Learning-Based Networking and Caching Technology for Mobile Crowd Sensing - Computer Science - EPSRC DTP funded PhD Studentship Ref: 2923

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Dr Jia Hu

Dr Geyong Min

Streatham Campus, 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 are aggregated and fused in the cloud of Cyber-Physical- System (CPS) for knowledge discovery and service. The explosive growing of data traffic in CPS 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 CPS architecture for efficient handling and robust delivery of ever-growing crowd-sensing data? 2) How to effectively learn from cross-space data to improve the Quality-of-Service (QoS) performance of heterogeneous mobile networking system? To answer these problems, this project will propose novel data-driven networking architectures and protocols for delivering the cross-space data from distributed participants to the backend CPS server over heterogeneous mobile networks with low bandwidth, high mobility, and energy-constrained devices.

In outline, the proposed project intends to

  • Design an adaptive network resource management framework where the packet routing and content caching decisions are jointly optimized.
  • Develop a learning-based proactive caching scheme to improve the caching efficiency by learning 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 analytical 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 information-centric networking and network function virtualisation to fully unlock their potential capabilities and remedy the intrinsic deficiencies of each other. To improve the network performance, a machine learning based proactive caching strategy will be proposed to use 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, an analytical model will be developed using Stochastic process/Markov Chain etc., and validated by computer simulation/emulation with synthetic/real-world data. The successful candidate should have a Bachelor/Master degree in Computer Science/Mathematics/Electrical Engineering. Strong programming and Maths skills are expected. The knowledge in computer networking/data analytics is desirable. 

Entry Requirements

You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in [subjects accepted]. Experience in [research areas] is desirable.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.


Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral Collegepgrenquiries@exeter.ac.uk

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email: pgrenquiries@exeter.ac.uk.
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.