers the proceedings of the 38th International Conference on Advanced Information Networking and Applications Written by experts in the field

Inhalt

Economic Method for Sensing Data Offloading in the Metaverse.- Securing Maritime Critical Infrastructure: A Multi-Domain AODV-based Communication Protocol for UxVs.- End-to-End MACsec Encryption in a Two-Data Centre Network over Routed WAN.- A New Broadcasting Scheme Based on Reinforcement Learning-Driven Adaptive Fuzzy Logic in Mobile Ad Hoc Networks.- Mobile Traffic Prediction Method Based on Federated Proximal Algorithm.- Reinforcement Learning Based Trajectory Optimization for MEC-UAV.- Deep Reinforcement Learning Based Resource Allocation in Ultra Reliable 5G Core Networks.