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Design and implementation of AI-enabled Secure Social Industrial Internet of Things (ASIIoT) platform: Leveraging the Industry 4.0


   Centre for Digital Innovation

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  Dr Zia Ush Shamszaman  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

We are experiencing a cabbalistic transformation in manufacturing products because of the digitization of the process of manufacturing products in the industries and factories. The key technologies in this transformation are, Cyber Physical Systems, the Internet of Things (IoT) and Information Systems. The transformation is so significant and inevitable that it is being considered as the 4th industrial revolution called Industry 4.0.

Social IoT (SIoT) is a recent amendment in the IoT paradigm where human-centric online social network principles are implied into IoT to enable IoT object as a new social element. SIoT work as a common platform for the data coming from IoT devices and social network services to facilitate omnidirectional interactions (human to human, human to object and object to object) among human and objects for creating new services in the future intelligent smart spaces. Industrial IoT (IIoT) has been noticed seriously and largely by the scientific community as well as by the industry because of its tremendous possibilities to leverage automaticity and intelligence mainly in the production, supply chain and manufacturing industries, i.e. leveraging the 4th industrial revolution. The progressing trends towards IIoT from the predominant IoT, the necessity of CPS, the vision of utilizing SIoT in the smart industries and factories, the immense possibility of AI to incorporate intelligence in the physical objects; and the scalability issues of existing security mechanisms on huge IIoT resources demand an approach that addresses the above-mentioned issues and moves forward with the vision and possibilities towards the Industry 4.0. Hence, this research proposes an AI-enabled Secure Social Industrial Internet of Things (ASIIoT that includes the utilization of resources i.e. machines, parts, devices, processes, human and physical objects intelligently.

However, the process of this transformation is not trivial and requires significant effort from the research community to overcome the existing challenges mainly because of the complexity, heterogeneity, time-sensitivity, and interoperability issues of resource-constrained connected objects. 

The aim of this research is to overcome several existing challenges to leverage Industry 4.0 through exploring the social relation of connected physical objects of IIoT and CPS, ensuring security by developing a novel Intrusion Prevention System (IPS) for resource-constrained connected objects and incorporating intelligence into the physical objects and processes through creating the virtual counterpart of the relevant physical objects including human on the cyberspace.

The primary objective of this research is to develop ASIIoT platform. The idea of social relationships among resources will ensure the collective participation of resources to accomplish a task together to enhance the performance of the factories and industries. The proposed platform shall include intelligence, real-time monitoring, real-time and historical analysis, control of machines, and control of manufacturing systems and virtualising physical objects to cyberspace. All the assets will be associated with socially connected actuators and sensors. The architecture will be designed from the semantic ontological perspective and an ontology model will be provided to make the relationship meaningful and reusable to the digital world. A novel IPS will be developed specifically for resource constrained objects and lightweight cryptography will be used to ensure confidentiality, integrity, non-repudiation and authenticity of data as well as of communication.

A sample scenario would be common machinery elements in the factories e.g. motor, bearing, axle, bracket, hanger, etc. perform various tasks. Regardless of their size and price, they are so important for the production systems that even a faulty tiny bearing may cause an entire system to halt which is unacceptable and sometimes unaffordable in terms of money and time. This proposed platform will enable prediction mechanisms that predict the upcoming failure and trigger the sourcing process beforehand for a replacement.

Entry Requirements

Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline. A masters level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area. Further details of the expected background may appear in the specific project details. International students will be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements and procedures.

How to Apply

Applicants should apply online for this opportunity at: https://e-vision.tees.ac.uk/si_prod/userdocs/web/apply.html?CourseID=1191

Please use the Online Application (Funded PHD) application form. When asked to specify funding select “other” and enter ‘RDS’ and the title of the PhD project that you are applying for. You should ensure that you clearly indicate that you are applying for a Funded Studentship and the title of the topic or project on the proposal that you will need to upload when applying. If you would like to apply for more than one project, you will need to complete a further application form and specify the relevant title for each application to a topic or project.

Applications for studentships that do not clearly indicate that the application is for a Funded Studentship and state the title of the project applied for on the proposal may mean that your application may not be considered for the appropriate funding.

For academic enquiries, please contact Dr. Zia Ush Shamszaman [Email Address Removed].

For administrative enquiries before or when making your application, contact [Email Address Removed].  


Funding Notes

The Fees-Paid PhD studentship will cover all tuition fees for the period of a full-time PhD Registration of up to four years. Successful applicants who are eligible will be able to access the UK Doctoral Loan scheme https://www.gov.uk/doctoral-loan to support with living costs. The Fully Funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applicants who are employed and their employer is interested in funding a PhD, can apply for a Collaborative Studentship
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