Annals of Telecommunications最新文献

Connected vehicles have software that must be updated to fix vulnerabilities or add new functionalities. While over-the-air updates prevent car owners from bringing their vehicles to a service center, they introduce significant security risks. This paper proposes a vehicular over-the-air update architecture combining the two most adopted trusted execution environment solutions: Intel SGX at the server and ARM TrustZone at the client. The main contribution is the protection of software updates from attackers that manipulate the entire operating system at both ends aiming to reverse engineering the software or introducing a malicious behavior. The implementation uses a device with OP-TEE and a software repository implemented with CACIC-DevKit. The paper also extends our previous work by evaluating an alternative server implementation using Gramine-SGX. Our experiments reveal that the impact of the TEE is negligible, even for small software block transfers. Compared with CACIC-DevKit, Gramine-SGX doubles the latency, despite the development simplification. This indicates that CACIC-DevKit better suits a high mobility scenario, such as vehicular networks, where the connection with the server may be short term.

In the realm of federated learning, a collaborative yet decentralized approach to machine learning, communication efficiency is a critical concern, particularly under constraints of limited bandwidth and resources. This paper evaluates FedSNIP, a novel method that leverages the SNIP (Single-shot Network Pruning based on Connection Sensitivity) technique within this context. By utilizing SNIP, FedSNIP effectively prunes neural networks, converting numerous weights to zero and resulting in sparser weight representations. This substantial reduction in weight density significantly decreases the volume of parameters that need to be communicated to the server, thereby reducing the communication overhead. Our experiments on the CIFAR-10 and UCI-HAR dataset demonstrate that FedSNIP not only lowers the data transmission between clients and the server but also maintains competitive model accuracy, comparable to conventional federated learning models. Additionally, we analyze various compression algorithms applied after pruning, specifically evaluating the compressed sparse row, coordinate list, and compressed sparse column formats to identify the most efficient approach. Our results show that compressed sparse row not only compresses the data more effectively and quickly but also achieves the highest reduction in data size, making it the most suitable format for enhancing the efficiency of federated learning, particularly in scenarios with restricted communication capabilities.

Transferring massive datasets in data-intensive science (DIS) systems often relies on physical WAN infrastructure for network connectivity. This infrastructure is typically provided by various National Research and Education Networks (NRENs), including ESnet, GÉANT, Internet2, and RNP. Studying these systems presents significant challenge due to their complexity, scale, and the numerous factors influencing data transport. Traditionally, network performance studies focus on a single bottleneck. In contrast, the Quantitative Theory of Bottlenecks Structures (QTBS) provides a mathematical framework that analyzes performance through the network’s entire bottleneck structure, offering valuable insights for optimizing and understanding overall network performance. This paper tackles such challenges by employing QTBS and by deploying and evaluating a virtual infrastructure for data transport within a national-scale WAN. Our approach focuses on three key aspects: (i) assessing flow completion times related to bandwidth allocation for interdependent transfers within a network slice, (ii) evaluating the performance of TCP congestion control algorithms (BBR versus Cubic) for data transport, and (iii) conducting QTBS analysis to compute flow allocation shares, ultimately aiming for an optimal design. Results show BBR outperforming Cubic in scenarios with high number of threads and data volume and the high influence of the number of threads.

Hadoop Distributed File System (HDFS) is known for its specialized strategies and policies tailored to enhance replica placement. This capability is critical for ensuring efficient and reliable access to data replicas, particularly as HDFS operates best when data are evenly distributed within the cluster. In this paper, we build upon earlier practical evaluations and conduct a thorough analysis of the replica balancing process in HDFS, focusing on two critical performance metrics: stability and efficiency. We evaluated these aspects alongside balancing operational cost by contrasting them with conventional HDFS solutions and employing a novel dynamic architecture for data replica balancing. On top of that, we delve into the optimizations in data locality brought about by effective replica balancing and their benefits for data-intensive applications, including enhanced read performance. Our findings reveal the extent to which data imbalance in HDFS directly affects the file system and highlight the struggles of the default replica placement policy in maintaining cluster balance. We examined the real but intricate and temporary effectiveness of on-demand balancing, underscoring the importance of regular and adaptable balancing interventions. This reaffirms the significance of context-aware replica balancing, as provided by the proposed dynamic architecture, not only for maintaining data equilibrium but also for ensuring efficient system performance.

This paper proposes a crosstalk-aware inter-core (XT) circuit reallocation algorithm for spatial division multiplexed elastic optical networks (SDM-EON). Unlike previous studies that utilize reallocation primarily for spectral defragmentation, this work focuses on circuit reallocation to mitigate XT, thereby reducing or preventing network blocking. The algorithm is triggered whenever a request is blocked, classifying it as a reactive approach. The push-pull and fast-switching techniques are employed for data traffic migration, ensuring seamless transition without service interruption. Furthermore, the proposed method is evaluated against other algorithms designed to mitigate inter-core crosstalk, considering the NSFNET, EON, and JPN network topologies. In terms of bandwidth blocking probability, the results demonstrate a reduction of at least 65%, with a maximum of 0.25% of active circuits reallocated per process.

