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MPhil: Predictive Maintenance for Large Vehicles Fleets using Edge Computing Techniques (KESS2 Scholarship, eligibility criteria apply)


Cardiff School of Computer Science & Informatics

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Dr C Perera , Prof O F Rana No more applications being accepted Funded PhD Project (UK Students Only)

About the Project

Knowledge Economy Skills Scholarship 2 East (KESS2 East)
See https://www.cardiff.ac.uk/study/postgraduate/funding/doctoral-training-initiatives/knowledge-economy-skills-scholarships-kess2

Context: This 1 year MPhil project focuses on developing a novel distributed predictive analytics technique that can efficiently be used in edge computing scenarios. Our partner iPoint is a company that develop sensor data based intelligent services (e.g., predictive maintenance), especially for large vehicle fleets such as trucks and busses. They provide a range of insights and recommendation to efficiently manage vehicles (e.g. evaluate driver behaviours and measure the quality of driving). Currently, all the sensor data collected by each vehicle and related accessories (e.g. sensor data generated by onboard sensors connected to the CAN bus) are directly sent to the cloud. All the required processing happens within the cloud, and relevant commands are sent back to each vehicle. This approach is inefficient from many aspects and could also impact the quality of service and customer satisfaction in certain scenarios.

Problem:
• Sending data to the cloud is costly (i.e., mobile data plans could cost significantly when it is required to send large volumes of data to the cloud).
• Sensing data to the cloud and received the commands from the cloud (i.e.,g decision-making loop) has high latency (i.e., It takes time to send data to the cloud and receive commands back). Given that we are dealing with moving vehicles, even small latency could lead to negative incidents.
• Mobile network connections are unreliable; therefore, any service depends on mobile network connectivity could be unreliable.
• Depending on external connectivity also open up higher security risks (e.g., more vulnerable to cyber-attacks)
• Typically, network communication consumers more energy than data processing/analysis (locally)

All of the above problems can be fully or partially mitigated by developing and adopting edge computing methods.

Research Objective: In order to address the challenges face by iPoint, we aim to develop a novel data processing architecture that is capable of moving analytics across different nodes (within the architecture). This means that iPoint will no longer be required to send all the sensor data to the cloud all the time. The onboard computer will conduct most of the data analytics local and will only send the summarised/aggregated data to the cloud. However, our proposed algorithms will consider context information when deciding where the data analysis should happen.

Develop novel algorithms (ranging from predictive militances to driving behaviour analysis):
• Design and development of distributed algorithms that can dynamically orchestrate IoT resources on edge to satisfy a given sensing requirement without continuous connectivity to the cloud.
• These algorithms will also determine how to distribute data analytics workloads (among heterogeneous edge nodes) in an optimal way to satisfy given requirements and real-world constraints.

Develop novel data processing architecture:
• Design and develop self-organizing and reconfigurable IoT infrastructure that integrates resources from multiple layers (sensing, edge/fog, cloud).

START DATE: 1 October 2020

ONLINE APPLICATIONS by 1 September 2020

HOW TO APPLY:

Applicants should apply to the Master of Philosophy in Computer Science and Informatics with a start date of October 2020.

In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select ’I will be applying for a scholarship/grant’ and specify that you are applying for advertised funding from KESS2 MPhil: Predictive Maintenance for Large Vehicles Fleets using Edge Computing Techniques.

Funding Notes

KESS2 Scholarship
UK tuition fees, stipend (£14,483 p.a. in first year - subject to confirmation), plus travel/conferences, support, consumables/equipment.

ELIGIBILITY - applicants must:
• have a home or work address in East Wales region (local authority areas Cardiff, Flintshire, Monmouthshire, Newport, Powys, Vale of Glamorgan and Wrexham) at application and enrolment;
• have the right to live and work in the UK for the duration of the scholarship, and the right to take up paid work in the East Wales region on completion of the scholarship;
• be classified as a ‘home’ or ‘EU’ student;
• satisfy the admissions criteria.

References

KESS2 Scholarship
See https://www.cardiff.ac.uk/study/postgraduate/research/doctoral-training-initiatives/knowledge-economy-skills-scholarships-kess2

ACADEMIC CRITERIA: 2:1 Honours undergraduate or a master's degree, in computing or related subject.


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