International Journal of Network Management最新文献

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.

We explore the challenges around security configuration and performance while utilizing publish–subscribe protocols in the Internet of Things, edge computing, and fog computing. Issues in security configuration can lead to disruptions and higher operation costs. These issues can be prevented by selecting the appropriate transmission technology for each configuration, considering the variations in sizing, installation, sensor profile, distribution, security, networking, and locality. We introduce a comparative analysis of different configurations around a smart agriculture use case. For that, we implemented a simulation environment to generate datasets relevant to research and compare the results in terms of performance, resource utilization, security, and resilience—focused on authentication process. This analysis provides a blueprint to decision support for fog computing engineers on the best practices around security configurations.

Swarm of Unmanned Aerial Vehicles (UAVs) broaden the field of application in various fields like military surveillance, crop monitoring in agriculture, combat operations, etc. Unfortunately, they are becoming increasingly susceptible to security attacks, such as jamming, information leakage and spoofing, as they become more common and in more demand. So, there is a wider need for UAVs, which requires the design of strong security procedures to fend off such attacks and security dangers. Even though several studies focused on security aspects, many questions remain unanswered, particularly in the areas of secure UAV-to-UAV communication, support for perfect forward secrecy and non-repudiation. In a battle situation, it is extremely important to close these gaps. The security requirements for the UAV communication protocol in a military setting were the focus of this study. In this paper, we present the issues faced by the UAV swarm, especially during military surveillance operations. To secure the communication link in UAV, a new protocol for UAV Swarm communication is proposed with anonymous secure messaging token-based protocol (ASMTP). The proposed protocol secures UAV-to-base station communication and safeguards the metadata of the sender and receiver nodes. The proposed model maintains the confidentiality, integrity and availability of data in the UAV Swarm and achieves robustness. In addition, it provides a different strategy for the cybersecurity gaps in the swarm of UAVs during military surveillance and combat operations.

The neural network-based technologies have emerged as a potent method for image fusion, object detection, and other computer vision tasks as the rapid development of deep learning. Multi-band infrared images, in particular, capture a more extensive range of radiation details and information compared to conventional single-band infrared images. Consequently, the fusion of multi-band infrared images can provide more features for object detection. However, it is crucial to consider that infrared images may contain sensitive information, potentially leading to privacy concerns. Ensuring datasets privacy protection plays a crucial role in the fusion and tracking process. To address both the need for improved detection performance and the necessity for privacy protection in the infrared environment, we proposed a procedure for object detection based on multi-band infrared image datasets and utilized the transfer learning technique to migrate knowledge learned from external infrared data to internal infrared data, thereby training the infrared image fusion model and detection model. The procedure consists of several steps: (1) data preprocessing of multi-band infrared images, (2) multi-band infrared image fusion, and (3) object detection. Standard evaluation metrics for image fusion and object detection ensure the authenticity of the experiments. The comprehensive validation experiments demonstrate the effectiveness of the proposed procedure in object detection tasks. Furthermore, the transfer learning can train our datasets and update the model without exposing the original data. This aspect of transfer learning is particularly beneficial for maintaining the privacy of multi-band infrared images during the fusion and detection processes.