Computer Communications最新文献

With the widespread use of mobile communication and smart devices in the medical field, mobile healthcare has gained significant attention due to its ability to overcome geographical limitations and provide more efficient and high-quality medical services. In mobile healthcare, various instruments and wearable device data are collected, encrypted, and uploaded to the cloud, accessible to medical professionals, researchers, and insurance companies, among others. However, ensuring the security and privacy of healthcare data in the context of mobile networks has been a highly challenging issue. Certificateless signature schemes allow patients to conceal their respective privacy information for different sharing needs. Nevertheless, existing mobile healthcare data protection solutions suffer from costly certificate management and the inability to restrict signature verifiers. This paper proposes a certificateless designated verifier sanitizable signature for mobile healthcare scenarios, aiming to enhance the security and privacy of mobile healthcare data. This scheme enables the sanitization of sensitive data without the need for certificate management and allows for the specification of signature verifiers. This ensures the confidentiality of medical data, protects patient privacy, and prevents unauthorized access to healthcare data. Through security analysis and experimental comparisons, it is demonstrated that the proposed scheme is both efficient and effectively ensures data security and user privacy. Therefore, it is well-suited for privacy protection in mobile healthcare data.

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the traditional radio access network (RAN), introducing Open RAN (O-RAN). This new paradigm is based on a virtualized and intelligent RAN architecture. However, with the increased complexity of 5G applications, traditional application-specific placement techniques have reached a bottleneck. Our paper presents a Transfer Learning (TL) augmented Reinforcement Learning (RL) based networking slicing (NS) solution targeting more effective placement and improving downtime for prolonged slice deployments. To achieve this, we propose an approach based on creating a robust and dynamic repository of specialized RL agents and network slices geared towards popular user service types such as eMBB, URLLC, and mMTC. The proposed solution consists of a heuristic-controlled two-module-based ML Engine and a repository. The objective function is formulated to minimize the downtime incurred by the VNFs hosted on the commercial-off-the-shelf (COTS) servers. The performance of the proposed system is evaluated compared to traditional approaches using industry-standard 5G traffic datasets. The evaluation results show that the proposed solution consistently achieves lower downtime than the traditional algorithms.

With cyber threats evolving alongside technological progress, strengthening network resilience to combat security vulnerabilities is crucial. This research extends cyber-crime analysis with an innovative approach, utilizing data mining and machine learning to not only predict cyber incidents but also reinforce network robustness. We introduce a real-time data collection framework to provide up-to-date cyberattack data, addressing current research limitations. By analyzing collected attack data, we identified temporal correlations in attack volumes across consecutive time frames. Our predictive model, developed using advanced machine learning and deep learning techniques, forecasts the frequency of cyber-attacks within specific time windows, demonstrating over a 15% improvement in accuracy compared to conventional baseline models. The methodologies employed include the use of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) for capturing complex patterns in time series data, and the integration of a sliding window technique to transform raw data into a format suitable for supervised learning. Our experiments evaluated the performance of various models, including ARIMA, Random Forest, Support Vector Regression, and K-Nearest Neighbors Regression, across multiple scenarios. Furthermore, we developed a Power BI platform for visualizing global cyber-attack trends, providing valuable insights for enhancing cybersecurity defences. Our research demonstrates that cyber incidents are not entirely random, and advanced AI tools can significantly enhance cybersecurity defences by analyzing patterns and trends from previous instances. This comprehensive approach not only improves prediction accuracy but also offers a robust framework for reducing the risk and impact of future cyber-crimes through enhanced detection and prediction capabilities.

This paper investigates the directional transmission scheme for airborne networks (ANs) with orthogonal time frequency space (OTFS) modulation, to cope with the degradations of communication performance due to the aircraft’s uncertain and high-speed mobilities. Besides showing better communication performance in high mobility scenarios such as AN, OTFS has also been verified for realizing the integrated sensing and communication (ISAC) systems. In this case, this paper proposes a sensing-assisted beam prediction method by exploiting echoes to predict the next locations of moving aircraft, solving the beam rendezvous problem at the transmitter. Besides, for the data detection problem at the receiver, this paper proposes a novel pilot placement scheme relying on the predicted delays and Dopplers, realizing accurate channel estimation with lower overheads. Simulation results show that the proposed OTFS-based directional transmission scheme can achieve reliable communication performance with a low bit error rate.

Providing cloud computing services leads to quick access of users to dynamic and distributed resources. Increasing demand has created challenges such as resource availability, privacy, and security to provide efficient services in cloud computing. Cloud environments contain various computing resources, and allocating a suitable node to process a request can improve the quality of service on a large scale. Load balancing is one of the strategies to improve service quality and resource utilization, which refers to the distribution of load among different nodes in a distributed system. The cloud application service broker is responsible for load balancing by choosing the appropriate geo-distributed datacenter to process the requests of each end user. Parameters such as transmission delay, network delay, processing time, number of servers, workload, and service cost can be considered to select a suitable datacenter in close proximity. To reduce the adverse effects of choosing a datacenter by a service broker, this paper presents Rank-based Load Balancing in Geo-Distributed datacenters (RLBGD) as an effective service broker strategy in cloud environments. RLBGD uses a weighted combination of several criteria such as processing time, number of servers, workload, processing speed, service cost, and response time for dynamic ranking and determining the appropriate datacenter. CloudAnalyst tool is used to simulate and analyze the performance of the proposed method. The results of experiments show the effectiveness of RLBGD in terms of metrics such as service cost and processing time in different scenarios.

