Computer Networks最新文献_第8页

MC-Det: Multi-channel representation fusion for malicious domain name detection MC-Det：多通道表示融合用于恶意域名检测

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110847

Yabo Wang , Ruizhi Xiao , Jiakun Sun , Shuyuan Jin

{"title":"MC-Det: Multi-channel representation fusion for malicious domain name detection","authors":"Yabo Wang , Ruizhi Xiao , Jiakun Sun , Shuyuan Jin","doi":"10.1016/j.comnet.2024.110847","DOIUrl":"10.1016/j.comnet.2024.110847","url":null,"abstract":"<div><div>As the essential fundamental infrastructure of the current network, the Domain Name System is widely abused by cyber attackers, malicious domain detection has become a crucial task in combating cyber crime. Most existing methods focus on local attributes, treating each domain name individually. Alternatively, they prioritize global associations among domain names, but ignore the attributes of the domains themselves, allowing malicious domain names to survive through sophisticated evasion techniques. In this paper, we propose MC-Det, a hybrid framework for detecting malicious domain names by fusing a Multi-channel representation of domain names. MC-Det first abstracts the domain name resolution process into three spatially independent information channels: Attribute space, which contains the intrinsic information in the domain name string itself, Constraint space, which involves the potential constraints imposed on the network activity behind the domain name, Topological space, which represents the actual usage and deployment of the domain name. Subsequently, it generates proper embedding representations of domain names for each channel. This novel Multi-channel representation provides a comprehensive understanding of domain name resolution process. Finally, a Multi-channel fusion strategy employing by attention mechanism is used to generate the final representation of domain names for the classifier, making MC-Det suitable for malicious domain name detection in different application scenarios. Experimental results demonstrate that MC-Det outperforms other state-of-the-art techniques, while only utilizing the resource information revealed in the domain name resolution phase.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110847"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Themis: A passive-active hybrid framework with in-network intelligence for lightweight failure localization Themis：具有网络内智能的被动-主动混合框架，用于轻量级故障定位

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110836

Jingyu Xiao , Qing Li , Dan Zhao , Xudong Zuo , Wenxin Tang , Yong Jiang

{"title":"Themis: A passive-active hybrid framework with in-network intelligence for lightweight failure localization","authors":"Jingyu Xiao , Qing Li , Dan Zhao , Xudong Zuo , Wenxin Tang , Yong Jiang","doi":"10.1016/j.comnet.2024.110836","DOIUrl":"10.1016/j.comnet.2024.110836","url":null,"abstract":"<div><div>The fast and efficient failure detection and localization is essential for stable network transmission. Unfortunately, existing schemes suffer from a few drawbacks such as significant resource consumption, lack of support for fast online failure localization, and limited applicable topologies. In this paper, we design Themis, a lightweight learning-based failure localization scheme for general networks. In the data plane, Themis achieves line-speed high performance failure detection using in-network classifiers and fine-grained traffic features. To reduce communication overhead, only coarse-grained traffic features are reported to the control plane for localization when a failure occurs. In the control plane, we propose a two-stage passive-active hybrid failure localization approach to accurately locate the failure without incurring excessive probing traffic. First, passive detection is conducted through the lightweight model XGBoost to infer a Potential Failure Link Set (PFLS). Then, active detection is done by only sending out probing packets to locations in the PFLS for precise failure localization. Comprehensive experiments demonstrate that Themis achieves ms-level failure localization with at least 95.63% accuracy, while saving 87.41% of bandwidth and 41.88% of hardware resource overhead on average compared with the state-of-the-art schemes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110836"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on indoor multi-floor positioning method based on LoRa 基于 LoRa 的室内多楼层定位方法研究

