Engineering and built environment PhD project opportunities

Find out more about self-funded PhD project opportunities in our School of Engineering and the Built Environment.

Climate change and the development of built asset adaptation strategies

Research Group

Future Cities Research Network

Global Sustainability Institute

Proposed supervisory team

Prof Keith Jones

Prof Aled Jones

Theme

Building Performance, Urban Resilience

Summary of the research project

Two recently completed research projects undertaken by Prof Keith Jones have questioned the ability of the current (forecasting) approach to built asset management to effectively accommodate adaptation to climate change as part of built asset management life-cycle. Keith has suggested an alternative approach based on back-casting as more appropriate to address the sustainability and climate change agendas. In essence, back-casting requires future needs (in this case of a building) to be established (20-25 years) and then an intervention journey to be planned, with short and medium term targets, that identify maintenance and refurbishment actions that are required to achieve the future position. This project will examine the above and develop and test a range of tools that facilities managers can use to develop built asset adaptation strategies. In particular the project will:

  • Examine the relationship between climate change projections and built asset impacts and develop models (physical, technical, economic, social) that can be used to predict future needs.
  • Develop building life-cycle scenarios/tools that allow intervention journeys to be planned.
  • Test the life-cycle scenarios/tools using interim targets and milestones for a range of built asset types.

The project will use an Action Research approach in which a small number of commercial partners will provide access to their (or their clients) buildings and employees to develop and test the new built asset adaptation strategy. The project will involve extensive fieldwork but will not require specialist equipment above that currently available within the Faculty.

Where you'll study

Chelmsford

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Contextual Awareness and Intelligent Data Mining with End-to-End Performance in 5G Networks

Research Group

Future Cities Research Network

Telecommunication Engineering Research Group (TERG)

Proposed supervisory team

Dr Sufian Yousef

Theme

Smart cities, 5G Networks

Summary of the research project

This topic goes in parallel with end-to-end performance and the ability for a network to know what device a user is using, what application is being used, the physical location and speed, and adapting the network performance to best serve those parameters. Work in that area is already ongoing. Big data analytics are already an area of interest for 5G, and researchers expect data-based intelligence to become more prevalent as part of contextual awareness. However, researchers acknowledged that as more and more content providers move to encrypted content, network providers have less visibility into those data streams. This is a very active area of research, and much can be inferred about the content of a data stream based on its behaviour. Many researchers expect to see content providers and network operators come around to sharing more information with one another, because both sides ultimately want an excellent end-user experience.

Data mining is considered to be one of the key enablers for the next generation of mobile networks. The building of knowledge models is expected to tackle the complexity of these networks and enable their dynamic management and operation.

Recently, this research area has attracted a lot of interest and several models have been proposed by the research community.

5G mobile networks target the provision of tailor-cut solutions not only for the telecommunications sector but also for the so called “vertical industries” (e.g., intelligent transportation systems, smart factories, the health sector, etc.). This result will be achieved by deploying multiple network slices over the same network infrastructure. Thus, 5G networks will be considerably more complex than the previous generations.

At the same time, the scientific community has identified that big data solutions can significantly improve the operation and management of existing and future mobile networks. Data mining is used to discover patterns and relationships between variables in large data sets. Several mechanisms that include statistical analysis, artificial intelligence and machine learning are applied in the data set to extract essentially knowledge from the examined data. data are collected from a number of network components. These data may include a variety of information fields such as the quality of the wireless channel, the network load, accounting information, configuration and fault indications, the profile of the subscribers, etc. These data are stored and updated regularly. When collected, they are passed through a pre-processing phase. During this phase transformation, discretization, normalization, outlier detection and dimensionality reduction is executed. The outcome of this phase is then passed to a data analysis phase where a model is built to extract knowledge from the processed data. For example, the result of this process will be the identification of situations where the occurrence of some specific events causes some specific result. The knowledge model may also include some solutions for specific situations (e.g., force the network components to place high moving users to macro cells). The list of the knowledge discovery results can then be communicated to either policy, management or control modules. These modules can use this information in order to optimize the operation of the network and improve the performance.

