Blockchain-based Middleware for Distributed Sensor Environments
University of Cyprus (UCY), Cyprus
Prof. Marios Dikaiakos / Dr. George Pallis
Blockchain for IoS, Consensus Algorithms, Data economy
Ioannis Savvidis obtained his diploma in Informatics and Telecommunications Engineering from the University of Western Macedonia in Kozani, Greece at 2018. During his studies there, he mainly focused on Software Engineering undertaking projects related to Game Development, Internet of Things, Android development, Web development and more.
After he finished his undergraduate studies Ioannis started his MSc in Advanced Computer Science and IT Management at The University of Manchester in the UK. During the first half of his MSc, he gained important knowledge in Software Engineering. In the second half, he was dedicated in his dissertation on blockchain, in which he developed a marketplace on Ethereum blockchain.
Currently, Ioannis is an early stage researcher under the Marie Curie Initial Training Network (ITN) program RAIS at the Laboratory for Internet Computing. He also continues his studies doing his PhD at the University of Cyprus. In his new position, Ioannis is occupied with the development of a Blockchain-based Middleware for Distributed Sensor Environments.
Blockchain, IoT, Software Engineering, Distributed Systems
Currently, we are working on a project that will enable data monetization. The project has two aspects. On the one hand, the goal is to develop a framework which allows the data providers to create value from their data. On the other hand, it allows data consumers to use the data for operations such as model training or aggregation. For this purpose, we make use of blockchain technology and cryptographic techniques. The blockchain can inherently provide immutability, security and availability while cryptography will provide a greater level of privacy. Also, the framework will be focused on the fair sharing of the profit among the data providers. The contribution of each provider is not strictly related to the volume of the data but its quality too. On this context, we explore the use of models derived from the game theory such as Shapley value, for the calculation of the contribution of each provider. Thus, the recompense of each provider will be proportional to her contribution to the final result.