Computer Communications最新文献

{"title":"Design, Implementation, Performance evaluation of a Sub-7 GHz 5G NR-U system","authors":"Mahamadou Diawara ,&nbsp;Andre Faye","doi":"10.1016/j.comcom.2026.108425","DOIUrl":"10.1016/j.comcom.2026.108425","url":null,"abstract":"<div><div>With the introduction of the fifth generation of mobile networks (5G) in <em>3GPP Release 15</em>, driven by the exponential growth in the number of mobile users, the emergence of new multimedia services and the proliferation of private networks, spectrum management has become a key challenge in the field of telecommunications. In light of the high costs associated with the acquisition and use of licensed frequency bands, the NR-U (New Radio-Unlicensed) standard has emerged as a strategic solution. It enables the extension of 5G services to unlicensed spectrum, thereby addressing the increasing demand for capacity and flexibility. Unlicensed bands, particularly those below 7 GHz, exhibit promising characteristics for supporting real-time critical applications. They offer a cost-effective, flexible communication infrastructure capable of dynamically adapting to network capacity demands. This paper presents an experimental study of 5G NR-U operation over sub-7 GHz unlicensed bands using the open-source OpenAirInterface (OAI) platform and USRP B210 software-defined radio. We integrated these bands into a 5G system and provided a reference framework for future research on communications over unlicensed spectrum with OAI. The implementation accounts for hardware constraints and the stringent requirements of real-time processing to emulate a realistic deployment environment. Performance, and power consumption analysis results confirm the relevance of using sub-7 GHz unlicensed bands for critical applications in private network scenarios or connectivity extensions in remote areas. The proposed implementation is validated through a drone-based application scenario.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108425"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980585","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":"FaaSBid: An auction-based model for Function as a Service in edge-fog environments using unallocated resources","authors":"Abdulrahman K. Al-Qadhi ,&nbsp;Rukshan Athauda ,&nbsp;Rohaya Latip ,&nbsp;Masnida Hussin","doi":"10.1016/j.comcom.2026.108413","DOIUrl":"10.1016/j.comcom.2026.108413","url":null,"abstract":"<div><div>The exponential growth of IoT devices has resulted in a need to process IoT workloads. Processing such workloads near the edge instead of the cloud has a number of advantages including lower latency, improved security and ability to meet many other Quality of Service attributes. Function as a Service (FaaS) is becoming a popular method to process such IoT workloads. In this paper, we propose a novel model, termed FaaSBid, that incentivise users to utilise serverless functions near the edge using unallocated resources. The service provider offers a discount range based on resource utilisation, where users offer bids to execute their functions near the edge resulting in cost savings while the service providers have a new revenue stream and higher resource utilisation near the edge. In this paper, a number of algorithms are proposed and evaluated for FaaSBid model. To initialise function placement, Fitness-Based Swap (FBSW) algorithm is proposed which places functions based on pre-defined information such as function size, function maximum execution time, and storage cost. Next, the Dynamic Demand Replacement Algorithm (DDRA) algorithm is used to place in-demand functions near the edge nodes periodically, while the proposed task scheduling algorithm - Maximum Revenue Bid (MRB) is used to give priority to tasks to maximise revenue near the edge. We have evaluated the FaaSBid model and the proposed algorithms and pricing model by comparing with a number of existing models and algorithms using real-world datasets. The results show that FaaSBid model provides higher resource utilisation, a new revenue stream for service providers while reducing costs for users. On average, in FaasBid, the proposed pricing model saved 12.9% and 6.5% compared to AWS fixed pricing and AuctionWhisk pricing respectively per function execution. Also, the results show that the proposed function placement and scheduling algorithms outperform many well-known function placement and scheduling algorithms in terms of revenue generated, resource utilisation, throughput, and latency with significant improvements near the edge. The results also demonstrated that dynamically placing functions based on demand has a significant impact. Overall, this paper outlines a new paradigm that uses unutilised resources near the edge, improving many QoS attributes from both service providers' and users’ perspectives.