Where you'll study

Chelmsford

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Cost and efficiency focused optimisation of domestic waste water heat recovery application

Research Group

Future Cities Research Network

Engineering Analysis Simulation and Tribology Research Group

Proposed supervisory team

Dr Ahad Ramezanpour

Theme

Sustainable Technology and Manufacturing

Summary of the research project

The low-grade heat recovery systems, recover part of waste water heat and save energy consumption in the form of preheating a cold water input to shower or boiler. The heat recovery unit is a heat exchanger which could be horizontally or vertically designed. The efficiency of the units are functions of design and size envelope influencing convective heat transfer rate and surface area. The adverse effect of pressure drop and physical contaminants in the efficiency of the units in long term requires a holistic view on the design of waste water heat recovery units. On top of this, low energy recovery and high-cost result in a high pay-back period for these units of up to 40 years.

This research focuses on holistic research on technological consideration and innovation of the waste water heat recovery units with a view on pay-back period and commercialisation aspects.

An extensive literature review on the subject and proposed innovations in design to reduce cost while maintaining or increasing efficiency are expected followed by detailed analysis of the selected design both numerically, using computational fluid dynamics, and experimentally, designing, building and testing a test rig. The project enjoys support and contact from industrial companies with excellent track record in the field.

The expected outcome of the research is innovation in the form of intellectual property, scientific publications, and proposed prototype for commercialisation of the final design. Considering that long pay-back period and the high initial cost is the major issue for mass use of domestic waste water heat recovery systems, the project has major environmental impacts by making a product viable and recovering/saving energy on a large scale.

Where you'll study

Chelmsford

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Distributed Cloud, Software-Defined Networking and Network Function Virtualization Evolution toward 5G Architecture for Ultra-Low Latency Applications

Research Group

Future Cities Research Network

Telecommunication Engineering Research Group (TERG)

Proposed supervisory team

Dr Sufian Yousef

Theme

Smart cities, 5G Networks

Summary of the research project

As the industry explores more flexible, automated network solutions, this part of the evolution toward 5G capabilities is already underway and researchers expect it to be fundamental to 5G. However, researchers pointed out that research questions remain on the best network architectures for different applications. Ultra-low-latency applications such as autonomous driving may require highly distributed networks simply due the geographical distributions, while applications that can tolerate higher latency could be served from fewer central locations.

Traditional networks make use of network configuration, bearer and QoS information to satisfy user requests, but the Distributed Cloud (DC) architecture additionally employs user and network context information such as where, when, why who and what is being requested as well as the user’s location and location type to service requests. The network is also able to make use of learned intelligence gathered from these additional resources both at the device and in the network.

The DC network will provide communications connection using both fixed and wireless bearers where available and will enable interconnection with internet, cloud and new content distribution networks. 5G has a main requirement of highly flexible, ultra-low latency and ultra-high bandwidth virtualised infrastructure in order to deliver end-to-end services.

Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies are the key enablers to federate heterogeneous experimental facilities and to integrate both network and cloud resources to offer advanced end-to-end 5G services upon multi-domain heterogeneous networks and distributed centers (DC). SDN has emerged as the most promising candidate to improve network programmability and dynamic adjustment of the network resources. SDN is defined as a control framework that supports the programmability of network functions and protocols by decoupling the data plane and the control plane, which are currently integrated vertically in most network equipment.

SDN proposes a logically centralized architecture where the control entity (SDN controller) is responsible for providing an abstraction of network resources through Application Programming Interfaces (API). One of the main benefits of this architecture resides on the ability to perform control and management tasks of different wireless and wired network forwarding technologies (e.g., packet/flow switching or circuit switching) by means of the same network controller. The OpenFlow protocol is the most commonly deployed protocol for enabling SDN. It offers a logical switch abstraction, mapping high-level instructions of the protocol to hide vendor-specific hardware details, which mitigates inter-operability issues commonly found in multi-vendor deployments. This abstraction enables SDN to perform network virtualization, that is, to slice the physical infrastructure and create multiple co-existing network slices (virtual networks) independent of the underlying wireless or optical technology and network protocols. In a multi-tenant environment, these virtual networks can be independently controlled by their own instance of SDN control plane (e.g., virtual operators).

