Main supervisor: Jia Hu (University of Exeter)

Project Description:
Recent years have witnessed the rapid proliferation of mobile devices such as smart phones and wearable devices, which are equipped with various built-in sensors and possess powerful computing and communications capabilities. The large number and diverse advanced functionalities of mobile devices empower ordinary citizens to contribute heterogeneous and cross-space data including machine-sensed data from mobile devices (physical space) and user-generated data from mobile social networking (cyber space), which are aggregated and fused in the cloud for knowledge discovery and service delivery. The explosive growing of data traffic in mobile networks poses imminent challenges while opening up new opportunities to all the aspects of the wireless and mobile system design, such as bandwidth efficiency, computing performance and network capacity. In this project, we will investigate two emerging open problems facing networking system and protocol design: 1) How to design a scalable mobile network architecture for efficient handling and robust delivery of ever-growing data traffic? 2) How to effectively learn from cross-space data to improve the Quality-of-Service (QoS) performance of mobile networking system? To answer these problems, this project will propose novel data-driven networking architectures and solutions for efficient QoS provisioning over wireless mobile networks.

This project aims to design and develop novel data-driven networking architectures and solutions for efficient QoS provisioning over wireless mobile networks. In outline, the proposed project intends to:

* Design a Software-Defined Networking (SDN) based network resource management framework where the packet routing and content caching decisions are jointly optimized.

* Develop an innovative learning-based distributed Information-Centric Networking (ICN) caching scheme to leverage the insights on the characteristics and popularity of contents, the preference and QoS demand of users, and the traffic and link conditions.

* Develop and validate a comprehensive mathematical model for evaluating the performance of the proposed resource management and caching techniques with multimedia applications and heterogeneous network conditions.

The project seeks to harness the complementary features of SDN and ICN to fully unlock their potential capabilities and remedy the intrinsic deficiencies of each other. To improve the network QoS performance, an innovative learning based distributed ICN caching strategy will be proposed to leverage the insights on the characteristics and popularity of contents, the preference and QoS demand, and the traffic and link conditions. In order to investigate the QoS of the proposed schemes, a mathematical model will be developed and validated by computer simulation/emulation.