Computer Communications最新文献

A Non-Orthogonal Multiple Access (NOMA) network, in which the base station (BS) incorporates massive Multiple-Input Multiple-Output (MIMO) technology, is considered in this paper. This research study focuses on investigating physical layer security in this network when a jammer is present, leveraging intelligent Reflecting Surface (IRS) technology. The IRS is an innovative approach strategically implemented to enhance communication quality by assisting distant users in establishing a reliable connection with the BS. Two key metrics in physical layer security are evaluated: the secrecy rate (SR) for pairs of NOMA users and the secrecy outage probability (SOP). Additionally, the impact of using a jammer is assessed by comparing the network’s performance with and without a jammer. The results indicate that by increasing in the antenna numbers, the rate of secrecy is improved, and the SOP is decreased. Moreover, as the transmit signal-to-noise ratio (SNR) increases, the SR is enhanced, but the SOP is degraded. However, the increase in the IRS element numbers results in a tendency for the SOP to rise. Furthermore, it is evident that incorporating a jammer improves the network’s performance.

Coexistence of enhanced mobile broadband and ultra-reliable low latency communication in 5G networks is a challenging problem due to the conflicting requirements. In this paper, we decompose the problem into eMBB and URLLC resource allocation phases. For the first phase we propose a heuristic algorithm with $O\left(n\right)$ runtime and prove its efficiency and optimality under min–max fairness paradigm. For the URLLC resource allocation, the puncturing framework is adopted and a novel approach using the Graph Neural Networks is proposed to maximize eMBB data rates and fairness while minimizing URLLC outage probability. We show that the runtime of this GNN-based algorithm is also $O\left(n\right)$. To train the GNN, an application-specific loss function is designed and empirically shown to be convergent. Our simulation results show that our proposed approach performs very well in terms of eMBB data rates, fairness, and URLLC outage probability in comparison to a number of thoughtfully chosen baselines. We also demonstrate that the proposed GNN is robust to changes in network topology and traffic volume. As we show our algorithm has $O\left(n\right)$ runtime, it is fully practical for solving the resource allocation problem in the very short time spans that are required by 5G and future generation networks.

Rumors in social media platforms and the identification of their sources is a challenging issue in modern-day computer communication. Existing approaches mostly fail to localize the source node accurately due to the lack of complete network information or timestamps. Besides, most of the techniques focused on single-source identification only, while sometimes multiple sources exist in the network. In this paper, we designed a new algorithm called Multi Snowballing with Partial Timestamps (MSPT) to find multiple sources utilizing partial timestamps available to monitors. We have explored the snowballing technique to determine the vulnerable radius that may contain the rumor source based on the partial timestamps of a few nodes. The overall complexity of the algorithm is $O(N_S\ast (N_S+E_S))$, where $N_S$ is the set of snowball nodes and $E_S$ represents edges in between snowball nodes. Extensive empirical analysis is performed on a variety of networks, which include small-scale, large-scale, and artificial networks. Empirical outcomes demonstrate that the presented algorithm is efficient in terms of error distance and execution time compared to baseline algorithms.

Innovation in medical technology and communication has rapidly empowered the development of smart healthcare devices. This has led to privacy breaches, threats and vulnerabilities to sensitive patient data that result in unwanted or targeted advertising. Previous research has focused on protecting access to sensitive patient data from unauthorized entities, especially by defining roles of healthcare entities in the overall system with their access privileges. However, such efforts need to be further robust due to the involvement of a single key authority that may lead to a critical point of failure. In this paper, this vulnerability has been addressed by developing a novel approach to crucially increase the number of key authorities using homomorphic encryption. The proposed approach ensures genuine access to the verified entity by forming a subsystem of t key authorities from a total of n authorities $(t<n)$. This creates rigorous challenge to a malicious attacker, obfuscating the selection and functioning of key access packets in a multi-key authority setup. The results of the proposed approach achieve medical data confidentiality, entity authentication, and strategic data sharing. The security of the proposed approach is assessed for different vulnerabilities of the overall system using a challenge–response game model. Moreover, the proposed approach is found to be better and secure as compared to existing schemes.

In communication network planning, a rational base station layout plays a crucial role in improving communication speed, ensuring service quality, and reducing investment costs. To address this, the article calibrated the urban microcell (UMa) signal propagation model using the least squares method, based on road test data collected from three distinct environments: dense urban areas, general urban areas, and suburbs. With the calibrated model, a detailed link budget analysis was performed on the planning area, calculating the maximum coverage radius required for a single base station to meet communication demands, and accordingly determining the number of base stations needed. Subsequently, this article proposed the Adaptive Mutation Genetic Algorithm (AMGA) and formulated a mathematical model for optimizing 5G base station coverage to improve the base station layout. Simulation experiments were conducted in three different scenarios, and the results indicate that the proposed AMGA algorithm effectively enhances base station coverage while reducing construction costs, thoroughly demonstrating the value of base station layout optimization in practical applications.

The asymmetric massive multiple-input–multiple-output (MIMO) array improves system capacity and provides wide-area coverage for the Internet of Things (IoT). In this paper, we propose a novel attention-based model for path loss (PL) prediction in asymmetric massive MIMO IoT systems. To represent the propagation characteristics, the propagation image that considers the detailed environment, beamwidth pattern, and propagation-statistics feature is designed. Benefiting from the shuffle attention computation, the proposed model, termed a shuffle-attention-based convolutional neural network (SAN), can effectively extract the detailed features of the propagation scenario from the image. Besides, we design the beamwidth-scenario transfer learning (BWSTL) algorithm to assist the SAN model in predicting PL in the new asymmetric massive MIMO IoT systems, where the beamwidth configuration and propagation scenario are different. It is shown that the proposed model outperforms the empirical model and other state-of-the-art artificial intelligence-based models. Aided by the BWSTL algorithm, the SAN model can be transferred to new propagation conditions with limited samples, which is beneficial to the fast deployment in the new asymmetric massive MIMO IoT systems.

