Computer Communications最新文献

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.

Despite opening attractive perspectives, the concept of Software Defined Networking raises doubts about the performance and practical feasibility. To contradict these concerns, we propose a deployment-ready system aimed at proactive and periodic optimization of flow paths. The modular system consists of modules responsible for traffic prediction, static optimization, measurements, flow management, and validation of optimization results. To make the system efficient, we resolved several scientific issues and proposed novel and valuable solutions, for example methods for efficient proactive flow management and periodic re-optimization of routing policies. Simultaneously, to make the system production-ready and create a reliable research environment, we provide solutions to several technical obstacles.

We validate the proposed system with three network topologies, each with three load levels, following real-life traffic models. We consider Equal-Cost Multi-Path Routing as a baseline. The results indicate that the system allows network operators to handle more traffic (packet loss reduced by up to 30%), improve quality of service (less congested links resulted in even 2.5 times lower latency), and reduce operational expenses (energy consumption lowered by up to 10%).

A blockchain is a data structure consisting of a list of blocks containing transactions and maintained by a network of nodes in a decentralized manner. In permissionless blockchains, anyone can contribute to the decentralization and security of the transactions. With the advent of smart contracts, programs whose execution is replicated by all the nodes of the network, the blockchain can be deemed not only a reliable and auditable data repository, but also a secure and verifiable computational infrastructure. However, due to the aforementioned features, the throughput of most permissionless blockchains is low, and executing a smart contract can be expensive, depending on its computational complexity. To mitigate these issues, a popular research line studies the implementation of Layer 2 solutions, which consists of nodes that operate off-chain yet remaining tethered to the blockchain. Our literature analysis revealed that a majority of the research articles surveying Layer 2 technologies and solutions typically classify them on the basis of the Layer 2 operations they perform, as well as their ability to improve the processing capacity of the blockchain. In this paper, instead, we survey the methodologies that provide a secure binding between Layer 2 and the blockchain. We refer to these binding techniques as “proving schemes” which we classify as: data integrity proofs, validity proofs, and fraud proofs. For each proving scheme, we describe its intended purpose, the advantages it offers, the methodologies commonly used to connect the operations performed at Layer 2 with the blockchain, and the applications that benefit from such scheme. Finally, we discuss and compare them to give a general comprehension about how schemes can satisfy general requirements common to most Decentralized Applications.

The practice of people counting serves as an indispensable tool for meticulously monitoring crowd dynamics, enabling informed decision-making in critical situations, and optimizing the management of urban spaces, facilities, and services. Beyond its fundamental role in safety and security, tracking people’s flows has evolved into a necessity for diverse business applications and the effective administration of both outdoor and indoor urban environments. In the ongoing exploration of the study, emphasis is placed on employing a passive counting technique. This method leverages WiFi probe request messages emitted by smart devices to assess the number of devices, providing a reliable estimate of the number of people in a specific area. However, it is crucial to acknowledge the dynamic landscape of privacy regulations and the concerted efforts by leading smart-device manufacturers to fortify user privacy, as evidenced by the adoption of MAC address randomization. In response to these considerations, an enhanced iteration of the WiFi traffic generator has been introduced. This upgraded version is designed to generate realistic datasets with ground truth, aligning with the evolving privacy landscape. Additionally, leveraging a profound understanding of probe requests and the capabilities of the designed generator, a novel crowd monitoring solution that incorporates machine learning techniques, named ARGO, has been developed. This innovative approach effectively addresses challenges posed by randomized MAC addresses, incorporating Bloom filters to ensure a formal “deniability” that complies with stringent regulations, including the European GDPR (European Parliament, Council of the European Union, Regulation (EU), 2016). The proposed solution adeptly addresses the pivotal task of people counting by harnessing WiFi probe request messages. Significantly, it prioritizes users’ privacy, aligning with the foundational principles outlined in regulations such as the European GDPR.

Following a seismic event, tsunami early warning systems (TEWSs) try to provide precise forecasts of the maximum height of incoming waves at designated target points along the coast. This information is crucial to trigger early warnings in areas where the impact of tsunami waves is predicted to be dangerous (or potentially cause destruction), to help the management of the potential impact of a tsunami as well as reduce environmental destruction and losses of human lives. For such a reason, it is crucial that TEWSs produce predictions with short computation time while maintaining a high prediction accuracy. This paper presents a parallel machine learning approach, based on regression trees, to discover tsunami predictive models from simulation data. In order to achieve the results in a short time, the proposed approach relies on the parallelization of the most time consuming tasks and on incremental learning executions, in order to achieve higher performances in terms of execution time, efficiency and scalability. The experimental evaluation, performed on two real tsunami cases occurred in the Western and Eastern Mediterranean basin in 2003 and 2017, shows reasonable advantages in terms of scalability and execution time, which is an important benefit in a urgent-computing scenarios.