Computer Communications最新文献

{"title":"Simulating extended reality traffic: An empirical model from user behavior to network packets","authors":"Luca Mastrandrea,&nbsp;Alessandro Priviero,&nbsp;Gaetano Scarano,&nbsp;Stefania Colonnese,&nbsp;Tiziana Cattai","doi":"10.1016/j.comcom.2025.108244","DOIUrl":"10.1016/j.comcom.2025.108244","url":null,"abstract":"<div><div>Several components in the design of next-generation networks, including user profiling and network slicing, rely on accurate models of traffic load. In this context, recent studies have focused on various video traffic categories, while traffic associated with extended reality (XR) services has received limited attention. This paper introduces a novel empirical model for 3D XR traffic, developed by encoding real Point Clouds using a standard-compliant codec, and able to account for the dynamic of service sessions and user behaviors over an entire session. Our methodology encompasses multiple temporal scales, ranging from milliseconds to minutes, to account for different phenomena related to both user behavior and encoder settings. Initially, we investigate the packet size distribution at the time scale of a semantic unit, corresponding to the encoding of a single point cloud. We verify that it can be effectively represented by a heavy-tailed Gamma distribution. Then, we illustrate how this insight can be leveraged to model application-layer phenomena. Specifically, we demonstrate the applicability of a general semi-hidden Markov model to capture both the temporal dynamics of service sessions and user behaviors. We provide results in terms of comparison of the empirical and fitting traffic distributions, based on quantile to quantile analysis and statistical tests. We also show how the model can be trained on real data and we provide a pseudo-code demonstrating the model application within a network simulator.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108244"},"PeriodicalIF":4.5,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364744","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":"BRI-MAC: Broadcast Receiver Initiated MAC protocol for Internet of Things","authors":"Lakhdar Goudjil ,&nbsp;Samir Fenanir ,&nbsp;Fouzi Semchedine ,&nbsp;Mounir Zerroug","doi":"10.1016/j.comcom.2025.108249","DOIUrl":"10.1016/j.comcom.2025.108249","url":null,"abstract":"<div><div>Efficient energy management in IoT networks, particularly at the MAC layer, is essential due to the excessive energy consumption of traditional protocols caused by retransmissions, idle listening, and control overhead. To address these challenges, this paper proposes BRI-MAC (Broadcast Receiver-Initiated MAC), a multi-hop duty cycle MAC protocol that enhances energy efficiency, reduces latency, and optimizes packet delivery. BRI-MAC employs a rendezvous beacon for transmission scheduling and integrates a collision resolution mechanism to minimize energy waste. In addition to performance improvements, BRI-MAC strengthens IoT security by mitigating DDoS, Sybil, and jamming attacks through adaptive scheduling and resilient multi-hop broadcasting. Simulation results demonstrate that BRI-MAC reduces end-to-end delay by 20 %, achieves a 97 % packet reception rate, maintains low energy consumption (30 %), and offers excellent scalability (90 %), compared to existing MAC protocols such as RI-MAC and EnRI-MAC. These findings highlight its superiority over conventional MAC protocols, making it a compelling solution for large-scale, energy-constrained IoT deployments.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108249"},"PeriodicalIF":4.5,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330712","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":"Joint estimation method for space–time frequency parameters of frequency-hopping network station in the case of low-quality data","authors":"Yuxiao Yang ,&nbsp;Chang Xu ,&nbsp;Xinyue Zhao ,&nbsp;Junjie Li ,&nbsp;Xiaoyu Dang","doi":"10.1016/j.comcom.2025.108234","DOIUrl":"10.1016/j.comcom.2025.108234","url":null,"abstract":"<div><div>The parameter estimation of frequency-hopping networks is an important research direction in the field of communication electronic countermeasures(CECM). However, in urban scenarios, non-line-of-sight transmission and noise interference are common, leading to data loss in sensor networks during information acquisition, which poses significant challenges for the parameter estimation of frequency-hopping networks. To address the issue of missing observation data, this paper proposes an atomic norm soft thresholding method that combines time–frequency and angle information as joint feature vectors(AST-JFV) to improve the accuracy of parameter estimation under conditions of partial data loss. It achieves high-precision estimation of hopping time, instantaneous frequency, and direction of arrival for frequency-hopping signals. Experimental results show that with 20% data loss and a SNR of 10 dB, the proposed method achieves an accuracy of over 90% in estimating hopping time, an estimation error of less than 0.1 MHz for instantaneous frequency, and an angle estimation error controlled within 0.5°, with a computation time reduction of over 80%.