The emergence of autonomous driving technologies has been significantly influenced by advancements in perception systems. Traditional single-agent detection models, while effective in certain scenarios, exhibit limitations in complex environments, necessitating the shift towards collaborative detection models. While numerous studies have investigated the fundamental architecture and primary elements within this domain, comprehensive analyses focusing on the evolution from single-agent-based detection systems to collaborative detection systems are notably absent. This paper provides a comprehensive examination of this transition, delineating the development from single agent to collaborative perception models in autonomous driving. Initially, this paper delves into single-agent detection models, discussing their capabilities, limitations, and application scenarios. Subsequently, the focus shifts to collaborative detection models, which leverage Vehicle-to-Everything (V2X) communication to enhance perception and decision-making in complex environments. Fundamental concepts about mainstream collaborative approaches and mechanisms are reviewed to present the general organization of collaborative detection models. Furthermore, we critically evaluates various collaborative models, comparing their performance, data fusion strategies, and adaptability in dynamic settings. The integration of V2X-enabled Internet-of-Vehicles (IoV) introduces a pivotal evolution in the transition from single-agent-based detection to multi-agent collaborative sensing. This advancement allows for real-time interaction of sensory information between vehicles, augmenting the development of collaborative sensing. However, the interaction of sensory information also increases the load on the network, highlighting the need for strategies that achieve a balance between communication overhead and the improvement in perception capabilities. We concludes with future perspectives, emphasizing the potential issues the development of collaborative detection models will meet and the promising directions for future research.

Appropriate data rate selection at the physical layer is crucial for Wi-Fi network performance: too high rates lead to loss of data frames, while too low rates cause increased latency and inefficient channel use. Most existing methods adopt a probing approach and empirically assess the transmission success probability for each available rate. However, a transmission failure can also be caused by frame collisions. Thus, each collision leads to an unnecessary decrease in the data rate. We avoid this issue by resorting to the fine timing measurement (FTM) procedure, part of IEEE 802.11, which allows stations to perform ranging, i.e., measure their spatial distance to the AP. Since distance is not affected by sporadic distortions such as internal and external channel interference, we use this knowledge for data rate selection. Specifically, we propose FTMRate, which applies statistical learning (a form of machine learning) to estimate the distance based on measurements, predicts channel quality from the distance, and selects data rates based on channel quality. We define three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. Then, with a thorough performance evaluation using simulations and an experimental validation with real-world devices, we show that our approach has several positive features: it is resilient to collisions, provides near-instantaneous convergence, is compatible with commercial-off-the-shelf devices, and supports pedestrian mobility. Thanks to these features, FTMRate outperforms existing solutions in a variety of line-of-sight scenarios, providing close to optimal results. Additionally, we introduce Hybrid FTMRate, which can intelligently fall back to a probing-based approach to cover non-line-of-sight cases. Finally, we discuss the applicability of the method and its usefulness in various scenarios.

Federated learning based on local differential privacy and blockchain can effectively mitigate the privacy issues of server and provide strong privacy against multiple kinds of attack. However, the actual privacy of users gradually decreases with the frequency of user updates, and noises from perturbation cause contradictions between privacy and utility. To enhance user privacy while ensuring data utility, we propose a Hybrid Aggregation mechanism based on Shuffling, Subsampling and Shapley value (HASSS) for federated learning under blockchain framework. HASSS includes two procedures, private intra-local domain aggregation and efficient inter-local domain evaluation. During the private aggregation, the local updates of users are selected and randomized to achieve gradient index privacy and gradient privacy, and then are shuffled and subsampled by shufflers to achieve identity privacy and privacy amplification. During the efficient evaluation, local servers that aggregated updates within domains broadcast and receive updates from other local servers, based on which the contribution of each local server is calculated to select nodes for global update. Two comprehensive sets are applied to evaluate the performance of HASSS. Simulations show that our scheme can enhance user privacy while ensuring data utility.

The development and roll-out of new Ethernet standards increase the available bandwidths in computer networks. This growth presents significant advantages, enabling novel applications. At the same time, the increase introduces new challenges; higher data rates reduce the available time budget to process each packet. This development also impacts software-defined networks. Their data planes need to keep up with the increased traffic rates. Nevertheless, the control plane must not be ignored; fast reaction times are necessary to handle the increased rates handled by data planes efficiently.

In our work, we analyze the interaction of a high-performance data plane and different implementations for the control plane. We selected a P4 switching ASIC as our data plane. For the control plane, we investigate vendor-specific implementations and a standardized implementation called P4Runtime. To determine the performance of the control plane, we introduce a novel measurement methodology. This methodology allows measuring the delay between the initiation of rule updates on the control plane and their application on the data plane. We investigate the behavior of the data plane, its performance and non-atomicity of updates. Based on our findings, we apply different optimization strategies to improve control plane performance. Our measurements show that neglecting the control plane performance may impact network behavior due to delayed updates, but we also show how to minimize this delay and, thereby, its impact. We have released the experiment artifacts of our study including experiment scripts and measurement data.