Mobility and cloud management in wireless heterogeneous 5g networks

Abstract

The network mobility management branch is responsible for the protocols and actions taken by the network to ensure connectivity and the continuity of services consumed by mobile users. In this dissertation we analyse how next-generation networks pave the way for the distribution of video in vehicular networks (VANETs), composed by an heterogeneous ultra-dense infrastructure, joining existing wireless communication technologies to obtain greater spectral efficiency. A handover algorithm called HoVe is presented. Based on various criteria for video distribution on ultra-dense 5G VANETs. The simulation results show HoVe’s efficiency in providing videos with 19% higher quality than state-of-the-art algorithms, improving the package delivery rate by at least 30%. This work studies a particular case of VANETs that benefits from computing at the edge of the network, the case of Connected Autonomous Vehicles, or CAVs. Edge and mist computing are emerging solutions for remote data processing for autonomous vehicles, offering greater computational power, as well as the low latency required by autonomous driving. This work proposes the MOSAIC algorithm for service migration and resource management for communication between layers and between layers in edge and fog computing. Simulation results show the efficiency of the proposed algorithm with a better performance of up to 50% in terms of latency and five times less migration failures.