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110838

Honghong Chen, Jie Yang, Zhanjun Hao, Tian Qi, TingTing Liu

{"title":"Research on indoor multi-floor positioning method based on LoRa","authors":"Honghong Chen, Jie Yang, Zhanjun Hao, Tian Qi, TingTing Liu","doi":"10.1016/j.comnet.2024.110838","DOIUrl":"10.1016/j.comnet.2024.110838","url":null,"abstract":"<div><div>Existing floor localization methods are plagued by low accuracy, high algorithmic complexity, dense node deployment, susceptibility to environmental factors, and the inability to track trajectories. This paper introduces a localization method designed to address the challenges of multi-floor environments, leveraging LoRa technology. The approach involves deploying LoRa vertical positioning devices and establishing offline and threshold fingerprint databases. To enhance localization accuracy, it combines Time-of-Flight (TOF) ranging values (referred to as \"RANGE\" in this paper) with Received Signal Strength Indicator (RSSI) values, referred to as \"RSSI-RANGE\". Subsequently, a multi-floor determination is achieved using the RSSI-RANGE floor determination algorithm and a range-based signal source autonomous switching mechanism. The fingerprinting technique is then employed for trajectory recognition. Comprehensive vertical information is obtained by combining floor determination and trajectory award. Gaussian filtering is utilized for fingerprint preprocessing to eliminate gross errors. The particle swarm optimization algorithm is employed to fine-tune the hyperparameters of the random forest algorithm following noise reduction. Using the random forest algorithm, optimal RSSI-RANGE values are derived, and the offline fingerprint database is established by applying Kriging interpolation. Localization is then achieved in the concluding online recognition phase. Empirical findings illustrate the system's high floor accuracy rate of 97.8%, achieving high determination accuracy and comprehensive floor localization when combined with trajectory recognition.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110838"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge-device collaborative computing for multi-view classification 多视角分类的边缘设备协同计算

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110823

Marco Palena , Tania Cerquitelli , Carla Fabiana Chiasserini

{"title":"Edge-device collaborative computing for multi-view classification","authors":"Marco Palena , Tania Cerquitelli , Carla Fabiana Chiasserini","doi":"10.1016/j.comnet.2024.110823","DOIUrl":"10.1016/j.comnet.2024.110823","url":null,"abstract":"<div><div>Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore <em>collaborative inference</em> at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce <em>selective schemes</em> that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110823"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated trajectory optimization for UAV-enabled wireless powered MEC system with joint energy consumption and AoI minimization 针对无人机无线供电 MEC 系统的综合轨迹优化，实现能耗和 AoI 的联合最小化

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-03 DOI: 10.1016/j.comnet.2024.110842

Yuchen Li , Hongwei Ding , Zhijun Yang , Bo Li , Zhuguan Liang

{"title":"Integrated trajectory optimization for UAV-enabled wireless powered MEC system with joint energy consumption and AoI minimization","authors":"Yuchen Li , Hongwei Ding , Zhijun Yang , Bo Li , Zhuguan Liang","doi":"10.1016/j.comnet.2024.110842","DOIUrl":"10.1016/j.comnet.2024.110842","url":null,"abstract":"<div><div>This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered mobile edge computing (MEC) system, where a UAV, equipped with RF Chains and MEC servers, can sustainably provide wireless energy for charging Internet of Things (IoT) devices and executing computing tasks from these devices while hovering at designated hover points. Our goal is to minimize the weighted sum of energy consumption and Age of information (AoI) in this system, which depended on the UAV’s hovering time at designated points and its flying time. To achieve this, we jointly optimize the deployment of hover points and the visiting order of these points by the UAV. It is NP hard and mixed-integer non-convex which is difficult to solve by traditional methods. To tackle this problem, we present a trajectory optimization algorithm for joint energy consumption and AoI (TOJEA), which consists of two phases. In the first phase, an Equilibrium Optimizer (EO) algorithm with a variable individual size via its coding and updating strategies, in which each particle (individual) with its concentration (position) represents a target solution i.e. the whole deployment of hover points, is proposed to optimize the number and locations of hover points. Based on the deployment of hover points, a low-complexity greedy algorithm is adopted in the second stage to generate the optimal visiting order for the UAV. Experimental results demonstrate that TOJEA outperforms other algorithms on ten instances with up to 400 IoT devices.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110842"},"PeriodicalIF":4.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure and Efficient Authentication using Linkage for permissionless Bitcoin network 在无权限比特币网络中使用链接进行安全高效的身份验证

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-02 DOI: 10.1016/j.comnet.2024.110840

