Computer Communications最新文献

Considering imperfect successive interference cancellation (SIC) for non-orthogonal multiple access (NOMA) communications, this work studies the cooperative reconfigurable intelligent surface (RIS)- and relay-assisted system under Nakagami-m fading. We focus on the performance comparison for such cooperative schemes, under different channel conditions and system parameters. In addition, we analyze the minimum required RIS elements number of the RIS-assisted scheme to achieve the same performance of the relay-assisted scheme with given signal-to-noise ratio (SNR). The cases of the optimal continuous phase shift and discrete phase shift designs of RIS are also discussed. We make a comparison between them, aiming to study the impact of the residual phase errors on performance. Specially, we compare the active RIS and passive RIS with the same system power constraint. Simulation results demonstrating the reliability of the analysis, validate that the relay-assisted scheme is superior to that of RIS-assisted one when the RIS elements number is small and transmitted power is lower. The results also confirm that the deployment of RIS should consider the actual situation of the application scenario.

Traditional techniques for edge computing resource scheduling may result in large amounts of wasted server resources and energy consumption; thus, exploring new approaches to achieve higher resource and energy efficiency is a new challenge. Deep reinforcement learning (DRL) offers a promising solution by balancing resource utilization, latency, and energy optimization. However, current methods often focus solely on energy optimization for offloading and computing tasks, neglecting the impact of server numbers and resource operation status on energy efficiency and load balancing. On the other hand, prioritizing latency optimization may result in resource imbalance and increased energy waste. To address these challenges, we propose a novel energy optimization method coupled with a load balancing strategy. Our approach aims to minimize overall energy consumption and achieve server load balancing under latency constraints. This is achieved by controlling the number of active servers and individual server load states through a two stage DRL-based energy and resource optimization algorithm. Experimental results demonstrate that our scheme can save an average of 19.84% energy compared to mainstream reinforcement learning methods and 49.60% and 45.33% compared to Round Robin (RR) and random scheduling, respectively. Additionally, our method is optimized for reward value, load balancing, runtime, and anti-interference capability.

In the 5G and beyond era, multipath transport protocols, including MPQUIC, are necessary in various use cases. In MPQUIC, one of the most critical issues is efficiently scheduling the upcoming transmission packets on several paths considering path dynamicity. To this end, this paper introduces FQ-SAT - a novel Fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization, including download time, in heterogeneous wireless networks. Different from previous works, FQ-SAT combines Q-learning and Fuzzy logic in an MPQUIC scheduler to determine optimal transmission on heterogeneous paths. FQ-SAT leverages the self-learning ability of reinforcement learning (i.e., in a Q-learning model) to deal with heterogeneity. Moreover, FQ-SAT facilitates Fuzzy logic to dynamically adjust the proposed Q-learning model’s hyper-parameters along with the networks’ rapid changes. We evaluate FQ-SAT extensively in various scenarios in both simulated and actual networks. The results show that FQ-SAT reduces the single-file download time by 3.2%–13.5% in simulation and by 4.1%–13.8% in actual network, reduces the download time of all resources up to 20.4% in web browsing evaluation, and reaches percentage of on-time segments up to 97.5% in video streaming, compared to state-of-the-art MPQUIC schedulers.

The Byzantine Fault Tolerance (BFT) consensus protocols are widely used in consortium blockchain to ensure data consistency. However, BFT protocols are generally static which means that the dynamic joining and exiting of nodes will lead to the reconfiguration of the consortium blockchain system. Moreover, most BFT protocols cannot support the clearing operation of slow, crashed, or faulty nodes, which limits the application of consortium blockchain. In order to solve these problems, this paper proposes a new Dynamic Scalable BFT (D-SBFT) protocol. D-SBFT optimizes SBFT by using Distributed Key Generation (DKG) technology and BLS aggregate signature scheme. On the basis of SBFT, we add Join, Exit, and Clear algorithms. Among them, Join and Exit algorithms enable nodes to actively join and exit the consortium blockchain more flexibly. Clear can remove slow, crashed or faulty nodes from the consortium blockchain. Experimental results show that our D-SBFT protocol can efficiently implement node dynamic change while exhibiting good performance in consensus process.

