Computer Networks最新文献

This study focuses on the innovative field of quantum synchronization for satellite-based navigation systems including Global Navigation Satellite Systems (GNSSs) and the Non-Terrestrial Network (NTN) component of future 6G networks integrating both communication and navigation services.

By combining a four-qubit system with the theoretical approach of the Lindblad master equation, we transcend the inherent limits of standard synchronization techniques. This achievement represents a quantum leap in satellite-based positioning, highlighting the scalability and cost-effectiveness of our technique for smaller satellites. The study demonstrates the possibility of reducing synchronization errors to less than one meter, significantly improving the reliability and precision of satellite-based navigation systems.

The results of this study may contribute to the future development of both user-centric localization systems (typically GNSS systems) and network-centric localization systems (typically through the NTN component of 6G networks), leading to better positioning performance, more flexible multi-functional systems with the potential to limit both cost and size of satellites.

The tremendous growth rate of global internet traffic in past years increases the importance of traffic prediction for network operators to ensure seamless Quality of Service (QoS) with proactive traffic engineering. Even minor anomalies in traffic management can lead to service disruptions that affect a vast user base, necessitating highly accurate traffic predictions. While recent studies have exploited Deep Learning for accurate traffic predictions, most of them have targeted mobile network traffic and they often fall short in delivering precise long-range predictions and effective spatiotemporal feature extraction from single-stream time-series data. This research addresses these limitations by proposing a Convolutional Recurrent Generative Adversarial Network (CoRe-GAN) consisting of generator and discriminator neural networks for high-accuracy traffic prediction. The generator with Convolutional Long Short-Term Memory (ConvLSTM) model effectively captures intricate features, whereas the discriminator utilizes a Convolutional Neural Network (CNN) to train the generator through feedback. Moreover, advanced training techniques like fact forcing and feature matching increase the learning convergence rate, avoid mode collapse, and amplify prediction accuracy of CoRe-GAN. The evaluation with Pangyo Network Dataset (PND) and synthetic Intrusion Detection Dataset (IDD) confirms CoRe-GAN superiority. The results show that it outperforms ConvLSTM models with an average 20% and 16% lower Mean Square Error (MSE) with PND and IDD traffic data, respectively.

Currently, biometric-based authentication schemes are widely adopted in the field of online payments. Consequently, this has led to an increasing number of people becoming concerned about the privacy-preservation of their biometric data. Gunasinghe et al. presented PrivBioMTAuth, a solution for mobile phone biometric-based authentication designed to protect users’ privacy. However, the solution has drawbacks, such as its impact on the execution efficiency of the authentication protocol or its vulnerability to man-in-the-middle attacks during the authentication phase. Moreover, the user’s biometric image and the password must be revealed to the identity provider, which may raise security concerns. In this work, we present a novel secure and efficient biometric-based anonymous authentication solution with fully succinct verification and significantly lower storage and communication overhead. Different from PrivBioMTAuth, we rely on the NIZK argument given in Groth’s work to reduce the size of the anonymous identity and simplify the verification complexity. In addition, we design a high-performance protocol for conducting large-scale verification of the user’s anonymous identities. We propose an optimized multi-exponentiation argument based on Bayer et al.’s work and utilize it to ensure that a semi-trusted identity provider who seeks to access the users’ sensitive biometric information can faithfully execute the users’ identity registration protocol. The experiment results show that our proposed scheme is efficient and has privacy-preserving capabilities, and it can be applied in the resource-constrained devices.

We develop a novel framework for personalized federated learning (PFL) utilizing a decoupled version of knowledge distillation (DKD). Unlike traditional PFL methods, the proposed PFL-DKD creates a dynamically connected network among local clients and categorizes them according to their knowledge, storage, and computational capabilities. The developed decoupling of knowledge distillation into target class (TC) and latent class (LC) enables knowledge-rich clients to efficiently transfer their expertise to knowledge-poor clients. To further enhance our innovative PFL-DKD approach, we extend it to PFL-FDKD by introducing a ”logit fusion” that seamlessly aggregates knowledge and experiences from neighboring clients. Both our theoretical analyses and extensive experiments reveal that PFL-DKD outperforms existing centralized and decentralized PFL approaches, making significant strides in mitigating the challenges associated with heterogeneous data and system configurations. The details of our implementation with the codebase are in PFL-DKD.

With the proliferation of data-intensive applications, there arises an urgent demand for a substantial amount of cloud services to meet their requirements for data analysis. This globalized yet cooperative business landscape necessitates new cooperative models across the world. JointCloud, as a novel cross-cloud cooperation computing model, takes the first step towards establishing an evolving cloud ecosystem where all cloud service providers could collaboratively serve globalized computation needs. The collaboration among various cloud service providers enhances both the availability and Quality of Services(QoS) of cloud services, enabling a cloud service provider to concurrently serve users with differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service recommendations, rendering conventional approaches obsolete and inefficient. Thus, there is an urgent need to improve the efficiency and effectiveness of the service recommendation method, which is of vital importance for the JointCloud environment. In this paper, we present a two-stage efficient regret theory-based service recommendation method for the JointCloud environment. In the first stage of our proposed method, we cluster the cloud service providers to reduce the choice space to improve the efficiency of cloud service recommendations. In the second stage, we meticulously identify the most appropriate services within one cluster. To enhance the overall rationality of service recommendation, we introduce a subjective and objective combined weighting method and a regret theory based ranking method. Extensive experimental results demonstrate that our approach can facilitate fast and accurate service recommendations.

