Xiao Chen

High performance data-driven trusted financial systems for smart mobile users

5G technologies enable large-scale and distributed smart mobile devices to join in the network and enables fast growth of new smart mobile services such as decentralised financial applications. Since blockchain technologies have emerged, their success can be attributed to both the research community and industry. Initially, blockchain was confined to the financial sector, but its decentralised and immutable ledger availability has made it popular for non-financial services. The ever-increasing size of blockchains, like Bitcoin and Ethereum, has led to issues of scalability. Thus, high-scalable and lightweight consensus becomes a crucial demand for extending blockchain technologies to more industry applications, particularly in a mobile environment. This project will explore the synergies of academic expertise within financial blockchain applications and data innovations with commercial potential.

The project aims to develop an extra-fast and high-scalable consensus algorithm based on a multiple and hierarchical committee framework. The scalable consensus algorithm allows consensus running parallelly in a group of committees, which ensures the high throughput and scalability with peers’ growth. Moreover, the consensus algorithm is designed with a reinforcement learning-driven optimisation scheme that can support the efficient portioning of committees and allocation of peers. The optimisation scheme aims to improve the system consensus performance by minimising committees and peers’ failure while maximising the consensus throughput. Second, the project also explores smart mobile technologies, and client-facing trusted systems with large-scale mobile users. The research team will explore integrated data-driven design to optimise the next-generation mobile services for banks, trading and insurance firms with trustworthy transaction technologies. The research will also adopt an inter-disciplinary research methodology combining distributed/parallel trust algorithm design, data-driven mathematical optimisation, modelling smart mobile users and easy to use financial transaction applications to address the research objectives.

The project aims to provide research-led disruptive innovation that would enable customised distributed fintech infrastructure provision and lead to better-designed fintech solutions. It is also expected to efficiently and effectively achieve significant economic impacts for the finance industry through mobile applications.

This TRAIN@Ed project has received funding from the DDI programme, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801215, and industry funding from Polydigi.

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Xiao Chen

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