Optimization of software-configurable flying access networks

Abstract

The considered technologies for using UAVs in communication networks open up new prospects for providing communication in conditions where traditional methods may be less effective or unavailable. The research on the model of joint traffic of the Internet of Things (IoT), Tactile Internet (TI) and Augmented Reality (AR) leads to an improvement in the quality of service and network management in the context of the growing proliferation of these types of traffic. It has been determined that the probability of packet loss for AR traffic is higher than for IoT traffic and lower than for TI traffic, which can be important for network design and the development of error correction mechanisms. The proposed model allows us to evaluate the quality of service for different types of traffic, including delivery delay and packet loss probability, which is important for ensuring specified service levels and network planning. The obtained results of the study contribute to the understanding and optimization of networks taking into account different types of traffic and allow to improve the efficiency of network systems in the face of increasing complexity and diversity of user requirements. The analysis of the possibilities and efficiency of joint use of software-configurable networks, edge computing, and UAV technologies indicates the possibility of integrating different technologies to optimize networks. The developed network model, in which software-configurable networks are fully implemented on UAVs, is innovative and can open up new opportunities for the development of communication networks, especially in inaccessible places and emergencies, and also contributes to the development of network technologies and identifies important areas of research to improve the efficiency and quality of service in modern communication networks.

The results of the study indicate a significant contribution to the development of methods for clustering and optimizing networks using UAVs. The use of the k-means method for clustering UAVs indicates an effective way to group and manage these devices to optimize network resources. The k-means-based clustering algorithm allows you to find the optimal coordinates of the controllers, opening up the possibility of effectively organizing groups of UAVs for better network management. The method of traffic offloading, which includes the possibility of direct transmission of information to UAVs or through repeaters, indicates the flexibility and adaptability of the data transmission system. The use of a dynamic programming algorithm to determine the size of UAV groups and the delay for offloading traffic emphasizes the importance of optimizing resources and quality of service. Thus, the results open the way for further research and development of innovative network management systems using UAVs that can be applied in various fields, including telecommunications, emergency situations, and other areas