This project can be investigated by Matlab, NS3 Simulation and hardware setup.

Where you'll study

Chelmsford

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Exploitation of the shadowing-free very high Spectrum in the Millimeter waves (60 GHz or more) at longer distances in New Radio (NR) 5G

Research Group

Future Cities Research Network

Telecommunication Engineering Research Group (TERG)

Proposed supervisory team

Dr Sufian Yousef

Theme

Smart cities, 5G Networks

Summary of the research project

There is a substantial amount of spectrum at very high frequencies, which makes it very attractive, but the engineering challenges are intense. This spectrum offers “huge opportunities and huge challenges,” especially the big challenge of the spectrum’s vulnerability to shadowing.

If there is not a line-of-sight link between the access point and the user device, then the connection basically goes to zero - unless there is a reflection off a very flat wall nearby. Researchers and engineers will have to figure out how to leverage that spectrum while providing the consistent high quality of experience, adding that performance over distance will be important as well. Researchers expect systems that can harness such spectrum over a meter or two of distance from the base station will start to appear soon, such as WiGig, but it will be important to design systems that can utilize the high-frequency spectrum at a distance of, say, 100 metres.

There is a high possibility to allocate 24.75-27.5GHz, 66-71GHz and 81-86GHz to 5G in ITU. There is currently a fierce competition of 5G among telecom powers. 5G is urged to fulfil enhanced needs of high data rata, easy usage and low latency. There are three methods to enlarge the capacity of 5G: higher spectrum efficiency, denser coverage and more spectrum resource. The spectrum efficiency of 4G is already very high. Although it can be developed, it can’t meet the growing requirement of the service. More base stations are on the way, they can increase the capacity of the system, but they’re not enough to meet the requirements of wide band and electromagnetic compatibility. As a result, it is urgent to allocate more frequencies for 5G. Frequency resource is essential for 5G deployment. It needs much more frequencies for large scale deployment of 5G. There is little frequency resource that can be allocated to 5G below 6GHz. But there are plentiful frequencies in the millimeter wave band. Millimeter wave band starts from 30GHz, but under some conditions, the band above 10GHz is also called millimeter wave band. For a long time, the characteristic of millimeter wave band is regarded as not suitable for mobile service (MS) because of its high transmitting loss and limitable coverage. Atmospheric attenuation, rain attenuation, tree leaves attenuation, and building penetration loss, contributes to the transmitting loss. But multiple antennas and beam forming can spread the distance of coverage in the Line Of Sight (LOS) in millimeter wave band. Degrading diffraction compensating by strong reflection with beam forming can fulfil the connection out of sight. This means that the low band remains the main operating band in 5G, meanwhile, the millimeter wave band can supplement the coverage with high capacity and high data rate in dense outdoor and indoor scenarios.

Where you'll study

Chelmsford

Funding

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Global resilience assessment using artificial neural network

Research Group

Future Cities Research Network

Proposed supervisory team

Dr Maryam Imani

Theme

Built Environment, Urban Resilience

Summary of the research project

Evaluation of global resilience in large and complex networks of infrastructure (water and wastewater, transport and railway systems etc.) can be computationally a challenging task for resilience promotion in Smart Cities in the future. One of the main reasons is the substantial time required to perform each round of the system simulation and resilience evaluation. As a result, Smart Cities will require innovative strategies to cope with this computational challenge. This study will fill this gap by applying artificial neural network (ANN) to develop a surrogate model that will be used as an implicit global resilience evaluator to reduce the computational time while preserving the accuracy of evaluations.

The approach of this study will have the potential to be extensively used in efficient and effective management of large and complex networks in Smart Cities.

Some potential objectives of this project could be outlined as:

  1. To investigate and identify the potential failure states (FSs) of the network (regardless of their origin, all- hazards approach)
  2. To define and develop a global resilience measure for the network
  3. To develop the ANN-based surrogate model (inputs, outputs, functions….) using FSs and global resilience measure
  4. To test and evaluate the surrogate model’s efficiency using two benchmark case-studies (one in water system and one in transport system).

