Computer Communications最新文献

{"title":"Deadline-constrained routing based on power-law and exponentially distributed contacts in DTNs","authors":"Tuan Le","doi":"10.1016/j.comcom.2024.108038","DOIUrl":"10.1016/j.comcom.2024.108038","url":null,"abstract":"<div><div>During a large-scale disaster, there is a severe destruction to physical infrastructures such as telecommunication and power lines, which result in the disruption of communication, making timely emergency response challenging. Since Delay Tolerant Networks (DTNs) are infrastructure-less, they tolerate physical destruction and thus can serve as an emergency response network during a disaster scenario. To be effective, DTNs need a routing protocol that maximizes the number of messages delivered within deadline. One obvious approach is to broadcast messages everywhere. However, this approach is impractical as DTNs are resource-constrained. In this work, we propose a cost-effective routing protocol based on the expected delivery delay that optimizes the number of messages delivered within deadline with a significantly low network overhead. Simulations using real-life mobility traces show that with our scheme, up to 95% of messages are delivered within deadline, while requiring on average less than three message copies.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"231 ","pages":"Article 108038"},"PeriodicalIF":4.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attribute-based policies through microservices in a smart home scenario","authors":"Alessandra Rizzardi,&nbsp;Sabrina Sicari,&nbsp;Alberto Coen-Porisini","doi":"10.1016/j.comcom.2024.108039","DOIUrl":"10.1016/j.comcom.2024.108039","url":null,"abstract":"<div><div>Application containerization allows for efficient resource utilization and improved performance when compared to traditional virtualization techniques. However, managing multiple containers and providing services such as load balancing, fault tolerance and security represent challenging tasks in the emerging microservices architectures. In this context, Kubernetes platform allows to build resilient distributed containers. Besides its efficiency in terms of configuration and architectural resiliency, it must also guarantee the access control to the managed resources. In fact, information must be protected throughout the different microservices which compose an application. To cope with such an issue, this paper proposes the definition of attribute-based policies able to regulate data disclosure within a Kubernetes-based microservices network. Simulations are carried out in a local Minikube environment, considering a smart residence scenario. The investigated metrics include response time, required memory, CPU load, and disk usage.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"231 ","pages":"Article 108039"},"PeriodicalIF":4.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143157034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}

{"title":"Blockchain-based VANET edge computing-assisted cross-vehicle enterprise authentication scheme","authors":"Jiaming Lai ,&nbsp;Xiaohong Zhang ,&nbsp;Shuling Liu ,&nbsp;Shaojiang Zhong ,&nbsp;Ata Jahangir Moshayedi","doi":"10.1016/j.comcom.2024.108040","DOIUrl":"10.1016/j.comcom.2024.108040","url":null,"abstract":"<div><div>Vehicular Ad hoc Network (VANET) is considered one of the feasible solutions to improve the efficiency and safety of modern transportation systems, and it provides new opportunities for creating a safe and efficient traffic environment. In recent years, this technology has attracted extensive attention from the academic community. However, VANET is an open network with frequent information interaction, and users are vulnerable to security and privacy threats. The existing schemes mainly consider the identity authentication of vehicles in vehicle enterprises (VEs). Due to concerns about the leakage of core data, VEs lack the motivation to establish a cross-vehicle enterprise identity authentication framework. Based on the above analysis, we propose a cross-vehicle enterprise authentication architecture by designing a two-stage certificate generation mechanism where certificate authority (CA) and VEs cooperate to generate identity credentials for vehicles. To address the concerns of VEs, we establish distributed trust and enable information sharing across VEs by introducing a consortium blockchain composed of car companies, CA, and pseudonym certificate authority (PCA). Considering the need for vehicles to access road traffic information, we use a public blockchain to store public information, and the practical byzantine fault tolerant (PBFT) algorithm is used to reach consensus. Instead of using computationally complex bilinear pairing and mapping-to-point hashing operations, the proposed scheme uses an elliptic curve cryptosystem (ECC), considering the limited hardware resources of the vehicle and RSU. In addition, our scheme integrates edge computing to solve complex computing tasks that cannot be performed locally and further reduces system latency. Security analysis and performance analysis show that our scheme has better performance than existing schemes in terms of security, computational overhead, and communication overhead.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"231 ","pages":"Article 108040"},"PeriodicalIF":4.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}