The emerging field of quantum internet offers multiple applications, enabling quantum communication across diverse networks. However, the current entanglement networks exhibit complex processes, characterized by variable entanglement generation rates, limited quantum memory capacity, and susceptibility to decoherence rates. Addressing these issues, we propose a two-stage routing system that harnesses the power of reinforcement learning (RL). The first stage focuses on identifying the most efficient routes for quantum data transmission. The second stage concentrates on establishing these routes and improving how and when to apply entanglement swapping and purification. Our extensive evaluations across various network sizes and configurations reveal that our method not only sustains superior end-to-end route fidelity but also achieves significantly higher request success rates compared to traditional methods. These findings highlight the efficacy of our approach in managing the complex dynamics of quantum networks, ensuring robust and scalable quantum communication. Our method’s adaptability to changing network conditions and its proactive management of quantum resources make an important contribution to quantum network efficiency.

Federated learning (FL) allows devices to train a machine learning model collaboratively without compromising data privacy. In wireless networks, FL presents challenges due to limited resources and the unstable nature of transmission channels that can cause delays and errors that compromise the consistency of global model updates. Furthermore, efficient allocation of communication resources is crucial in Internet of Things (IoT) environments, where devices often have limited energy capacity. This work introduces a novel FL algorithm called DFed-w(_{text {Opt}}^{text {DP}}), designed for wireless networks within the IoT framework. This algorithm incorporates a device selection mechanism that evaluates the quality of device data distribution and connection quality with the aggregate server. By optimizing the power allocation for each device, DFed-w(_{text {Opt}}^{text {DP}}) minimizes overall energy consumption while enhancing the success rate of transmissions. The simulation results demonstrate that DFed-w(_{text {Opt}}^{text {DP}}) effectively operates with low transmission power while preserving the accuracy of the global model compared to other algorithms.

Spatial division multiplexing elastic optical networks (SDM-EONs) based on multicore fibers (MCFs) are a technology that can handle the Internet’s growing traffic demand. However, SDM-EONs present challenges in their implementation, such as the physical layer impairments (PLI) and the spectrum fragmentation. This paper proposes the fragmentation-aware and PLI-aware algorithm (FXAA) to solve the core and spectrum assignment problem in MCF-based SDM-EONs. The FXAA implements a low-cost PLI-aware mechanism to select lightpaths with low inter- and intra-core impairment incidence, ensuring the quality of transmission (QoT) of the network lightpaths. In addition, FXAA clusters the lightpaths with the same number of frequency slots to reduce spectrum fragmentation. The numerical results show that compared with the other nine algorithms proposed in the literature, FXAA achieves a gain of circuit blocking probability of at least 33.36%, a gain of bandwidth blocking probability of at least 17.99%, and an increase in spectral utilization of at least 1.08%.

5G vehicle-to-everything (V2X) connectivity plays a fundamental role in enabling advanced vehicular networks within intelligent transportation systems (ITS). However, challenges arising from limited resources, such as unreliable connections between vehicles and the substantial signaling overhead in centralized resource distribution methods, impede the efficiency of V2X communication systems, especially in safety-critical applications. This study critically explores the limitations of centralized resource management in 5G-V2X, focusing on issues of resource scarcity and allocation inefficiencies. In response to these challenges, our approach focuses on optimizing resource utilization within the constraints of limited resources. The article introduces innovative strategies to enhance V2X service satisfaction, emphasizing the efficient allocation of resources for different service classes. Simulations showcase the impact of our tailored approach on resource utilization and satisfaction rates, shedding light on potential improvements in scenarios with constrained resources.

Precision agriculture (PA) and plant disease detection (PDD) are essential for farm crops’ life quality and crop yield. Unfortunately, current PDD algorithms are trained and deployed with perfect plant images. This is impractical since PA sensor networks (PANs) transfer imperfect data due to wireless communication imperfections, such as channel estimation and noise, as well as hardware imperfections and noise. To capture the influence of channel imperfections and combat its effect, this work considers on- and/or offsite PDD implementation using plant image data transferred over multi-path imperfect PAN. Here, both traditional decode-and-forward (DF) data routing and channel effect considering machine learning data autoencoder multi-path routing are used for image data transmission. The multi-path DF data routing considers equal gain combining (EGC) and maximum ratio combining (MRC) techniques at the destination gateway for data decoding. In addition, a PDD deep learning algorithm is developed to predict whether or not a farm plant is diseased, using the noisy image data captured by the multi-path data routing PAN. From the PAN-PDD integrated system simulation, the proposed ML multi-path PAN-PDD algorithms (i.e., EGC and MRC) are compared to the ML single-path PAN-PDD algorithm and the traditional single-path PAN-PDD system. The simulation results showed that the multi-path approach performed fairly well over the other DF PAN-PDD systems. Incorporating the channel effects in designing an intelligent wireless data transfer solution/technique improves the communication system performance in PDD implementation.