Speech is among the main forms of communication between humans and robots in industrial settings, being the most natural way for a human worker to issue commands. However, the presence of pervasive and loud environmental noise poses significant challenges to the adoption of Speech-Command Recognition systems onboard manufacturing robots; indeed, they are expected to perform in real time on hardware with limited computational capabilities and also to be robust and accurate in such complex environments. In this paper, we propose an innovative system based on an End-to-End architecture with a Conformer backbone. Our system is specifically designed to achieve high accuracy in noisy industrial environments and to guarantee a minimal computational burden to meet stringent real-time requirements while running on computing devices that are embedded in robots. In order to increase the generalization capability of the system, the training procedure is driven by a Curriculum Learning strategy combined with dynamic data augmentation techniques, that progressively increase the complexity of input samples by increasing the noise during the training phase. We have conducted extensive experimentation to assess the effectiveness of our system, using a dataset composed of more than 50,000 samples, of which about 2,000 have been acquired during the daily operations of a Stellantis Italian factory. The results confirm the suitability of the proposed approach to be adopted in a real industrial environment; indeed, it is able to achieve, on both English and Italian commands, an accuracy higher than 90%, maintaining a compact model size (the network is 1.81 $MB$) and running in real-time on an industrial embedded device (namely $41\mathrm{ms}$ over an NVIDIA Xavier NX).

Locally verifiable aggregate signature primitives can reduce the complexity of aggregate signature verification by computing locally open algorithms to generate auxiliary parameters. However, the breakthrough results of quantum computers at this stage indicate that it will be possible for quantum computers to break through the security of traditional hardness-based aggregated signature schemes. In order to solve the above problems, this paper proposes for the first time a new locally verifiable class of multi-member quantum threshold aggregated digital signature scheme based on the property that the verification of quantum coset states is a projection on the trans-subspace. Combined with the idea of auxiliary parameter generation in traditional locally verifiable aggregated signatures, it makes the current stage of threshold quantum digital signatures realize the aggregated features, and reduces the complexity of the verification of aggregated signatures while realizing post-quantum security. In addition, the verification of the signature key (quantum state) of the signature members does not require measurement operations, and the generated signatures are classical, so the communication between the trusted third center (TC), the set of signature members, the classical digital signature verifier (CV), and the third-party trusted aggregation generator (TA) are all classical, simplifying the communication model. In the performance analysis we make this quantum aggregation signature scheme more flexible as well as less quantum state preparation compared to other schemes.

The space-air-ground integrated network (SAGIN) comprises a multitude of interconnected and integrated heterogeneous networks. Its network is large in scale, complex in structure, and highly dynamic. Virtual network embedding (VNE) is designed to efficiently allocate resources within the physical host to diverse virtual network requests (VNRs) with different constraints while improving the acceptance ratio of VNRs. However, in a heterogeneous SAGIN environment, improving the utilization of network resources while ensuring the performance of the VNE algorithm is a very challenging topic. To address the aforementioned issues, we first introduce a services diversion strategy (SDS) to select embedded nodes based on different service types and network state, thereby alleviating the uneven use of resources in different network domains. Subsequently, we propose a VNE algorithm (GAIL-VNE) based on generative adversarial imitation learning (GAIL). We construct a generator network based on the actor-critic architecture, which can generate the probability of physical nodes being embedded based on the observed network state. Secondly, we construct a discriminator network to distinguish between generator samples and expert samples, which aids in updating the generator network. After offline training, the generator and discriminator reach a Nash equilibrium through game confrontation. During the embedding process of VNRs, the output of the generator provides an effective basis for generating VNE solutions. Finally, we verify the effectiveness of this method through experiments involving offline training and online embedding.

In heterogeneous cellular networks (HCNs), neighboring users often request similar contents asynchronously. Based on the content popularity, base stations (BSs) can download and cache contents when the network is idle, and transmit them locally when the network is busy, which can effectively reduce the backhaul burden and the transmission delay. We consider a two-tier HCN, where macro base stations (MBSs) and small base stations (SBSs) can cooperatively and probabilistically cache contents. Each user is associated to the BS with the maximum average received signal power in any tier. With the cooperative content transfer between MBS tier and SBS tier, users can adaptively obtain contents from BSs or remote content servers. We properly model both wired and wireless delays when a user requests an arbitrary content, and propose the concept of effective delay. Content caching probabilities are optimized using the Marine Predators Algorithm via minimizing the average effective delay. Numerical results show that our proposed cooperative caching scheme achieves much shorter delays than the benchmark caching schemes.