Abstract
The centralised nature of the current Internet i.e., Web 2.0, brings data privacy and security issues to the fore as critical barriers to the realisation of the digital economy. Due to such issues, it is difficult for data-driven services such as ‘ML-as-a-service’ to prosper under the umbrella of Web 2.0. Therefore, it is important to explore the platform utilities Web 3.0 can provide to support such services as they require to be executed and served in a highly distributed manner. This paper envisages an ML model marketplace for Industrial IoT applications exploiting next-generation IIoT components. A theoretical analysis of the ML economy and the technical components required to realize this marketplace are presented in this paper along with the specification of key open research questions.
This project has received funding from Sustainable Energy Authority of Ireland under the SEAI Research, Development & Demonstration Funding Programme 2021, Grant number 21/RDD/688 and Science Foundation Ireland SFI Research Centres 2017 PhD Awards Program under SFI CONNECT Centre 13/RC/2077 and SFI CONNECT Centre 13/RC/2077 P2.
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Bharti, S., Ranathunga, T., Dhanapala, I., Rea, S., McGibney, A. (2024). Machine Learning Economy for Next Generation Industrial IoT: A Vision Under Web 3.0. In: Chbeir, R., Benslimane, D., Zervakis, M., Manolopoulos, Y., Ngyuen, N.T., Tekli, J. (eds) Management of Digital EcoSystems. MEDES 2023. Communications in Computer and Information Science, vol 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-51643-6_8
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