The project will involve computer scientists and engineers from different infrastructure systems. The project will be desktop-based and will require specialist software/tools above what is currently available within the Faculty of Science and Engineering. There are potential organisations interested to join and collaborate.

Where you'll study

Chelmsford

Funding

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Mitigation, adaptation, built asset management and climate change

Research Group

Future Cities Research Network

Proposed supervisory team

Prof Keith Jones

Dr Alan Coday

Theme

Smart Cities

Summary of the research project

Mitigation and adaptation to climate change is one of the key global challenges of our age. To date research has tended to focus on either future carbon reduction through the use of alternative fuel sources (mitigation) or changes to the socio-technical systems that support human living in light of inevitable climate change (adaptation). However, there is a growing school of thought amongst policy makers and researchers that real progress in tackling global warming and producing a sustainable built environment will only be achieved if mitigation and adaptation are tackled together in the context of the built asset management. This project will be the first to explore the challenges associated with integrating adaptation and mitigation solutions into the building life cycle. In particular the project will:

  1. Investigate the theoretical relationships between adaptation, mitigation and building performance from a technical, social, environmental and business perspective.
  2. Integrate these relationships into the building life cycle model to produce a new sustainable built asset management model for maintenance and refurbishment of existing buildings.
  3. Test the new model against a series of exemplar/case study buildings drawn from across the UK/Europe.
  4. Produce practical guidance for Architects, Engineers and Facilities Managers on integrating adaptation and mitigation into built asset management decision making.

The project will involve a wide range of stakeholders including: building owners/operators; built environment professionals; community groups; planners; infrastructure operators; landlords [social and private]; businesses; local government; national government etc. that will form the basis of a study group and will provide access to exemplar/case study buildings. The project will involve extensive desk work (objectives 1, 2 & 4); case study field work (objective 1) and participatory field work as part of a design/development team (object 3) but will not require specialist equipment above that currently available within the Faculty. Initial discussions have identified a number of UK social housing landlords who will provide data although other types of organisation will be sought to provide a wider evidence base.

Where you'll study

Chelmsford

Funding

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Remote collection and analysis of biomechanics data

Research Group

Future Cities Research Network

Biomechanics Lab (Faculty of Health, Education, Medicine and Social Care)

Proposed supervisory team

Dr Jennifer Martay

Dr Shabnam Sadeghi-Esfahlani

Mr Alireza Sanaei

Dr Stephen Hughes (HEMS)

Theme

Healthcare, Telemedicine, Quality of life

Summary of the research project

The lockdown due to Covid-19 has highlighted the importance of telemedicine: being able to collect and analyse data with the patent and doctor at different locations. The NHS will continue telemedicine after lockdown ends, particularly for regular monitoring of long-term patients, due to telemedicine being “convenient, accessible, and cost-effective” (1).

The ability to remotely collect and analyse data is also beneficial to research. For example, longitudinal studies (participants required to regularly come into the lab for repeated measurements) often had low participant retention levels. These retention levels can be improved by allowing participants to collect the data at home in their own time.

In this PhD, you will develop methods of using everyday devices to collect biomechanics data, which will then be sent for remote, automatic analysis (you will also create these analysis methods). Developing and validating these data collection and analysis methods will be major objectives of this PhD. A third major objective will be then applying your methods to conduct a longitudinal study into an aspect of healthcare.

(1) Technology Enabled Care Services. (n.d.). Available at https://www.england.nhs.uk/tecs/ [Accessed 15 March 2021].

Where you'll study

Chelmsford

Funding

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Resilience-based asset management in Integrated Urban Wastewater System (IUWS)

Research Group

Future Cities Research Network

Proposed supervisory team

Dr Maryam Imani

Theme

Built Environment, Urban Resilience

Summary of the research project

Asset management in wastewater industry (sewer system and wastewater treatment plant) will require innovative approaches to effectively respond to emerging challenges and uncertainties in future cities. To date, multi-objective risk-based approaches (including operational risk, economic risk, social and environmental risks) have been extensively used and tested for asset management in wastewater industry. These approaches are often leading to fail-safe and reliability-based solutions. However, with growing challenges (climate change, population growth, aged assets, financial crisis and so on), wastewater industry will require to incorporate resilience-based approaches into its asset management process.

