Data-centric Blockchain System Management

Abstract

This dissertation explores the integration of blockchain technology with data-centric systems to enhance decentralized energy trading and data management. The research addresses the scalability, security, and transparency challenges inherent in centralized infrastructures by leveraging blockchain's decentralized and immutable ledger properties.

The primary objectives are to develop a robust framework for blockchain-based energy trade management among prosumers and to create a decentralized storage system for efficient data management. The first part of the study proposes a single-agent model using a Q-learning algorithm to mediate energy trades, ensuring secure and transparent transactions. The second part introduces a cloud-native, blockchain-supported decentralized storage solution to overcome performance and scalability limitations of traditional systems.

Key contributions include the design of an energy trade management system that reduces maintenance costs through community energy storage (CES) and an innovative decentralized storage architecture that combines blockchain security with cloud-native efficiency. Significant outcomes demonstrate the enhanced reliability, scalability, and security of the proposed systems, offering a viable alternative to centralized data management approaches.

By addressing these objectives, this research provides valuable insights and practical solutions for the implementation of blockchain in decentralized energy and data management, ultimately contributing to the advancement of scalable and secure systems in the digital age.