This study introduces the innovative concepts of the cooperation between unmanned aerial vehicles (UAVs) and automated guided vehicles (AGVs) in remote toxic gas sensing and alarming schemes in a smart factory. Initially, the UAV is dispatched in different directions to detect toxic gas leakage on the fly in the smart factory premises. However, due to the UAVs’ concern about smokeless and high-density gas detection capabilities, AGVs are proposed to cooperate with UAVs in the smart factory, especially in the basement areas. Because of their limited computational power, UAVs and AGVs securely transfer sensor data to a nearby multi-access edge computing (MEC) server for processing. A hybrid cryptographic technique and unique data authentication mechanisms are exploited to ensure security while transmitting the data in this proposed scheme. Subsequently, the MEC server automatically triggers an emergency alarm during toxic gas leakage to alert all the employees inside the boundaries of the smart factory. The implementation results exhibit that the proposed scheme can successfully sense toxic gas leakage using UAVs and AGVs, securely transfer the data to the MEC server to process, and enhance the overall quality of service compared with the other existing literature. Finally, the outcome analysis demonstrates that the proposed scheme is more worthwhile and has distinctive features than other literary works.

With rapid development of network communications, the performance requirements of applications are getting more differentiated. Many applications like high-definition video transfer require high throughput but do tolerate occasional packet losses. Traditional generic transport protocols however, only provide inflexible data transfer guarantees (TCP (Transmission Control Protocol) offers full reliability guarantees and UDP (User Datagram Protocol) offers no guarantees). Moreover, TCP pays a significant price to ensure a full reliability guarantee over lossy wireless communications environment like 5G millimeter wave (mmWave) communications. While existing, “partially” reliable transport protocols are either specifically designed for certain applications or need router’s support. In this paper, we design a new generic Differentiated Reliable Transport Protocol (DRTP), aiming to provide a differentiated and deterministic reliability guarantee for upper layer applications while maximizing the throughput under the constraint of guaranteeing a required reliability of data transfer. DRTP is a generic and pure end-to-end partially reliable transport protocol, and as such is easy to deploy regardless of the application in use and with no need for router’s support. The performance of DRTP is evaluated under various network conditions using extensive NS-3 (Network Simulator) simulations and practical experiments over the mmWave communications environment. The results show much higher throughput compared to typical transport protocols while guaranteeing the required transfer reliability.

Digital Twins (DTs), which are paired to Internet of Things (IoT) devices to represent them and augment their capabilities, are gaining ground as a promising technology to enable a wide variety of applications in the sixth-generation (6G) ecosystem, ranging from autonomous driving to extended reality and metaverse. In particular, “social” IoT (SIoT) devices, which are devices capable to establish social relationships with other devices, can be coupled with their virtual counterparts, i.e., social DTS (SDTs), to improve service discovery enabled by browsing the social network of friend devices. However, the mobility of SIoT devices (e.g., smartphones, wearables, vehicular on board units, etc.) may require frequent changes in the corresponding SDT placement in the edge domain to maintain a low latency between the physical device and its digital replica. Triggering SDT relocation at the right time is a critical task, because an incorrect choice could lead to either increased delays or a waste of network resources. This work proposes a learning-powered social-aware orchestration that predicts the mobility of SIoT devices to make more judicious migration decisions and efficiently move the paired SDTs accordingly, while ensuring the minimization of both intra-twin and inter-twin communication latencies. Different machine learning (ML) and deep learning (DL) algorithms are used for SIoT device mobility prediction and compared in terms of a wide set of meaningful metrics in order to identify the model that achieves the best trade-off between prediction accuracy and inference times under different scenarios. Simulation results showcase the improvements of the proposal in terms of reduced network overhead (by up to a factor of 3) and intra-twin and inter-twin communication latency (by up to 10%) compared to a more traditional solution, which activates the relocation of the DTs at fixed time intervals following periodic optimizations.