This study aims to contribute to this transform by developing a resilience-based asset management model to support the wastewater industry in future cities.

Some potential objectives of this project could be outlined as:

  1. To investigate and identify the potential failure states (FSs) – or failure scenarios - of the assets in a wastewater system (regardless of their origins, all - hazards approach)
  2. To define and develop global resilience measure(s) for the wastewater system performance
  3. To integrate the above FSs and global resilience measure(s) to create a new resilience-based asset evaluation model
  4. To test and evaluate the new model using a benchmark case-study
  5. To determine the critical assets (condition assessment) using the new developed model
  6. To define asset management criteria/objectives
  7. To develop adaptation and mitigation strategies to manage the critical assets using the above criteria.

The project will involve a range of stakeholders including: water and wastewater industry (engineers, managers, operators, and regulators); urban planners, and county councils. The project will be desktop-based and will require specialist software/tools above what is currently available within the Faculty of Science and Engineering. There are potential organisations interested to join and collaborate.

Where you'll study

Chelmsford

Funding

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Smart City Data Security, Gateways Efficient Routing and IP Capacity

Research Group

Future Cities Research Network

Telecommunications Engineering Research Group

Proposed supervisory team

Dr Sufian Yousef

Theme

Smart Cities

Summary of the research project

The smart city is an alluring image of the future, where traffic lights, smart meters for utilities, public transport, smart street lights, traffic tracking sensors in roads, collected data on pollution and health, earthquake early warning and flood control in some cases are expected sources of data management and control through wired and wireless networks.

The software and hardware technologies are allowing exponentially rising numbers of smart devices to be connected to the Internet for the sake of smart city applications and services. The collected data from devices, sensors, smart units will be at different formats and quality requirements. This can be a source of hindrance for an effective use of the collected data. The scalability of the data collection and analysis methods that needs higher level of abstraction can be a challenge. Semantic Web technologies can provide reasoning about security, information and knowledge extraction and interoperability.

Smart Cities process large amounts of data streams which raise serious privacy and security concerns for everybody involved. Many attempts are needed to be made to ensure the security of the people’s data. There are issues that categorise data, for example, data reliability, data ownership and service provider trustworthiness. Some researchers have warned that smart cities could be more vulnerable to hacking than the smartphones of today.

The fact that most of the data will be transmitted wirelessly poses a real exposure to all sources of security attacks. This means that a very reliable and strong encryption method that can outperform the current encryption techniques is required. At our University in the Telecommunications Engineering Research Group a new encryption method that uses parallel computing has been designed and tested where the complexity is very high and the resulted delay in the encryption process is negligible. This method is named “Anglia 1”.

In this proposal, the research in advanced computing will consider the security of cloud computing by deployment of data offloading on a middleware that will be supporting a mobile cloudlet which is a medium between the core server and the mobile device. The aim is to ensure cloud computing end-to-end secure and privacy features for trustable data acquisition, transmission, processing and legitimate service provisioning.

This research on the privacy and security of data will be part of designing suitable smart network infrastructure that can have their own security protocols which are able to work harmoniously with the proposed confidentiality and authentication techniques. Gateways play good role in connecting devices to the network. However, an interfacing between gateways and IP has to be stable in routing and resource allocation management. This means that it is essential to convert the current IP protocol to hoist larger address spaces than the current addressing capacity of IPv6 in order to suit large number of connected sensors.

In a nutshell, this proposal is confined to developing a complexity encryption and authentication technique for wireless networks, mobile networks, ad hoc mobile networks and cloud networks by parallel computing encryption, gateways routing of large sensors’ data and the IPv6 address extensions. These proposed research items are related to each other.

Where you'll study

Chelmsford

Funding

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, we strongly advise you contact the proposed supervisory team. You will also need to formally apply for our Engineering and the Built Environment PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.