International Journal of Network Management最新文献

This special issue contains extended versions of the best papers from the IEEE CryptoEx 2023 workshop (https://icbc2023.ieee-icbc.org/workshop/cryptoex-2023), which was held as a co-located workshop with 2023 IEEE International Conference on Blockchain and Cryptocurrency. The workshop was held on Friday, May 5, 2023, in Dubai, UAE. The papers in this special issue explore crucial advancements in fractional NFTs, stablecoins, and cryptocurrency exchanges, reflecting the diverse and innovative applications of blockchain technology.

The first paper, titled “Fractional Non-Fungible Tokens (NFTs): Overview, Evaluation, Marketplaces, and Challenges,” authored by Wonseok Choi, Jongsoo Woo, and James Won-Ki Hong, explores the innovative concept of fractional NFTs. By democratizing access to high-value digital assets, fractional NFTs merge tokenization, smart contracts, and ownership models to revolutionize the digital economy. The paper evaluates gas consumption and examines regulatory and security challenges, underscoring the importance of transparency and robust security measures in fostering trust within fractional NFT ecosystems.

The second paper, titled “Leveraging Ponzi-like Designs in Stablecoins,” by Shange Fu, Qin Wang, Jiangshan Yu, and Shiping Chen, provides a novel perspective on algorithmic stablecoins, which are often dismissed as Ponzi schemes. This study clarifies the fundamental nature of Ponzi schemes and introduces a rational model for evaluating the sustainability of algorithmic stablecoins. By applying historical data, the paper identifies conditions under which these stablecoins can function effectively as rational Ponzi games, offering a new understanding of their stability mechanisms.

The third paper, titled “Athena: Smart Order Routing on Centralized Crypto Exchanges using a Unified Order Book,” authored by Robert Henker, Daniel Atzberger, Jan Ole Vollmer, Willy Scheibel, Jürgen Döllner, and Markus Bick, describes the development and implementation of Athena. This system optimizes trading strategies by integrating order books from multiple centralized crypto exchanges into a unified order book. Athena's smart order routing algorithm significantly reduces implicit trading costs, making it particularly beneficial for institutional investors in illiquid crypto markets.

The fourth paper, titled “Deeper: A Shared Liquidity DEX Design for Low Trading Volume Tokens to Enhance Average Liquidity,” by Srisht Fateh Singh, Panagiotis Michalopoulos, and Andreas Veneris, introduces Deeper, a decentralized exchange design aimed at improving liquidity for low trading volume tokens. By enabling liquidity providers to share reserves of a common token, Deeper addresses issues like high slippage and sandwich attacks. The paper demonstrates the enhanced liquidity achieved through historical price experiments and highlights potential risks for liquidity providers.

We believe that these four papers make significant cont

Metaverse's augmented reality (AR) function allows virtual information to be seamlessly superimposed onto real scenes through the camera of a head-mounted device. However, this raises concerns about privacy protection and copyright authentication when transmitting cross-media information. Additionally, there is a risk of secret information leakage due to screen candid shooting in the real world. Ensuring information security and copyright authentication in case of unauthorized screen capturing is crucial. To prevent information loss and interference from cross-media transfer between screens and cameras, we implement digital watermarking for copyright protection. We have proposed an innovative framework for automatic document watermarking that can resist screen-shooting. Our approach involves embedding a ring watermark in the document underlay. On the extraction side, the watermark extraction process is divided into three key steps: automatic location, automatic correction, and automatic extraction. First, the document image is located in the covert photography. Then, perspective correction is performed based on the text line features of the document. Finally, the watermark information is extracted by combining the ring watermark features. Our method is capable of automatically extracting watermarks from covert photography while considering aspects such as concealment, robustness, and visual quality. The watermark is embedded in the document underlay, which ensures good visual quality and does not affect the normal reading and editing. We also propose various embedding strength schemes that can adapt to different usage scenarios, providing resistance to screen-shooting or screenshot attacks, as well as various noise attacks. Through extensive experiments, we have demonstrated the feasibility of the proposed automated framework and the robustness of the watermarking algorithm, as well as the superiority and broad application prospects of our method.

Thoracic surgeries in major lung resections for primary lung cancer are fraught with potential risks, emphasising the need to understand factors contributing to postoperative mortality. This study investigates the interplay of objective and subjective data in predicting postoperative outcomes to reduce data transmission costs in the Internet of Medical Things (IoMT). Objective metrics, such as forced vital capacity (FVC), offer consistent, quantifiable insights essential for predictive modelling. Conversely, subjective data derived from patient self-reports suggest that the patient's personal experiences are crucial for assessing the quality of life postsurgery. Utilising a dataset from the University of California, Irvine's Machine Learning Repository (UCI), 17 distinct attributes were examined. Using ensemble learning classifiers, the extra trees classifier is superior when utilising all features, achieving an accuracy of 0.92. Combining select subjective features, specifically PRE6, PRE8 and AGE (demographic), with objective data, yielded a comparable accuracy of 0.91. Feature importance analysis further highlights the significance of features like PRE5, PRE4 and AGE. This suggests potential redundancies in the full feature set, emphasising the importance of feature selection. Importantly, when compared with existing literature, this study's findings offer insights into the future of predictive modelling in thoracic surgeries, with implications for the rapidly evolving field of the IoMT.

We investigate the recent fee mechanism EIP1559 of the Ethereum network. Whereas previous studies have focused on myopic miners, we here focus on strategic miners in the sense of miners being able to reason about the future blocks. We derive expressions for optimal miner behavior (in terms of setting block sizes) in the case of two-block foresight and varying degrees of hashing power. Results indicate that a sufficiently large mining pool will have enough hashing power to gain by strategic foresight. We further use a simulation study to examine the impact of both two-block and three-block foresight. In particular, the simulation study indicates that for realistic levels of hashing power, mining pools do not gain from being able to reason more than two blocks ahead. Moreover, even though the presence of strategic miners increases the variation in block sizes and potentially empty blocks, overall system throughput tends to increase slightly compared with myopic mining. We further analyze the effect of varying the base fee updating rule.

Stablecoin represents a unique subset of cryptocurrencies designed to offer price stability, achieved either through backing by specific assets or by employing algorithms that adjust their supply in response to market demand. In its landscape, algorithmic stablecoin is one special type that is not backed by any asset, and it stands to revolutionize the way a sovereign fiat operates. As implemented, algorithmic stablecoins are poorly stabilized in most cases; their prices easily deviate from the target or even fall into a catastrophic collapse and are as a result often dismissed as a Ponzi scheme. However, what is the essence of Ponzi? In this paper, we try to clarify such a deceptive concept and reveal how algorithmic stablecoin works from a higher level. We find that Ponzi is basically a financial protocol that pays existing investors with funds collected from new ones. Running a Ponzi, however, does not necessarily imply that any participant is in any sense losing out, as long as the game can be perpetually rolled over. Economists call such realization as a rational Ponzi game. We accordingly propose a rational model in the context of algorithmic stablecoin and draw its holding conditions. We apply the model and use historical data to examine if the major types of algorithmic stablecoins meet the criteria for being a rational Ponzi game.

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency.

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency.