6G streaming advancement intensifies challenges in content security, copyright traceability, and privacy protection. This paper proposes a novel architecture integrating federated learning and blockchain. By enhancing protocol stacks and reconstructing trust mechanisms, it delivers an end-to-end solution spanning content creation, auditing, distribution, and value allocation. Federated learning metadata and blockchain interfaces are embedded within RTP/RTCP protocols, establishing a terminal-edge-cloud three-tier auditing system for hierarchical content filtering and localized privacy processing. A smart contract framework enables digital rights confirmation and value circulation through decentralized incentives and token-driven economics. Performance analysis confirms significant improvements in auditing efficiency and copyright transparency while maintaining ultra-low latency and supporting multimodal experiences, providing a systematic paradigm for secure, trustworthy streaming ecosystems.
Trading is an effective way to exchange resources such as energy, data, and computing services between Internet of Things (IoT) for mutual benefit. The emerging blockchain-enabled market facilitates the transparency and traceability of trading, but poses new challenges to the financial stability of the market. The existing blockchain-enabled market framework is designed as a completely free market, and due to the inherent characteristics of blockchain, it is difficult to take effective measures to maintain financial stability. To address this challenge, we propose a regulated blockchain-enabled market framework for IoT devices. Based on the heterogeneous interacting agent theory in economics, we study the price dynamics and equilibrium in the market. We establish a trading smart contract that supports trading among IoT devices, and provides the market regulator with interfaces to adjust the regulatable factors in the market to promote financial stability. A prototype system of our framework is implemented in Ethereum. The experimental results demonstrate the low execution cost of the smart contract, the correctness of our theoretical analysis for the market, and the effectiveness of the regulatory policies.
With the increasing popularity of high-frequency trading and decentralized finance (DeFi) applications, it has become essential to implement protections for regular users of DeFi platforms against large parties with massive amounts of resources, allowing them to engage in market manipulation strategies like the maximum extractable value (MEV). MEV can be extracted using the Bellman-Ford flow algorithm, making it easy for bots to realize MEV in DeFi. In classical trading, front-running is illegal and is classified under the umbrella of insider trading. However, the classical approaches to the mitigation of MEV do not apply to DeFi applications, as the mitigation of classic insider trading is based on central enforcement through legal laws. DeFi has in it both blockchain platforms and automated market makers (AMM) on decentralized exchanges (DEX), which introduces additional stakeholders that are virtual/anonymous for which the fear of legal laws/suits do not apply. Hence, a variety of approaches have been proposed in the literature to overcome or minimize MEV. In this paper, first, we illustrate various MEV attacks in classical trading, show how MEV can be exploited algorithmically, and discuss mitigation techniques to overcome MEV in classical trading. Secondly, we show that the same mitigation techniques for overcoming MEV in DeFi do not apply as additional stakeholders are introduced. Thirdly, we describe the design of a robust AMM on XRPL blockchain ledger that overcomes MEV. From such a design on XRPL which is one of the widely used blockchain platforms that has several constraints as compared to the general permissionless or permissioned blockchain platforms, we discuss issues of overcoming MEV in general DeFi applications. It is argued that such limitations necessitate a practical regulatory approach supported by a good forensic methodology with tools to instill confidence in the use of DeFi.
This paper explores the application of blockchain technology in managing and sharing medical data, focusing on the use of Non-Fungible Tokens (NFTs) and the InterPlanetary File System (IPFS) to enhance data security, privacy, and interoperability. Traditional methods of data management in healthcare face significant challenges, including data fragmentation, security vulnerabilities, and lack of interoperability among diverse systems. Blockchain technology, with its decentralized and immutable nature, offers a robust solution to these challenges. We provide a comparative analysis of transaction fees across four Ethereum Virtual Machine (EVM)-compatible platforms—BNB Chain, Fantom, Polygon, and Celo—to assess their suitability for medical data management. The study evaluates key operations, such as initiating transactions, minting NFTs, and transferring NFTs, which are essential for the secure and efficient handling of medical records. By examining these platforms, this paper aims to identify a cost-effective blockchain solution that balances operational expenses with performance requirements. The findings contribute to the ongoing development of secure, transparent, and reliable systems for managing medical data in healthcare settings.
In this paper, we design a decentralized drone delivery platform named CoEat. The article describes the necessity of the CoEat platform and the birth of DeFi economy, and elaborates on the three development stages of the platform in detai. Finally, the article will further discuss the innovative technologies applied in platform development and provide prospects for the platform’s future growth.
