Postgraduate Research Opportunity

Flexible networking and computing architecture for health applications

Details

Title: SCH15: Flexible networking and computing architecture for health applications

Funding: Full time Home/EU fees and a stipend of £15,009 p.a. (terms & conditions)

Application deadline: 7 June 2019

Start date: October 2019

Duration: 3 years (full time)

Location: Colchester Campus

Based in: School of Computer Science and Electronic Engineering (in collaboration with the School of Sport, Rehabilitation and Exercise Sciences)

Overview

This three-year faculty funded collaborative project brings together experts in Computing Systems, Communications and Sports & Rehabilitation.

The Internet of Things (IoT) applications are expected to connect us and our environments in unprecedented ways. Flexible offering of network services at the edge and deep in the Cloud is a corner stone in delivering this vision, particularly for sports and health applications. Requirements for: efficient data collection from multiple wireless sensors, intelligent data processing to provide cognitive services, power conservation of wearable devices, mobility and security renders the design of suitable Edge/Cloud fabrics a complex issue.

This necessitates undertaking rigorous investigation into Edge/Cloud architectures in order to design and develop novel solutions, suitable for interconnecting wearable sensors used primarily for sport and health applications.

Increased development of wearable sensors for physiological monitoring has spurred complementary interest in the detection of health and performance indicators. Wireless sensors such as near-infrared spectroscopy (NIRS) devices (PortaMon, MOXY Monitor), portable gas analysers (Cosmed K5), heart rate monitors and global positioning systems offer the capability to monitor and guide individual exercise prescription. Most devices operate as independent sensors and therefore networking multiple data outputs from various sources may provide a more holistic view of physiological load and facilitate intelligent data processing to enable cognitive exercise guidance.

Data security and network reliability are critical concerns of sports and health applications. For instance, ‘real-time’ data should be reported, processed and communicated accurately and timely to allow for accurate profiling of individuals health and performance. Manipulating this data could have severe consequences on the individual.

However, the exposed nature of wearable sensors - reporting the data - and the wireless body network that connects them makes for an easy target of data or network attacks. Added to that, portable devices are often constrained in resources and hence may not be able to apply typical, process-heavy, security mechanisms to protect against data manipulation or to mitigate an attack against the network. Enabling accurate processing and communication of this data requires investigating novel, lightweight, solutions to data authentication and access authorisation.

The project

This funded PhD studentship will address the design and development of a novel, flexible and intelligent Edge/Cloud networking solutions for smart sport and health applications.

The study would have three key phases:

Phase 1 - will focus on the design and development of novel Edge/Cloud fabrics, suitable for interconnecting wireless sensors in sports and health applications. This will include efficient and context-aware collection of ‘real-time’ data from multiple sensors of an individual, as well as from a group of people. Orthogonal to data collection, will be developing tailored process offloading solutions that alternate between the Edge and the Cloud, depending on process requirements and resource constraints of both portable devices and Edge nodes.

Performance evaluation of the system in a smart exercising scenario will considered through analytical analysis and Proof-of-Concept experimentation over the future network testbed, hosted in the Network Convergence Laboratory (NCL).

Phase 2 – Performance profiling of individuals during incremental exercise tasks and developing cognitive networking capability to effectively process, interpret and inform exercise intensity and physiological response. The accuracy, detail-richness, comprehensiveness and responsiveness of the profiling solutions will be compared against traditional individual data collection and profiling mechanisms, currently applied in sports and rehabilitation exercising programs, to assess the benefits of the system and identify further optimisation aspects.

Phase 3 – Securing data as well as network resources both at the Edge and in the Cloud. This will require undertaking an investigation to identify vulnerabilities and security threats when exchanging data between constrained portable and edge devices. Lightweight mechanisms will be designed to authenticate the generated data and secure the data exchange at the Edge; while fully-fledged solutions will be designed to secure the data in the Cloud.

The viability and benefits of these mechanisms will be analysed with respect to  data protection during competitive sports events, involving group exercising. Furthermore, the solutions will be evaluated with analytical models that consider link characteristics for communication and resource constraints for processing.

Funding

The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

Supervisors

 Christopher McManus

Co-supervisor

School of Sport, Rehabilitation and Exercise Sciences, University of Essex

Christopher McManus is an accredited sport and exercise scientist, lecturer and researcher at the School of Sport, Rehabilitation and Exercise Sciences (SRES). He is passionate about improving performance and health, with a particular interest in anthropometry, sports nutrition and optimising training and recovery.

Dr Masood Ur Rehman

Co-supervisor

School of Computer Science and Electronic Engineering, University of Essex

Dr. Masood Ur Rehman is a Lecturer (Assistant Professor) in the School of Computer Science and Electronic Engineering. His research interests include compact antenna design, radiowave propagation and channel characterization, satellite navigation system antennas in cluttered environment, electromagnetic wave interaction with human body, body-centric wireless networks and sensors, remote health care technology, mmWave and nano communications for body-centric networks and D2D/H2H communications. Although Masood will be leaving Essex before this studentship begins, he will be acting as an external supervisor for this project.

Criteria

Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Computer Science, Electronic Engineering, or Mathematics.

Preference will be given to the candidates who have expertise in one or more of the following areas:

  • Edge/Cloud computing
  • Internet of Things (IoT)
  • Future network
  • Software Defined Networks (SDN)
  • Wireless communications
  • Cyber security
  • Machine learning
  • Information theory.

How to apply

You can apply for this postgraduate research opportunity online.

Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.

The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

  • For "Course title" please put "PhD Computing and Electronic Systems"
  • For "Proposed research topic or area of research" please put the title of this studentship (SCH15: Flexible networking and computing architecture for health applications)
  • For "If you have contacted a potential supervisor..." please put the name of the lead supervisor (Dr Mays Al-Naday)

If you have any informal queries about this opportunity please email the lead supervisor, Dr Mays Al-Naday (mfhaln@essex.ac.uk)

You can find the terms and conditions of this studentship here (PDF).