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108234"},"PeriodicalIF":4.5,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322596","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-objective optimization of advanced sleep mode for energy saving in cognitive radio network","authors":"Ajay Singh,&nbsp;Rakhee Kulshrestha,&nbsp;Vijaypal Poonia","doi":"10.1016/j.comcom.2025.108232","DOIUrl":"10.1016/j.comcom.2025.108232","url":null,"abstract":"<div><div>The Advanced Sleep Modes (ASM) concept corresponds to entering the Base Station (BS) progressively deeper and less energy-intensive states to reduce energy consumption. Introducing the ASM can mitigate energy wastage during low-traffic periods in the Cognitive Radio Network (CRN). In this study, we propose a strategy for integrating ASM within the CRN architecture to effectively handle primary and secondary traffic across varying ASM sleep states. Additionally, we study the general scenario of CRN with heterogeneous secondary users, imperfect sensing, and unreliable BS due to the arrival of negative packets (virus attack). By modeling the entire system as a three-dimensional discrete-time Markov chain, we conduct the transient analysis of the proposed model. Through numerical demonstrations involving reliability and queueing analyses, we substantiate the validity of the proposed model and examine the impact of reliability on its performance. Then, we showcased the effectiveness of the ASM strategy by comparing it with the Sleep Mode (SM) strategy in terms of the waiting time and blocking probability of the secondary user and the degree of energy savings. Also, simulation experiments are conducted to corroborate the accuracy and validity of the numerical results. Finally, we formulate the Cost Benefit Function (CBF), which depends on both the successful transmission and waiting time of secondary packets. Subsequently, we obtain the Pareto optimal solution for CBF and the degree of energy saving using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques for multi-objective optimization.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108232"},"PeriodicalIF":4.5,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307501","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":"Crowd counting with WiFi sensing based on iterative attentional feature fusion","authors":"BeiMing Yan,&nbsp;Yong Li,&nbsp;LiMeng Dong,&nbsp;ZeRong Ren,&nbsp;HuiMin Liu,&nbsp;Xiang Gao,&nbsp;Wei Cheng","doi":"10.1016/j.comcom.2025.108245","DOIUrl":"10.1016/j.comcom.2025.108245","url":null,"abstract":"<div><div>—Crowd counting has great appeal for a variety of applications, such as public transportation, disaster management and building automation. Recently, WiFi-based crowd counting has gained dominance due to its ubiquitous and non-invasive advantages. However, current WiFi-based crowd counting systems have a limitation in that they do not consider the effect of dynamic crowds and static crowds on crowd counting. In contrast to previous studies, this paper investigates the effect of crowds in different states on crowd counting performance, and proposes a WiFi-based multi-state crowd counting system, which can not only count dynamic or static crowds, but also count joint dynamic and static crowds. By analyzing the effect of crowd states on the signal, we demonstrate that the channel state information (CSI) subcarrier distribution can indicate the count of crowds in different states. To this end, we adopt an iterative attentional feature fusion (IAFF) which allows for the fusion of amplitude and phase information from multiple antennas and adaptively assigns weights to amplitude and phase on multiple subcarriers, thus enabling the counting of crowds in various states. The experimental results show that the system has recognition accuracy of 99.38 % for static crowds, 95.94 % for dynamic crowds, and 97.57 % for joint dynamic and static crowds.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108245"},"PeriodicalIF":4.5,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271214","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-dimensional resource placement algorithm based on parallel genetic algorithm","authors":"Qinlu He ,&nbsp;Fan Zhang ,&nbsp;Genqing Bian ,&nbsp;Weiqi Zhang ,&nbsp;Zhen Li","doi":"10.1016/j.comcom.2025.108235","DOIUrl":"10.1016/j.comcom.2025.108235","url":null,"abstract":"<div><div>With the advancement of cloud-native technologies, container cluster management systems such as Kubernetes, Swarm, and Mesos have emerged. Due to its superior container orchestration capabilities, Kubernetes has been widely adopted across diverse domains and is now the industry-preferred solution for container cluster management. However, Kubernetes primarily relies on a single resource dimension for Pod placement, which often leads to imbalanced resource utilization and single-resource bottlenecks. To address this limitation, we optimize the Pod placement strategy in Kubernetes by designing a parallel genetic algorithm based on the island model, which accounts for multi-dimensional resource consumption in cloud-native environments. The genetic algorithm is tailored to the cloud-native context through enhancements in genetic coding design, initial population generation, and objective function formulation. By integrating the island model with genetic algorithms, our parallel optimization approach improves computational efficiency and addresses the NP-hard challenge of resource placement in cloud environments. Experimental results demonstrate that the proposed algorithm reduces the average single-prediction time by 42.5 %, achieves a cluster resource utilization rate of 93.77 %, and attains a parallel speedup ratio of 3.681. Furthermore, it mitigates resource imbalance and enhances utilization efficiency across clusters of varying scales.