Hsiang-Jen Hong , Sang-Yoon Chang , Wenjun Fan , Simeon Wuthier , Xiaobo Zhou

{"title":"Secure and Efficient Authentication using Linkage for permissionless Bitcoin network","authors":"Hsiang-Jen Hong , Sang-Yoon Chang , Wenjun Fan , Simeon Wuthier , Xiaobo Zhou","doi":"10.1016/j.comnet.2024.110840","DOIUrl":"10.1016/j.comnet.2024.110840","url":null,"abstract":"<div><div>The cryptocurrency’s permissionless and large-scale broadcasting requirements prohibit the traditional authentication implementation on the blockchain’s underlying peer-to-peer (P2P) networking. Thus, blockchain networking implementations remain vulnerable to networking integrity threats such as spoofing or hijacking. We design Secure and Efficient Authentication using Linkage (SEAL) to build connection security for permissionless Bitcoin networking. SEAL uses the linkage between the packets for a symmetric operation, in contrast to the traditional authentication approach relying on identity-credential-based trust. To make it appropriate for cryptocurrency networking, SEAL utilizes the packet header, protects the end-to-end connection, and separates the online process and the offline process so that the real-time overhead is minimal for greater efficiency and practicality. We implement SEAL on a functioning Bitcoin node and demonstrate that SEAL operates efficiently with minimal overhead. Specifically, it reduces the hash rate by only 1.3% compared to an unsecured node. Additionally, we use a network simulator to emulate the Bitcoin Mainnet and analyze SEAL’s impact on block propagation delay. SEAL yields 2.04 times smaller delay and 1.25 times smaller delay in block propagation than HMAC and ChaCha20-Poly1305, respectively. The key advantage of SEAL is that it requires fewer hash computations and simpler mixing operations, resulting in significantly lower computational overhead compared to traditional authentication schemes based on message authentication codes (MACs).</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110840"},"PeriodicalIF":4.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G DT-Block：面向 6G 安全高效通信的自适应垂直联合强化学习方案

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-01 DOI: 10.1016/j.comnet.2024.110841

Ihsan H. Abdulqadder , Israa T. Aziz , Deqing Zou

{"title":"DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G","authors":"Ihsan H. Abdulqadder , Israa T. Aziz , Deqing Zou","doi":"10.1016/j.comnet.2024.110841","DOIUrl":"10.1016/j.comnet.2024.110841","url":null,"abstract":"<div><div>The necessities of security and data sharing have focused on federated learning because of using decentralized data sources. The existing works used federated learning for security, however, it still faces many challenges such as poor security and privacy, computational complexity, etc. In this research, we propose adaptive vertical federated learning using a reinforcement learning approach and blockchain. The proposed work includes three phases: user registration and authentication, machine learning-based client selection, and adaptive secure federated learning. Initially, all the users register their credentials to the cognitive agent, which generates a private key, public key, and random number using a Chaotic Isogenic Post Quantum Cryptography (CIPQC) algorithm. Second, optimal clients are selected for participating in federated learning which improves learning rate and reduces complexity. Here, optimal clients are selected by the Enhanced Multilayer Feed Forward Neural Network (EMFFN) algorithm by considering CSI, RSSI, bandwidth, energy, communication efficiency, and statistical efficiency. Finally, adaptive secure federated learning is performed by the Distributed Distributional Deep Deterministic Policy Gradient (D<span><math><msup><mrow></mrow><mrow><mn>4</mn></mrow></msup></math></span>PG) algorithm, where the local models are adaptively used by the private strategy based on its sensitivity. The aggregated global models are stored in DT-block (dendrimer tree-based blockchain) which stores the data in a dendrimer tree structure for increasing scalability and reducing search time during data retrieval. The simulation of this research is conducted by NS-3.26 network simulator and the performance of the proposed DT-Block model is estimated based on various performance metrics such as accuracy, delay, loss, f1-score, and security strength this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110841"},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks 在 CR-IoT 网络中利用专用能量发射器实现高效多通道能量采集

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-01 DOI: 10.1016/j.comnet.2024.110834

