International Journal of Network Management最新文献

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.

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.