Abstract
It has been envisaged that in future 5G networks user devices will become an integral part of the network by participating in the transmission of mobile content traffic typically through Device-to-device (D2D) technologies. In this context, we promote the concept of Mobility as a Service (MaaS), where the mobile network edge is equipped with necessary knowledge on device mobility in order to meet specific service requirements for clients via a small number of helper devices. In this thesis, we propose a MaaS paradigm based frameworks to address clients’ requirement with regards to content offloading service and connectivity relaying service via network assisted D2D communication framework. To address content traffic offloading, we present a device-level Information Centric Networking (ICN) architecture that is able to perform intelligent content distribution operations according to necessary context information on mobile user mobility and content characteristics. Based on such an architecture, we further introduce device-level online content caching and offline helper selection algorithms in order to optimise the overall system efficiency. In particular, this piece of work sheds distinct light on the importance of user mobility data analytics based on which helper selection can lead to overall system optimality. Based on representative user mobility models, we conducted realistic simulation experiments and modelling which have proven the efficiency in terms of both network traffic offloading gains and user-oriented performance improvements. In addition, we show how the framework can be flexibly configured to meet specific delay tolerance constraints according to specific context policies. With regard to connectivity relaying service, we introduce a novel scheme of using D2D communications for enabling data relay services in partial Not-Spots, where a client without local network access may require data relay by other devices. Depending on specific social application scenarios, this piece of work introduces tailored algorithms in order to achieve optimised data relay service performance. The approach is to exploit the network’s knowledge on its local user mobility patterns to identify best helper devices for participating in data relay operations. This framework is also supported with our proposed helper selection optimisation algorithm based on prediction of individual user mobility. According to our simulation analysis, based on both theoretical mobility models and real human mobility data traces, the proposed scheme is able to flexibly support different service requirements in specific social application scenarios.