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108413"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980581","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":"Context-aware anomaly detection by community detection in the Internet of Things","authors":"Fatemeh Stodt ,&nbsp;Christoph Reich ,&nbsp;Fabrice Theoleyre","doi":"10.1016/j.comcom.2026.108414","DOIUrl":"10.1016/j.comcom.2026.108414","url":null,"abstract":"<div><div>This paper introduces a novel context-aware anomaly detection framework for the Internet of Things, leveraging community detection in multi-edge graphs with a heterogeneous Graph Neural Network (HeteroGNN) architecture to enhance network security. The proposed framework detects anomalies such as unexpected communication patterns among devices that rarely interact, unusual traffic spikes during off-hours, or deviations in the contextual and knowledge-based interactions of devices. For example, in an industrial IoT environment, unauthorized access or malicious activity can be inferred from unexpected communication within a device community after working hours. Our detection approach uses multi-edge graphs to model diverse interactions (network communication, context, knowledge) and applies community detection to capture stable graph structures. By incorporating these insights into a HeteroGNN, the framework effectively distinguishes anomalous edges while maintaining scalability and adaptability to dynamic network conditions. Experimental evaluation on the CIC-ToN-IoT and CIC-IDS2017 dataset demonstrates the framework’s superior accuracy, precision, and robustness, establishing it as a practical and effective solution for securing IoT networks against both known and emerging threats.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108414"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145904069","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":"Deep reinforcement learning based energy management in full-duplex ultra dense networks with cell switching and radio resource allocation","authors":"Tahere Rahmati,&nbsp;Behrouz Shahgholi Ghahfarokhi","doi":"10.1016/j.comcom.2026.108430","DOIUrl":"10.1016/j.comcom.2026.108430","url":null,"abstract":"<div><div>The exponential growth in traffic load and increasing number of connected devices have driven cellular networks to offer high capacity and to support massive access. Full-Duplex Ultra-Dense Networks (FD-UDNs) represent a promising technology to meet this demand in cellular networks. However, these networks encounter serious challenges concerning energy consumption and high levels of interference, which, if not properly managed, can adversely affect overall network performance. This paper presents a deep reinforcement learning-based solution for the problem of joint small base station (SBS) on/off switching and resource allocation, with the objective of maximizing energy efficiency and meeting quality of service (QoS) requirements. To reduce complexity, we decompose the problem into two sub-problems: 1) BS sleep management and 2) power and radio resource allocation. For BS sleep management, two approaches are proposed: centralized and distributed. In the centralized approach, the network decides about the sleep state of the SBSs. In the distributed approach, each SBS independently decides on its sleep state. Subsequently, by assigning users to the active stations, each BS allocates transmit power and radio resources to its users. The simulation results highlight performance of the proposed methods compared to the previous method in terms of both energy efficiency and user satisfaction rate. Additionally, the results show that our distributed sleep management method outperforms the centralized one.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108430"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079028","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":"L4D: An outlier-based learning framework for detecting event patterns in vehicular networks","authors":"Kawthar Zaraket ,&nbsp;Hassan Harb ,&nbsp;Ismail Bennis ,&nbsp;Ali Jaber ,&nbsp;Abdelhafid Abouaissa","doi":"10.1016/j.comcom.2026.108436","DOIUrl":"10.1016/j.comcom.2026.108436","url":null,"abstract":"<div><div>This paper presents Learning4Detecting (L4D), an efficient learning framework designed for identifying unusual traffic incidents in Vehicular Ad hoc Networks (VANETs). L4D addresses the challenges of traffic outlier detection by combining advanced feature extraction techniques (LBP, GLCM, HOG) with a two-tier hybrid binary classification system, including event pattern recognition followed by a second verification classifier. This is complemented by a multi-class classification layer for event categorization. Unlike existing methods, L4D optimizes both preprocessing and classification to enhance detection accuracy, effectively handle unseen events, and capture spatio-temporal patterns, all while reducing computational overhead. Experimental results demonstrate that L4D outperforms existing techniques in both accuracy and efficiency when applied to real-world VANET datasets.