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108235"},"PeriodicalIF":4.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262738","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":"Joint analysis of outage probability and energy efficiency in underlay cognitive radio-inspired NOMA systems using the AF relay in a downlink transmission","authors":"Nourollah Davoudian,&nbsp;Hamidreza Bakhshi","doi":"10.1016/j.comcom.2025.108233","DOIUrl":"10.1016/j.comcom.2025.108233","url":null,"abstract":"<div><div>In this paper, we investigate the performance of the underlay Cognitive Radio-Inspired Non-Orthogonal Multiple Access (NOMA) system in a downlink transmission. In our proposed model, a primary transmitter (PT) operating over a weak channel shares its frequency band with a secondary transmitter (ST) to enhance overall spectrum utilization. This work employs a coordinated direct and relay transmission (CDRT) strategy, where the relay operates in amplify-and-forward (AF) mode. We considered co-channel interference affecting both primary and secondary users. We present a novel decoding technique for the symbols of the primary and secondary users based on a combined signal-to-interference plus noise ratio (SINR) framework. This technique significantly improves the outage probability for both user types, resulting in enhanced system performance overall. The main advantage of this method lies in utilizing the combined SINR from both transmission phases, which enables a more robust decoding process. In this study, we derived a closed-form analytical expression for the outage probability of both PU and secondary users (SU). Additionally, we provided an asymptotic approximation of the outage probability at high signal-to-noise ratio (SNR) regimes. Our analytical results show that, at a high SNR, the system's performance based on the asymptotic approximation matches the simulation results, which validates the accuracy of our model. An essential aspect of this study is analyzing power allocation coefficients, as they directly affect system efficiency and energy utilization. In this regard, we derived the power allocation coefficients for the primary and secondary users and presented the corresponding analyses. Based on these coefficients, we calculated the SU's throughput, and system's overall energy efficiency. Our findings indicate that the proposed model reduces outage probability and increases throughput while also enhancing energy efficiency, which is crucial for sustainability and optimal resource utilization. Ultimately, this work can significantly enhance the performance of NOMA and cognitive radio (CR) systems, making it more applicable to the next generation of communication networks.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108233"},"PeriodicalIF":4.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253746","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":"Security optimization and beamforming design for active RIS-assisted UAV relaying NOMA networks","authors":"Ruoyu Zhang ,&nbsp;Songlin Cheng ,&nbsp;Niansheng Chen ,&nbsp;Guangyu Fan ,&nbsp;Lei Rao ,&nbsp;Xiaoyong Song ,&nbsp;Dingyu Yang","doi":"10.1016/j.comcom.2025.108220","DOIUrl":"10.1016/j.comcom.2025.108220","url":null,"abstract":"<div><div>Physical layer security (PLS) in non-orthogonal multiple access (NOMA) networks faces critical challenges, especially for eavesdropping risk of cell-edge users in conventional passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAV) relaying systems, which may be increased because of channel sharing and multiplicative fading. Unlike ground-based RIS deployments limited by installation constraints and multi-path signal degradation, this work proposes an active RIS-assisted UAV (U-ARIS) for multi-user multiple-input single-output (MU-MISO) NOMA systems with two legitimate users and one eavesdropper. Moreover, we formulate a joint optimization problem to maximize sum secrecy rate by coordinating base station beamforming and U-ARIS reflection beamforming. Due to the multivariate nature of optimization variables and complex non-convexity, an alternating optimization (AO) algorithm is designed to decompose the problem into two sub-problems, which can be solved by semidefinite relaxation (SDR) and successive convex approximation (SCA). Simulations demonstrate that the proposed U-ARIS-assisted NOMA scheme achieves secrecy rate improvements of 12.5% and 50% compared to UAV-passive RIS (U-PRIS) and conventional OMA systems, respectively.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108220"},"PeriodicalIF":4.