Level: BA, MA, MAP 
Responsible person: Dr. Mark C. Ballandies
Keywords: Machin learning, Data Mining, Network Analysis 

Decentralized physical infrastructure networks (DePINs) represent an emerging sector within the web3 ecosystem [1]. Utilizing a community-driven approach to develop global infrastructures, DePINs aim to provide services such as mobile network coverage, enhanced weather forecasting, and improved GPS positioning. These networks promise to drive real-world adoption of blockchain technology, upholding core values like decentralization, permissionlessness, and transparency. 

However, DePINs often resemble centralized systems, replicating the hierarchical structures of the past rather than embodying truly decentralized models capable of adapting to complex environments. This thesis aims to assess the levels of decentralization, permissionlessness, and transparency within DePIN systems. 

Depending on the thesis's scope, you will develop methods to quantitatively measure these dimensions in DePINs, integrate them into a taxonomy of DePIN systems, classify these systems, and provide insights for constructing successful DePIN networks by applying machine learning algorithms to analyze the classified data. 

[1] Ballandies, M.C., Wang, H., Law, A.C.C., Yang, J.C., Gösken, C. and Andrew, M., 2023, October. A Taxonomy for Blockchain-based Decentralized Physical Infrastructure Networks (DePIN). In 2023 IEEE 9th World Forum on Internet of Things (WF-IoT) (pp. 1-6). IEEE.