Due to the emerging advances in connected and autonomous vehicles, today’s in-vehicle networks, unlike traditional networks, are not only internally connected but externally as well, exposing the vehicle to the outside world and making it more vulnerable to cyber-security threats. Monitoring the in-vehicle network, thus, becomes one of the essential and crucial tasks to be implemented in vehicles. However, the closed-in nature of the vehicle’s components hinders the global monitoring of the in-vehicle network, leading to incomplete measurements, which may result in undetected failures. One solution to this is to use network tomography. Nevertheless, applying network tomography in in-vehicle networks is not a trivial task. Mainly because it requires that the in-vehicle network topology should be identifiable. To this end, we propose in this work an identifiable in-vehicle network topology that enables overall monitoring of the network using network tomography. The new topology is proposed based on extensive analysis to ensure full identifiability under the constraint that only edge nodes can monitor the network, which is the case for in-vehicle networks where internal nodes are not directly accessible. We propose two main algorithms to transform existing in-vehicle network topologies. The first algorithm applies to an existing topology which can be transformed into full identifiability by adding extra nodes/links. Evaluation results show the effectiveness of the proposed transformation algorithms with a maximum added weight of only 3% of the original weight. Furthermore, a new optimisation algorithm is also proposed to minimise the topology weight whilst maintaining the full identifiability by redesigning a new topology. With this algorithm, the results show that the total weight can be reduced by 6%. In addition, compared with the existing approaches, monitoring the in-vehicle networks with the proposed approach can achieve better monitoring overhead and a 100% identifiability ratio.

Virtual Reality (VR) streaming enables end-users to seamlessly immerse themselves in interactive virtual environments using even low-end devices. However, the quality of the VR experience heavily relies on Wireless Fidelity (Wi-Fi) performance, since it serves as the last hop in the network chain. Our study delves into the intricate interplay between Wi-Fi and VR traffic, drawing upon empirical data and leveraging a Wi-Fi simulator. In this work, we further evaluate Wi-Fi’s suitability for VR streaming in terms of the Quality of Service (QoS) it provides. In particular, we employ Unity Render Streaming to remotely stream real-time VR gaming content over Wi-Fi 6 using Web Real-Time Communication (WebRTC), considering a server physically located at the network’s edge, near the end user. Our findings demonstrate the system’s sustained network performance, showcasing minimal round-trip time (RTT) and jitter at 60 and 90 frames per second (fps). In addition, we uncover the characteristics and patterns of the generated traffic streams, unveiling a distinctive video transmission approach inherent to WebRTC-based services: the systematic packetization of video frames (VFs) and their transmission in discrete batches at regular intervals, regardless of the targeted frame rate. This interval-based transmission strategy maintains consistent video packet delays across video frame rates but leads to increased Wi-Fi airtime consumption. Our results demonstrate that shortening the interval between batches is advantageous, as it enhances Wi-Fi efficiency and reduces delays in delivering complete frames.

Intelligent Reflecting Surfaces (IRS), software-controlled metasurfaces, have emerged as an upcoming sixth-generation (6G) wireless communication technology. IRS intelligently manipulates and optimizes signal propagation using a large-scale array of intelligent elements, enhancing signal coverage, increasing capacity, mitigating path loss, and combating multipath fading This work provides a new energy-efficiency model for multi-IRS-assisted multi-cell non-orthogonal multiple access (NOMA) vehicular to infrastructure communication networks. The objective is the joint optimization of the total power budget at the roadside unit (RSU), NOMA power allocation for the user equipment, and designing phase shifts for IRS in each cell to maximize the achievable energy efficiency of the system. Due to non-convexity, the original non-convex problem is first decoupled and transformed using block coordinate descent and successive convex approximation methods. Then, an efficient solution is achieved using Gradient-based and interior-point methods. We also consider two benchmark schemes: (1) NOMA power optimization at RSU with random phase shift design at IRS and (2) orthogonal multiple access power allocation with optimal phase shift design at IRS. Numerical results show the superiority of the proposed solution compared to the benchmark schemes. The proposed solution outperforms the benchmarks, demonstrating a 59.57% and 151.21% improvement over the NOMA and orthogonal schemes, respectively, at $p_{ct}=2$ dBm. Additionally, it shows up to a 10.43% better performance than OMA at 10 IRS elements.