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240697","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":"From 5G to 6G: Empowering vertical industries with next-gen technologies and trial facilities","authors":"Vincent Charpentier ,&nbsp;Miguel Camelo ,&nbsp;Johann M. Marquez-Barja ,&nbsp;Nina Slamnik-Kriještorac","doi":"10.1016/j.comcom.2025.108218","DOIUrl":"10.1016/j.comcom.2025.108218","url":null,"abstract":"<div><div>In the fifth-generation (5G) era, there are opportunities for innovation in various vertical industries, including Transport and Logistics (T&amp;L) and automotive, among others. For instance, the automotive sector can advance with edge computing nodes connected through network slices, providing more reliable teleoperation of vehicles, and enhancing vehicle-to-everything (V2X) use cases. However, widespread adoption of these technologies (e.g., edge computing, teleoperation of vehicles, V2X) has been limited, partly due to a lack of resources to test and explore among verticals to experience the potential improvements that 5G and Beyond (B5G) networks can offer to them. Thus, establishing B5G trial facilities is crucial. These facilities enable real-life B5G deployments across various verticals and serve as collaborative ecosystems where industries, telecom providers, and academia can co-create tailored services to meet specific operational needs.</div><div>As the sixth-generation (6G) era approaches, it is important to equip trial facilities with cutting-edge B5G technologies to support advanced vertical services. This article examines key 5G and B5G technologies for vertical industries, analyzing 40 major trial facilities to assess their capabilities. In addition, offering insights and recommendations to enhance trial facilities for testing and validating innovative vertical use cases in the 6G era.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"241 ","pages":"Article 108218"},"PeriodicalIF":4.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203731","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":"AIRSDN: AI based routing in software-defined networks for multimedia traffic transmission","authors":"Anıl Dursun İpek ,&nbsp;Murtaza Cicioğlu ,&nbsp;Ali Çalhan","doi":"10.1016/j.comcom.2025.108222","DOIUrl":"10.1016/j.comcom.2025.108222","url":null,"abstract":"<div><div>With the rapid increase in internet usage and the number of network-connected devices, network management and optimization have become increasingly challenging, particularly for high-bandwidth applications such as video streaming. The decentralized structure of traditional networks and the lack of standardization further complicate these challenges. Software Defined Networking (SDN) has emerged as a solution, enabling a more flexible and programmable architecture by centralizing network control. However, existing SDN controllers typically determine the optimal path based on simple metrics such as hop count and bandwidth, which can be insufficient in high-traffic scenarios. To overcome these limitations, this study proposes a novel artificial intelligence (AI)-based routing algorithm. Operating within the SDN framework, the proposed algorithm analyzes network traffic levels and dynamically selects the most efficient data transmission paths. The proposed algorithm is simulated in Mininet, a virtual network environment, using a network model inspired by real-world internet structures (NSFNET). Simulations are conducted under varying traffic conditions, with TCP (Transport Control Protocol) data and video transmission scenarios. Key performance metrics are observed, including round-trip time (RTT), throughput, packet loss, and video quality (measured using PSNR and SSIM). The machine learning model was trained using a custom dataset consisting of 876 records generated in the Mininet environment. Although the dataset size is sufficient for the simulation environment, caution should be exercised when generalizing the results to real-world network conditions. Future studies may aim to enhance the model's reliability by exploring data augmentation techniques and utilizing larger datasets that include real-world data. To classify traffic levels, machine learning models are trained, and the best-performing model (Logistic Regression) is integrated into the proposed routing algorithm. The results demonstrate that the proposed AI-based routing algorithm significantly improves network performance compared to both traditional hop-count-based and QoS-aware routing. Particularly in high-traffic scenarios, it achieves lower latency, higher throughput, and better video quality. Additionally, resource usage was analyzed on a Raspberry Pi 5 device, revealing stable RAM consumption (∼50 %) and fluctuating CPU utilization (10–90 %), indicating the feasibility of lightweight deployment with awareness of processing load. This study highlights the potential of AI-driven SDN frameworks for adaptive and efficient network traffic management in high-demand applications, offering a robust solution for dynamic routing.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"240 ","pages":"Article 108222"},"PeriodicalIF":4.5,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177802","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}