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108436"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079029","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":"Large and reliable data transfer service for LoRa mesh network applications","authors":"Joan Miquel Solé,&nbsp;Roger Pueyo Centelles,&nbsp;Felix Freitag,&nbsp;Roc Meseguer,&nbsp;Roger Baig Viñas","doi":"10.1016/j.comcom.2025.108404","DOIUrl":"10.1016/j.comcom.2025.108404","url":null,"abstract":"<div><div>Recently, LoRa mesh networks have appeared as a communication technology for Internet of Things (IoT) devices. Through node-to-node communication, novel distributed IoT applications that extend beyond the capabilities of the LoRaWAN architecture can be enabled. However, current technologies for LoRa networks do not provide mechanisms for large and reliable data transfers between IoT nodes. This paper presents a service for such data transfers in LoRa mesh network applications, along with the protocol and formats used for inter-node communication. We explain the design choices and detail the implementation decisions to ensure that this service is practically usable. To this end, the service was integrated into the LoRaMesher library and is available as an open-source operational implementation. In experiments with ten real nodes and two network topologies, we observe that the service effectively achieves a large and reliable message delivery in an environment of concurrent transmissions and packet losses. In contrast, the cost of reliability for large data transfers is an increased number of messages and a higher delivery time. With the integration of the service into the LoRaMesher technology, developers now have a library that provides a reliable and large payload service for LoRa mesh network applications, eliminating the need to develop such capacity as a specific application-level solution.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108404"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929282","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":"Sparse QoS prediction for cloud services via inductive subgraph pattern aware graph neural network","authors":"Jianlong Xu ,&nbsp;Caiyi Chen ,&nbsp;Qingcao Dai ,&nbsp;Guanchen Du ,&nbsp;Ruiqi Wang ,&nbsp;Mingtong Li ,&nbsp;Quanqing Guo ,&nbsp;Yuxiang Zeng","doi":"10.1016/j.comcom.2026.108415","DOIUrl":"10.1016/j.comcom.2026.108415","url":null,"abstract":"<div><div>Accurately predicting the Quality of Service (QoS) is a crucial issue for selecting suitable cloud services for each user. Collaborative prediction models have been successful in selecting suitable cloud services for users. However, they often struggle with extreme sparsity, where only a limited number of interactions are available for collaborative filtering. Some models excel at handling extreme sparsity but struggle with generalization at the same time. To overcome these challenges, we propose a sparse QoS prediction framework for cloud services via an inductive subgraph pattern-aware graph neural network (ISPA-GNN). Our framework employs a novel graph-based collaborative filtering approach combined with a subgraph sampling strategy to extract semantic information about user-service interactions more effectively. To optimize memory usage and enhance the generalization of unseen users or services, we utilize decoupled ID-based embeddings that maximize contextual information. Extensive experiments on a large-scale, real-world QoS dataset demonstrate that ISPA-GNN outperforms most current collaborative QoS prediction techniques in terms of mean absolute error (MAE) and root mean squared error (RMSE), while also achieving significant gains in memory efficiency.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108415"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929269","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":"An efficient master head selection for multi-EEG to multi-fog IoT network using 6G-driven FaaS","authors":"Rupalin Nanda ,&nbsp;Sakthivel P. ,&nbsp;Rama Krushna Rath ,&nbsp;Abhishek Hazra","doi":"10.1016/j.comcom.2026.108429","DOIUrl":"10.1016/j.comcom.2026.108429","url":null,"abstract":"<div><div>An Electroencephalogram (EEG) signal plays a vital role in a healthcare communication system for recording the electrical activities of the human brain from the scalp. In recent times, the conventional IoT-based healthcare system uses the cloud computing paradigm to manage time-critical healthcare data. Moreover, switching to the fog computing, the fog-assisted EEG systems are for single EEG applications. However, the use of a fog computing paradigm for a single EEG system is not an efficient solution in terms of resource management and time consumption. Therefore, we introduce a Fog-enabled EEG architecture where multiple fog devices collaboratively process the data in a single integrated IoT platform. As the proposed architecture is new, we focus on developing the mathematical model of the architecture and discuss the crucial aspects. Additionally, we devise a dynamic optimal fog head selection within the network using a weighted multi-criteria decision-making approach. From the simulation, we observe that the average propagation delay is reduced by approximately 95% using 6G-enabled fog computing as compared to the cloud. Further, our method reduces the total delay by 83.87% compared to the existing baseline KCHE technique, showing the effectiveness of this work.