{"title":"Leveraging decentralized communication for privacy-preserving federated learning in 6G Networks","authors":"Rafael Teixeira ,&nbsp;Gabriele Baldoni ,&nbsp;Mário Antunes ,&nbsp;Diogo Gomes ,&nbsp;Rui L. Aguiar","doi":"10.1016/j.comcom.2025.108072","DOIUrl":"10.1016/j.comcom.2025.108072","url":null,"abstract":"<div><div>Artificial intelligence (AI) is a fundamental pillar in developing next-generation networks. Federated learning (FL) emerges as a promising solution to address data privacy concerns during AI model training within the network. However, training AI models on user equipment raises challenges regarding battery consumption, unreliable connections, and communication overhead. This paper proposes Zenoh, a data-centric communication middleware, as an alternative to the traditional Message Passing Interface (MPI) for FL applications. Zenoh’s decentralized nature and low communication overhead make it suitable for resource-constrained devices and unreliable network connections. The paper compares Zenoh and MPI in a realistic FL scenario, demonstrating Zenoh’s potential to outperform MPI in terms of flexibility, communication efficiency, and system complexity.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"233 ","pages":"Article 108072"},"PeriodicalIF":4.5,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}

{"title":"UAV-assisted mobile edge computing model for cognitive radio-based IoT networks","authors":"Hisham M. Almasaeid","doi":"10.1016/j.comcom.2025.108071","DOIUrl":"10.1016/j.comcom.2025.108071","url":null,"abstract":"<div><div>The explosive growth in Internet of Things (IoT) in terms of number of applications and deployed devices has created many challenges over the past decade. Among the most critical of which are the increasing demand on spectrum resources, the growing computation and data processing cost, and the limited energy resources. In this paper, we present a model for IoT networks that incorporates the technologies of cognitive radio (CR), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and radio-frequency energy harvesting to address the aforementioned challenges. In this model, UAVs provide computation and energy recharging services to IoT devices. These services can be requested/delivered through multiple spectrum bands by exploiting the CR technology. Specifically, aim at scheduling the task offloading and energy transmission/harvesting activities over time and frequency so that the maximum energy consumption rate among IoT devices is minimized. A mixed integer linear program was formulated to find such schedule. A greedy sub-optimal algorithm was also proposed, where our results show that it is within <span><math><mrow><mo>≈</mo><mn>11</mn><mtext>%</mtext></mrow></math></span> of the optimal solution.</div><div>We also investigate the maximum energy consumption rate among IoT devices under several settings regarding number of UAV MEC servers, task size, task offloading cost, and task local computation cost.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"233 ","pages":"Article 108071"},"PeriodicalIF":4.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RP-DFC: Responsive Probes and Dynamic Flow Classification based load balancing in datacenter networks","authors":"Bo Li,&nbsp;Qiang Li,&nbsp;Bo Peng,&nbsp;Ji Zhao,&nbsp;Shunhua Tan","doi":"10.1016/j.comcom.2025.108069","DOIUrl":"10.1016/j.comcom.2025.108069","url":null,"abstract":"<div><div>Datacenter networks achieve high bandwidth by establishing multiple accessible paths between hosts. This necessitates a load-balancing approach to effectively select optimal paths for data flows, minimizing transmission delays and path congestion. The current congestion awareness and load-balancing methods that rely on active detection encounter issues with substantial bandwidth overhead from probes and overlook the distribution characteristics of network traffic. This paper introduces RP-DFC as a distributed load-balancing approach that utilizes responsive probes and flow classification within the data plane. RP-DFC employs in-band network telemetry and active detection to create a responsive probe congestion awareness mechanism. This mechanism can adaptively adjust the detection frequency according to the network congestion status, significantly decreasing the bandwidth overhead of active detection. RP-DFC further enhances network performance in high-load scenarios by employing advanced large-flow and small-flow classification techniques tailored to data centers’ unique traffic distribution characteristics. This strategic implementation at the network edge optimizes traffic management, significantly outperforming existing methods. RP-DFC exhibits a substantial 30% performance improvement over HULA under high-load conditions, concurrently reducing probe overhead by an impressive 98%. Moreover, when benchmarked against alternative methods like W-ECMP, RP-DFC achieves a notable 29% enhancement in performance, highlighting its effectiveness in optimizing data center network operations.