We review recent advancements in the domain of Joint Detection Receivers and Entanglement-Assisted Data transmission links with an emphasis on their potential use in future networks. Both data transmission techniques can surpass the Shannon limit by significant amounts in situations where either the number of photons per received information carrier or the number of transmitted photons per information carrier is extremely small. To obtain an advantage from shared entanglement, significant noise levels are needed as well. These fundamental constraints dictate that only certain application scenarios are of relevance to the new technology. We discuss these constraints in detail in the context of the current network architecture and stress the relation to optical computation. Based on our discussion, we go on to propose potential domains of application for the new technology.

In traditional blockchain systems, the Unsent Transaction Output (UTXO) dataset stores all generated UTXOs, which typically represent digital assets of the users. UTXOs can be used to track the output status of cryptocurrency transactions. However, with the development of blockchain networks, the UTXO dataset continuously increasing in scale. In the UTXO dataset, a large number of low-value UTXOs occupied huge storage space, making the blockchain dataset expanded. This impacts the running performance of the whole blockchain system. To address this issue, this paper proposes the Dynamic Blockchain UTXO Processing strategy (DBUP) for UTXO storage efficiency optimization. This strategy aims to rapidly consume the low-value UTXOs, effectively alleviating UTXO dataset expansion. Furthermore, Through improved simulated annealing (ISA) algorithm and a UTXO dynamic adjustment mechanism in DBUP, the strategy ensures that the transaction fee in the transaction process remain at a low level. The experimental evaluation indicates that our strategy can achieve the better performance against the Low Value First (LVF) and the First-In-First-Out (FIFO) strategies, enhancing the storage efficiency of blockchain systems.

This paper presents a novel routing method called ray tracing. The method targets wireless nanonetworks, which are networks of nanometer-sized nodes with applications in various domains such as future medicine and metamaterial. Due to their tiny size, nanonodes have quite limited resources, in particular energy, and it is crucial to use as few resources as possible. In multi-hop nanonetworks, resource consumption for routing depends on several factors, such as the number of nodes that forward packets, and the number of packets sent and received. To reduce the energy used, in the proposed ray tracing routing, only the nodes that receive duplicate packets at the same time forward them. This leads to the selection of the forwarding nodes over a quasistraight line between the source and the destination, which can bend at the edges of the network. The proposed method is a major improvement over its previous version, aiming to fix several crucial issues such as double propagation, large forwarding angle, and die-out. Moreover, the applicability of ray tracing in heterogeneous and 3D nanonetworks is also analyzed and a practical application for the protocol is proposed. Extensive simulation results demonstrate the fixing of these issues, and the superiority of the proposed improved ray tracing algorithm over other routing methods in the long and narrow nanonetworks, such as blood vessels and branches.

This paper investigates the performance of coded massive multiple-input multiple-output (MIMO) systems utilizing Orthogonal Time Frequency and Space modulation (OTFS). Our innovative approach harnesses the power of OTFS modulation, a cutting-edge modulation technique renowned for its capacity to mitigate the detrimental effects of time-varying channels. Additionally, we introduce a comprehensive system model that incorporates the pivotal elements of channel coding and decoding The system model incorporates channel coding and decoding to improve the bit error rate and enhance the overall performance. The numerical results show that the proposed scheme outperforms existing techniques in terms of BER and spectral efficiency, especially in high-mobility scenarios. The proposed system demonstrates significant robustness against channel estimation errors and Doppler spread. This indicates that coded massive MIMO employing OTFS modulation offers a highly effective solution for future wireless communication systems. The findings highlight the potential of this approach to enhance the reliability and performance of next-generation networks.

Service Function Chain (SFC) as an effective solution can satisfy the diverse service requirements of six application scenarios in Network Function Virtualization (NFV)-enabled 6G networks. Resilience as a new key capability indicator in 6G networks puts forward higher requirements for the Quality of Service (QoS) of SFCs. In this article, we study the resilience recovery method in network attack scenarios. We make full use of the monitoring and early warning capability of the monitoring function and propose a proactive recovery method called Double Deep Q-Network based on SFC Attack Graph (DDQN-SFCAG). We fully consider the characteristics of network attacks to generate SFC attack graphs and determine the recovery strategy of SFC according to the service security requirements to provide guidance for recovery. Among them, we design three recovery modes for the recovered Virtual Network Functions (VNFs) to determine the optimal recovery strategy and avoid resource waste. We formalize the SFC recovery problem, which aims to minimize the recovery cost while meeting the recovery strategy. In order to shorten the interruption time, we use DDQN to quickly solve the recovery solution to ensure optimal recovery performance. Our extensive evaluation shows DDQN-SFCAG has excellent recovery performance in network attack scenarios and can reduce the recovery cost by at least 31% compared to the state-of-the-art methods.