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108429"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980580","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":"Performance analysis and optimisation of wireless sensor networks with startup times and (V,N)-policy sleep scheduling","authors":"Yongcong Mou ,&nbsp;Yinghui Tang ,&nbsp;Miaomiao Yu","doi":"10.1016/j.comcom.2026.108427","DOIUrl":"10.1016/j.comcom.2026.108427","url":null,"abstract":"<div><div>To effectively conserve energy in wireless sensor networks (WSNs) and reduce packet delay, we propose a (<span><math><mrow><mi>V</mi><mo>,</mo><mi>N</mi></mrow></math></span>)-policy sleep scheme for each sensor node, functioning in four distinct states. We model the sensor node, which incorporates the sleep mechanism, as a discrete-time <span><math><mrow><mi>G</mi><mi>e</mi><mi>o</mi><mo>/</mo><mi>G</mi><mo>/</mo><mn>1</mn></mrow></math></span> vacation queueing system that accounts for startup times and an activation threshold. We first employ a probabilistic analysis technique to conduct a transient analysis of the system, aiming to derive recursive formulas for the steady-state distribution of the number of packets. We further obtain explicit expressions for several essential system performance metrics, including the expected number of packets, mean delay, and average energy cost of the node. The simulation experiments on models with various service time distributions confirm the analytical results, and extensive numerical experiments evaluate the sensitivity of system performance to several parameters. A weighted-sum cost function integrating mean delay and average energy consumption is formulated, and optimal sleep-wake strategies that minimise the weighted sum cost are evaluated across diverse sleep time distributions, service time distributions, weight coefficients, and delay constraints. The results demonstrate the advantages of the <span><math><mrow><mo>(</mo><mi>V</mi><mo>,</mo><mi>N</mi><mo>)</mo></mrow></math></span>-policy in achieving an ideal equilibrium between energy efficiency and mean delay in WSNs.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108427"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024173","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":"Applying NovaGenesis: A service-oriented, self-organizing, and programmable IoT architecture for LoRa and Wi-Fi-based environmental monitoring","authors":"Antonio M. Alberti ,&nbsp;Epper Bonomo ,&nbsp;Rodrigo H. Santos ,&nbsp;Victor A. de J. Alberti ,&nbsp;Marcelo E. Pellenz ,&nbsp;Rodrigo da Rosa Righi","doi":"10.1016/j.comcom.2026.108423","DOIUrl":"10.1016/j.comcom.2026.108423","url":null,"abstract":"<div><div>This work integrates NovaGenesis (NG), a clean-slate IoT architecture, with LoRa technology within low-power wide-area networks (LPWAN), extending previous efforts on NG connectivity with Wi-Fi. The research aims to update the embedded version of NG and develop devices for seamless LoRa and Wi-Fi IoT operation. It evaluates NG’s performance on LoRa and Wi-Fi, focusing on throughput, delay, and packet loss. Despite LPWAN limitations, the results show that the NG deployment is feasible, validating its self-organizing IoT life cycle to maintain service continuity between an ESP-32 and a data client. Performance meets the needs of IoT applications in agribusiness, logistics, and smart monitoring. In addition, a 24-hour environmental monitoring experiment was conducted in Santa Rita do Sapucaí(SRS), Minas Gerais, Brazil, where a commercial weather station was modified to integrate NG, allowing accurate collection of temperature, humidity, atmospheric pressure, wind conditions, solar radiation and UV index. The results met expected diurnal patterns in SRS, proving the reliability and precision of the sensors and communication infrastructure. This solution overcomes common IETF IoT stack limitations in devices naming, information provenance, entities identification, programmability via digital twins, programmability, services and devices self-organization, and trust formation, offering a robust platform for varied IoT scenarios in LPWAN environments. These are the key benefits of applying NovaGenesis for LoRa and Wi-Fi-based environmental monitoring.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108423"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980584","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}