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"234 ","pages":"Article 108069"},"PeriodicalIF":4.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143341153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deeply fused flow and topology features for botnet detection based on a pretrained GCN","authors":"Xiaoyuan Meng ,&nbsp;Bo Lang ,&nbsp;Yuhao Yan ,&nbsp;Yanxi Liu","doi":"10.1016/j.comcom.2025.108084","DOIUrl":"10.1016/j.comcom.2025.108084","url":null,"abstract":"<div><div>The characteristics of botnets are mainly reflected in their network behaviors and the intercommunication relationships among their bots. The existing botnet detection methods typically use only one kind of feature, i.e., flow features or topological features; each feature type overlooks the other type of features and affects the resulting model performance. In this paper, for the first time, we propose a botnet detection model that uses a graph convolutional network (GCN) to deeply fuse flow features and topological features. We construct communication graphs from network traffic and represent node attributes with flow features. The extreme sample imbalance phenomenon exhibited by the existing public traffic datasets makes training a GCN model impractical. To address this problem, we propose a pretrained GCN framework that utilizes a public balanced artificial communication graph dataset to pretrain the GCN model, and the feature output obtained from the last hidden layer of the GCN model containing the flow and topology information is input into the Extra Tree classification model. Furthermore, our model can effectively detect command-and-control (C2) and peer-to-peer (P2P) botnets by simply adjusting the number of layers in the GCN. The experimental results obtained on public datasets demonstrate that our approach outperforms the current state-of-the-art botnet detection models. In addition, our model also performs well in real-world botnet detection scenarios.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"233 ","pages":"Article 108084"},"PeriodicalIF":4.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-agent deep reinforcement learning-based partial offloading and resource allocation in vehicular edge computing networks","authors":"Jianbin Xue ,&nbsp;Luyao Wang ,&nbsp;Qingda Yu ,&nbsp;Peipei Mao","doi":"10.1016/j.comcom.2025.108081","DOIUrl":"10.1016/j.comcom.2025.108081","url":null,"abstract":"<div><div>The advancement of intelligent transportation systems and the increase in vehicle density have led to a need for more efficient computation offloading in vehicular edge computing networks (VECNs). However, traditional approaches are unable to meet the service demand of each vehicle due to limited resources and overload. Therefore, in this paper, we aim to minimize the long-term computation overhead (including delay and energy consumption) of vehicles. First, we propose combining the computational resources of local vehicles, idle vehicles, and roadside units (RSUs) to formulate a computation offloading strategy and resource allocation scheme based on multi-agent deep reinforcement learning (MADRL), which optimizes the dual offloading decisions for both total and residual tasks as well as system resource allocation for each vehicle. Furthermore, due to the high mobility of vehicles, we propose a task migration strategy (TMS) algorithm based on communication distance and vehicle movement speed to avoid failure of computation result delivery when a vehicle moves out of the communication range of an RSU service node. Finally, we formulate the computation offloading problem for vehicles as a Markov game process and design a Partial Offloading and Resource Allocation algorithm based on the collaborative Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (PORA-MATD3). PORA-MATD3 optimizes the offloading decisions and resource allocation for each vehicle through a centralized training and distributed execution approach. Simulation results demonstrate that PORA-MATD3 significantly reduces the computational overhead of each vehicle compared to other baseline algorithms in VECN scenarios.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"234 ","pages":"Article 108081"},"PeriodicalIF":4.