Computer Networks最新文献

In the domain of Time-Sensitive Networking (TSN), the quest for ultra-reliable low-latency communication is paramount. Current scheduling strategies, which hinge on strict isolation to ensure low latency and jitter, confront the challenges of high overhead in worst-case latency evaluation and consequent limitations in network flow capacity. This paper introduces an innovative framework that transcends traditional isolation constraints, thereby expanding the solution space and augmenting network schedulability. At the heart of this framework lies a novel latency jitter analysis method that assesses the viability of non-isolation scenarios with constant time complexity. This method underpins a heuristic scheduling algorithm that not only boasts the smallest time complexity among existing heuristics but also significantly increases the number of scheduled flows. Complementing this, we integrate a discrete time reference approach to hasten time-intensive scheduling operations, achieving an optimal balance between schedulability and runtime efficiency. The framework further incorporates a workload-shifting technique to enhance online scheduling responsiveness. It adeptly manages the variability in scheduling times caused by disharmonious flow periods, further bolstering the framework’s robustness. Experimental validations demonstrate that our framework can increase the scheduled flows up to 269%. It reduces scheduling runtime by up to 98.44% for medium-scale networks while maintaining a flat runtime growth curve, ensuring predictable performance in online scheduling scenarios.

The future releases of 3rd Generation Partnership Project (3GPP) specifications, that is, beyond release 18, will consider the possibility to localize the User Equipment (UE) at network-side, eventually using satellite constellations of the integrated Terrestrial-Non-Terrestrial networks (T-NTN). Satellite network-centric localization schemes can be categorized into single- and multi-satellite localization using spatio-temporal measurements or instantaneous spatial diversity, respectively Direct channel measurements such as Doppler, Received Signal Strength (RSS), Round Trip Time (RTT) and Angle-of-Arrival (AoA) or differential measurements such as Time-Difference-of-Arrival (TDoA) and Frequency-Difference-of-Arrival (FDoA) have been considered in the literature to aid the localization operation. This paper focuses on the applicability of an RTT approach, which has some advantages with respect to the other approaches in case of satellite network-centric localization in the integrated T-NTN. The paper shows some preliminary results of the proposed RTT approach. Finally, challenges and research trends of this novel research field have been highlighted.

The growing connected car market requires mobile network operators (MNOs) to rethink their network architecture to deliver ultra-reliable low-latency communications. In response, Multi-Access Edge Computing (MEC) has emerged as a solution, enabling the deployment of computing resources at the network edge. For MNOs to tap into the potential benefits of MEC, they need to transform their networks accordingly. Consequently, the primary objective of this study is to design a realistic MEC architecture and corresponding optimal deployment strategy – deciding on the placement and configuration of computing resources – as opposed to prior studies focusing on MEC run-time management and orchestration (e.g., service placement, computation offloading, and user allocation). To cater to the heterogeneous demands of vehicular services, we propose a multi-tier MEC architecture aligned with 5G and Beyond-5G radio access network deployments. Therefore, we frame MEC deployment as an optimization problem within this architecture, assuming 3 MEC tiers. Our data-driven evaluation, grounded in realistic assumptions about network architecture, usage, latency, and cost models, relies on datasets from a major MNO in the UK. We show the benefits of adopting a 3-tier MEC architecture over single-tier (centralized or distributed) architectures for heterogeneous vehicular services, in terms of deployment cost, energy consumption, and robustness.

The emergence of the Internet of Things (IoT), Industry 5.0 applications and associated services have caused a powerful transition in the cyber threat landscape. As a result, organisations require new ways to proactively manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing. However, in many cases, CTI contains sensitive information that has the potential to leak valuable information or cause reputational damage to the sharing organisation. While a number of existing CTI sharing approaches have utilised blockchain to facilitate privacy, it can be highlighted that a comprehensive approach that enables dynamic trust-based decision-making, facilitates decentralised trust evaluation and provides CTI producers with highly granular sharing of CTI is lacking. Subsequently, in this paper, we propose a blockchain-based CTI sharing framework, called Priv-Share, as a promising solution towards this challenge. In particular, we highlight that the integration of differential sharing, trustless delegation, democratic group managers and incentives as part of Priv-Share ensures that it can satisfy these criteria. The results of an analytical evaluation of the proposed framework using both queuing and game theory demonstrate its ability to provide scalable CTI sharing in a trustless manner. Moreover, a quantitative evaluation of an Ethereum proof-of-concept prototype demonstrates that applying the proposed framework within real-world contexts is feasible.

Coflow scheduling is crucial for enhancing application-level communication performance in data-parallel clusters. While schemes like Varys can potentially achieve optimal performance, their dependence on a prior information about coflows poses practical challenges. Existing non-clairvoyant solutions, such as Aalo, approximate the classical online Shortest-Job-First (SJF) scheduling but fail to identify bottleneck flows in coflows. Consequently, they often allocate excessive bandwidth to non-bottleneck flows, leading to bandwidth wastage and reduced overall performance. In this paper, we introduce MSDQ, a coflow scheduling mechanism that operates without prior knowledge, utilizing multi-scheduling dual-priority queues, and using width estimates. This method adjusts coflow queue priorities and scheduling sequences based on the coflow’s width and the volume of data transmitted. By reallocating unused network bandwidth at multiple points during the scheduling process, MSDQ maximizes the bandwidth usage and significantly reduces the average coflow completion time. Our evaluation, using a publicly available production cluster trace from Facebook, demonstrates that MSDQ reduces the average coflow completion time by $1.42\times$ compared to Aalo.