Hisham M. Almasaeid

{"title":"Efficient multichannel energy harvesting with dedicated energy transmitters in CR-IoT networks","authors":"Hisham M. Almasaeid","doi":"10.1016/j.comnet.2024.110834","DOIUrl":"10.1016/j.comnet.2024.110834","url":null,"abstract":"<div><div>Radio Frequency (RF) energy harvesting is strongly believed to be a sustainable solution to the power depletion problem in battery powered IoT devices. In addition to harvesting energy from ambient RF signals, the use of dedicated energy transmitters (ETs) that transmit energy to nearby IoT devices via RF signals has recently been proposed. In this paper, we study the problem of designing an energy harvesting policy for a group of cognitive radio-enabled IoT (CR-IoT) devices served by a number of ETs to maximize the minimum of their charging rates. With the help of cognitive radios, a CR-IoT node is capable of changing its frequency channel of operation allowing for multi-channel energy harvesting. Frequency channels are assumed to be opportunistically accessible depending on the activity of wireless users that are licensed to use those channels. The problem entails the design of the ET’s transmission policy (to what CR-IoT device, and over what channel) and the design of an ambient harvesting policy for every CR-IoT device (when it is not served by ETs). The problem is formulated as a mixed integer linear program (MILP). The objective is to maximize a lower bound on the total harvested energy in a given time frame per CR-IoT node. This optimization is subject to scheduling, total energy budget, and maximum transmit power constraints. Given the intractability of MILP formulations, a sub-optimal algorithm is proposed. Extensive experimentation is carried out to assess the effectiveness of the proposed sub-optimal algorithm by comparing it to the MILP’s solution obtained using IBM CPLEX solver with a limit on the execution time. We also combine our sub-optimal algorithm withe the CPLEX solver to produce a new two-stages algorithm that improves the original one by around 47%. Finally, we investigate the effect of multiple parameters including number of ETs, number of channels, and channel availability probability on the minimum charging rate.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110834"},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cyber threat indicators extraction based on contextual knowledge prompt 基于上下文知识提示的网络威胁指标提取

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-01 DOI: 10.1016/j.comnet.2024.110839

Hailiang Tang , Dawei Lin , Wanyu Li , Wenxiao Zhang , Jun Zhao

{"title":"Cyber threat indicators extraction based on contextual knowledge prompt","authors":"Hailiang Tang , Dawei Lin , Wanyu Li , Wenxiao Zhang , Jun Zhao","doi":"10.1016/j.comnet.2024.110839","DOIUrl":"10.1016/j.comnet.2024.110839","url":null,"abstract":"<div><div>Extracting Indicators of Compromise (IOC) from security-related social data (e.g., security blogs, hacker forums) is crucial for predicting cyber risks and mitigating cyber attacks proactively. However, existing IOC extraction approaches suffer from two serious limitations. First, they fail to learn the multiculti-granular and fine-grained IOC features, resulting in high false positives. Second, current methods cannot incorporate symbolic rules and contextual knowledge, resulting in poor interpretability. In this paper, we propose AIIOC, an <u>A</u>ccurate and <u>I</u>nterpretable <u>I</u> <u>O</u> <u>C</u> extraction model based on contextual knowledge prompts. Particularly, AIIOC first proposes a multi-granularity attention mechanism to learn fine-grained IOC features and boost the accuracy of IOC identification. Additionally, AIIOC designs a novel sequence labeling method that integrates symbolic rules and contextual knowledge prompts, which can encode symbolic rules and contextual semantics of IOC in trainable recurrent neural networks to improve both accuracy and interpretability. Experimental results on two real-world datasets verify that AIIOC outperforms state-of-the-art methods and showcases promising interpretability by incorporating symbolic rules and contextual knowledge prompts.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110839"},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

aCroSS: AI-driven cross-layer adaptive streaming for short video applications aCroSS：面向短视频应用的人工智能驱动跨层自适应流媒体技术

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-09-30 DOI: 10.1016/j.comnet.2024.110832

Bin Dai, Hetao Li, Yifan Wang

{"title":"aCroSS: AI-driven cross-layer adaptive streaming for short video applications","authors":"Bin Dai, Hetao Li, Yifan Wang","doi":"10.1016/j.comnet.2024.110832","DOIUrl":"10.1016/j.comnet.2024.110832","url":null,"abstract":"<div><div>As short video applications gain popularity, researchers are exploring ways to enhance Quality of Experience (QoE) for short videos while maximizing network bandwidth efficiency. Despite the growing interest, existing Adaptive Bitrate (ABR) algorithms primarily concentrate on content prefetching strategies and often overlook the dynamic interaction between network congestion control and ABR. This interaction is especially critical for short video streaming, where network conditions can fluctuate rapidly, and user expectations for seamless playback are high. To address these challenges, we propose aCroSS, an AI-driven framework for adaptive short video streaming that jointly optimizes both the application and transport layers to enhance QoE and bandwidth utilization. The aCroSS algorithm leverages advanced machine learning techniques to adapt in real time to fluctuating network conditions and dynamic user behaviors, delivering a more robust and responsive streaming experience. Our simulation results demonstrate that aCroSS consistently outperforms existing baseline algorithms, achieving more than a 10% improvement in utility scores across various network trace datasets. This highlights the effectiveness of aCroSS in delivering superior performance in diverse streaming environments.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110832"},"PeriodicalIF":4.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0