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the right choice of data from popular datasets for Internet traffic classification","authors":"Jacek Krupski,&nbsp;Marcin Iwanowski,&nbsp;Waldemar Graniszewski","doi":"10.1016/j.comcom.2025.108068","DOIUrl":"10.1016/j.comcom.2025.108068","url":null,"abstract":"<div><div>Machine learning (ML) models used to analyze Internet traffic, similar to models in all other fields of ML, need to be fed by training datasets. Many such sets consist of labeled samples of the collected traffic data from harmful and benign traffic classes captured from the actual traffic. Since the traffic recording tools capture all the transmitted data, they contain much information related to the registration process that is irrelevant to the actual traffic class. Moreover, they are not fully anonymized. Thus, there is a need to preprocess the data before proper modeling, which should always be addressed in related studies, but often, this is not done. In our paper, we focus on the dependence of the efficiency of threat detection ML models by selecting the appropriate data samples from the training sets during preprocessing. We are analyzing three popular datasets: USTC-TFC2016, VPN-nonVPN, and TOR-nonTOR, which are widely used in traffic classification, security, and privacy-enhancing technologies research. We show that some choices of data sample pieces, although maximizing the model’s efficiency, would not result in similar outcomes in the case of traffic data other than the learning set. The reason is that, in these cases, models are biased due to learning incidental correlations that appear in the datasets used for training the model, introduced by auxiliary data related to the network traffic capturing and transmission process. They are present in popular datasets but may never appear in traffic data. Consequently, the models trained on such datasets, without any preprocessing and anonymization, would never reach the accuracy levels of the training data. Our paper introduces five consecutive levels of anonymization of the traffic data and points out that only the highest provide correct learning results. We validate the results by applying decision trees, random forests, and extra tree models. Having found the optimal part of the header data that may safely be used, we focus on the length of the remaining part of the traffic data to find its minimal length, which preserves good detection accuracy.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"233 ","pages":"Article 108068"},"PeriodicalIF":4.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}

{"title":"Evaluating Conditional handover for 5G networks with dynamic obstacles","authors":"Souvik Deb ,&nbsp;Megh Rathod ,&nbsp;Rishi Balamurugan ,&nbsp;Shankar K. Ghosh ,&nbsp;Rajeev Kumar Singh ,&nbsp;Samriddha Sanyal","doi":"10.1016/j.comcom.2025.108067","DOIUrl":"10.1016/j.comcom.2025.108067","url":null,"abstract":"<div><div>To enhance seamless connectivity in millimetre wave New Radio networks, Conditional handover has evolved as a promising solution. Unlike A3 handover where handover execution is certain after receiving handover command from the serving access network, in Conditional handover, handover execution is <em>conditional</em> on Reference signal received power measurements from current and target access networks, as well as on handover parameters such as preparation and execution offsets. Presence of dynamic obstacles may block the signal from serving and (or) target access networks, which results in violation of the conditions for handover preparation/execution. Moreover, signal blockage by dynamic obstacles may cause radio link failure, which may cause handover failure as well. Analytic evaluation of Conditional handover in the presence of dynamic obstacles is quite limited in the existing literature. In this work, handover performance of Conditional handover has been analysed in terms of handover latency, handover packet loss and handover failure probability. A Markov model accounting the effect of dynamic obstacles, handover parameters (e.g., execution offset, preparation offset, time-to-preparation and time-to-execution), user velocity and channel fading characteristics has been proposed to characterize handover failure. Results obtained from the proposed analytic model have been validated against simulation results. Our study reveals that optimal configuration of handover parameters is actually conditional on the presence of dynamic obstacles, user velocity and fading characteristics. This study will be helpful for the mobile operators to configure handover parameters for New Radio systems where dynamic obstacles are present.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"233 ","pages":"Article 108067"},"PeriodicalIF":4.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}