Mobile communications have followed a progression model detailed by the Gartner hype cycle, from a proof-of-concept to widespread productivity. As fifth-generation (5G) mobile networks are being deployed, their potential and constraints are becoming more evident. Although 5G boasts a flexible architecture, enhanced bandwidth, and data throughput, it still grapples with infrastructure challenges, security vulnerabilities, coverage issues, and limitations in fully enabling the Internet of Everything (IoE). As the world experiences exponential growth in Internet users and digitized devices, relying solely on evolutionary technologies seems inadequate. Recognizing this, global entities such as the 3rd Generation Partnership Project (3GPP) are laying the groundwork for 5G Advanced, a precursor to 6G. This article argues against a mere evolutionary leap from 5G to 6G. We propose a radical shift towards a disruptive 6G architecture (D6G) that harnesses the power of smart contracts, decentralized Artificial Intelligence (AI), and digital twins. This novel design offers a software-centric, AI-driven, and digital market-based redefinition of mobile technologies. As a result of an integrated collaboration among researchers from the Brazil 6G Project, this work identifies and synthesizes fifty-one key emerging enablers for 6G, devising a unique and holistic integration framework. Emphasizing flexibility, D6G promotes a digital market environment, allowing seamless resource sharing and solving several of 5G’s current challenges. This article comprehensively explores these enablers, presenting a groundbreaking approach to 6G’s design and implementation and setting the foundation for a more adaptable, autonomous, digitally monitored, and AI-driven mobile communication landscape. Finally, we developed a queuing theory model to evaluate the D6G architecture. Results show that the worst-case delay for deploying a smart contract in a 6G domain was 23 s. Furthermore, under high transaction rates of ten transactions per minute, the delay for contracting a 6G slice was estimated at 53.7 s, demonstrating the architecture’s capability to handle high transaction volumes efficiently.

Mobile Edge Computing (MEC) is a key technology that emerged to address the increasing computational demands and communication requirements of vehicular networks. It is a form of edge computing that brings cloud computing capabilities closer to end-users, specifically within the context of vehicular networks, which are part of the broader Internet of Vehicles (IoV) ecosystem. However, the dynamic nature of traffic flows in MEC in 5G/6G vehicular networks poses challenges for accurate prediction and resource allocation when aiming to provide edge service for mobile vehicles. In this paper, we present a novel approach to predict the traffic flow of MEC in 5G/6G vehicular networks using graph-based learning. In our framework, MEC servers in vehicular networks are construed as nodes to construct a dynamic similarity graph and a dynamic transition graph over a duration of multiple days. We utilize Graph Attention Networks (GAT) to learn and fuse the node embeddings of these dynamic graphs. A transformer model is subsequently employed to predict the vehicle frequency accessing the edge computing services for the next day. Our experimental results have shown that the model achieves high accuracy in predicting edge service access volumes with low error metrics.

With the rapid development of new networks such as 5 G/6 G, the Internet of Vehicles has been given features such as hyper-connectivity and hyper-intelligence, promoting the implementation of new scenarios for autonomous vehicles. However, on the Internet of vehicles, vehicles use many cameras, radars and other sensors to sense the environment and execute instructions, facing security issues such as road data tampering and hijacking. Consequently, this paper presents a data tampering detection and recovery scheme based on multi-branch target extraction. Specifically, for road images collected by sensors, this paper presents a target extraction method based on multi-branch spatial feature pyramid blocks to obtain road salient targets. Then, a tamper detection and recovery algorithm based on interval mapping is presented. Once the image road target is tampered with, this method can quickly detect the tampering traces and restore them to achieve the authenticity and integrity of the road image on the Internet of Vehicles. The availability of the proposed scheme is verified through comparative experiments, and the performance is improved satisfactorily compared with other works.

Log data is crucial for security threat detection and audit analysis. However, traditional log systems are susceptible to tampering, posing a significant security risk to information systems. Although blockchain technology has been introduced to enhance tamper resistance, existing blockchain-based log systems still suffer from storage and query efficiency issues. In this paper, we propose a novel secure and efficient log storage and query framework that combines on-chain and off-chain collaboration. An inverted index table is constructed by extracting keywords from logs, which are stored on the blockchain as on-chain data, while the logs themselves are maintained as off-chain data. this approach facilitates the rapid retrieval of specific keywords and ensures the immutability of the logs. furthermore, we propose a secure and efficient log query method featuring a smart contract designed to automatically handle requests from legitimate log queriers. we also design a data structure based on merkle adaptive radix tree (MART) and merkle B+ tree (MBT) to store index entries, thereby achieving efficient log retrieval. we provide formal security proofs and comprehensively evaluate the proposed framework’s performance experimentally. results demonstrate that MBT and MART reduce average query times by 20.09% and 51% respectively, compared to the state-of-the-art schemes.

Social participatory-sensing, as an emerging research field, is extremely challenging to recruit trustworthy and active high-value users in a sparse user context. And the richness and closeness of users' social relationships provide new research ideas. Therefore, a participatory sensing user recruitment method based on dynamic social network role detection (DSRD) is proposed, in which firstly, multiple identity information of users is mined based on the decomposition of their overlapping social relationships to filter out high-quality sensing users. Secondly, based on role-oriented network representation learning, it models users' role information and establishes a role hierarchy model to evaluate users' social functions and role values. Finally, the concept of temporal social centrality is proposed for the first time for integrating users' social and network structural features to assess the overall value of users and ensure the coverage of task assignments under a sparse user pool. Experimental results on the open datasets Gowalla and Brightkite show that under the constraints of cost budget and number of users, the proposed user recruitment framework DSRD effectively improves task coverage with less time overhead compared to the baseline algorithm.