IEEE Open Journal of the Communications Society最新文献

{"title":"EMOPP-IRS: Evolutionary Multi-Objective Path Planning for Intelligent IRS-Assisted IoT Networks","authors":"Muhammad Farhan;Lei Wang;Nadir Shah;Gabriel-Miro Muntean","doi":"10.1109/OJCOMS.2025.3610553","DOIUrl":"10.1109/OJCOMS.2025.3610553","url":null,"abstract":"Intelligent Reflecting Surfaces (IRS) have recently emerged as a transformative technology in the realm of satellite communication networks, particularly in Space-Air-Ground Integrated Networks (SAGIN), offering significant improvements, particularly in remote and last-mile terrestrial areas affected by urban blockage, non-Line-of-Sight (NLoS) links, and multipath fading. This paper proposes EMOPP-IRS, an innovative solution aimed at improving overall network performance through multi-objective optimization in IRS-assisted communication networks. EMOPP-IRS integrates a wide range of parameters and employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously optimize multiple conflicting objectives such as received power, latency, and energy efficiency. A comprehensive evaluation demonstrates that the proposed EMOPP-IRS significantly outperforms existing solutions by achieving a well-balanced trade-off under varying operational conditions. EMOPP-IRS adapts effectively to dynamic network scenarios while preserving diversity among optimal solutions, leading to substantial improvements in network performance, scalability, and power efficiency during communications through the IRS-assisted network at the ground segment of the satellite network.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"7920-7938"},"PeriodicalIF":6.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Handover Management in UAV Networks With Blockages","authors":"Neetu R. R;Gourab Ghatak;Vivek Ashok Bohara","doi":"10.1109/OJCOMS.2025.3610952","DOIUrl":"10.1109/OJCOMS.2025.3610952","url":null,"abstract":"We investigate the performance of UAV-based networks in urban environments characterized by blockages, focusing on their capability to support the service demands of mobile users. The UAV-base stations (UAV-BSs) are modeled using a 2D marked-Poisson point process (MPPP), where the marks represent the altitude of each UAV-BS. Leveraging stochastic geometry, we analyze the impact of blockages on network reliability by studying the meta distribution (MD) of the SINR for a specific reliability threshold and the association probabilities for both LoS and NLoS UAV-BSs. Furthermore, to enhance the performance of mobile users, we propose a novel cache-based handover (HO) management strategy that dynamically selects the cell search time and delays the received signal strength (RSS)-based base station associations. This strategy aims to minimize unnecessary HOs experienced by users by leveraging caching capabilities at user equipment (UE), thus reducing latency, ensuring seamless connectivity, and maintaining the quality of service (QoS). This study provides valuable insights into optimizing UAV network deployments to support the stringent requirements of the network, ensuring reliable, low-latency, and high-throughput communication for next-generation smart cities.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"8209-8224"},"PeriodicalIF":6.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11168872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AoI-Aware Pilot Sequence Construction for Active User Detection in MTC Networks","authors":"Gabriel Martins De Jesus;Onel Alcaraz López;Richard Demo Souza;João Luiz Rebelatto;Markku Juntti","doi":"10.1109/OJCOMS.2025.3610944","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3610944","url":null,"abstract":"Many Internet of Things (IoT) applications require fresh information for proper operation, and the age of information (AoI) metric serves as a key indicator of freshness. To guarantee the delivery of fresh information, transmitting users must be correctly identified so that their packets can be decoded, making active user detection (AUD) a prerequisite for establishing communication. The AUD becomes particularly challenging in scenarios with multiple active users and limited orthogonal resources. While compressed sensing (CS) techniques have shown promise for AUD by enabling simultaneous communication in resource-scarce environments, most research has focused on improving detection precision rather than optimizing AoI. This work introduces a novel modification to CS-based AUD to prioritize high-AoI transmissions, reducing the network’s average AoI. Specifically, we propose a pilot sequence construction method where users generate pilots as linear combinations of an orthogonal sequence basis, with unique weight sets assigned to each user. High-AoI users are allocated exclusive pilot sequences, ensuring orthogonality between high- and low-AoI transmissions and enhancing the detection of high-AoI users, even in congested settings. Simulations reveal that optimizing the AoI threshold and the number of sequences dedicated to high- and low-AoI transmissions leads to significant reductions in average AoI, especially as the number of active users increases. Interestingly, this comes at the cost of reduced detection precision compared to standard setups. A special case dedicates all pilots to high-AoI users, further enhancing AoI reduction at the expense of discarding low-AoI packets.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"7747-7757"},"PeriodicalIF":6.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11168852","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DAP-MFL: Distributed AP-Assisted Multilayer Federated Learning for Resource-Constrained IoT Networks With RAW-Slot Management","authors":"Mumin Adam;Uthman Baroudi","doi":"10.1109/OJCOMS.2025.3611093","DOIUrl":"10.1109/OJCOMS.2025.3611093","url":null,"abstract":"This paper introduces DAP-MFL, a novel Distributed Access Point-Assisted Multilayer Federated Learning framework tailored for resource-constrained IoT systems. Recognizing the limitations of traditional 4G/5G-based FL deployments in terms of energy consumption and scalability, DAP-MFL strategically leverages an energy-efficient IEEE 802.11ah network protocol to enable more sustainable federated learning implementations. The framework introduces a client–edge–fog–cloud architecture that systematically distributes the aggregation process across multiple network layers, thereby optimizing both computational and communication resources while maintaining the integrity of the learning process. At the core of DAP-MFL are three specialized slot management methods developed to address the unique channel access constraints of IEEE 802.11ah networks: (1) Strict Slot Assignment with Dropping (SSAD), which introduces a strict dropping policy for latency-sensitive scenarios, maximizing efficiency by removing stragglers instantly; (2) Selective Replacement with Gradual Inclusion (SRGI), which prioritizes stability via a unique phased-replacement strategy, gradually integrating nonparticipant stations without disrupting training; and (3) Round Robin (RR), which provides fairness in resource allocation. Extensive evaluations using real-world IoT datasets (AQC and WISDMM) and the benchmark MNIST dataset demonstrate that SSAD achieves the highest latency reduction of up to 97% and energy savings of up to 95% compared to the baseline methods, while SRGI simultaneously offers significant latency reduction of up to 70% and energy efficiency improvements of up to 78% along with enhanced stability and long-term learning without compromising the accuracy compared to the baseline methods. These results validate the efficacy of our framework, showcasing SSAD’s superior efficiency of SSAD and SRGI’s balanced performance of SRGI, making it well-suited for large-scale IoT deployments in smart cities and beyond.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"8158-8174"},"PeriodicalIF":6.3,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11168861","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power Allocation Optimization for Secure OFDM-NOMA Downlink Systems","authors":"Hanxue Yue;Cheng Guo;Quanzhong Li;Hao Chen;Qi Zhang","doi":"10.1109/OJCOMS.2025.3610253","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3610253","url":null,"abstract":"Non-orthogonal multiple access (NOMA) is considered as an important enabling technique for the sixth generation wireless network. In this paper, we study a secure orthogonal frequency division multiplexing (OFDM)-NOMA downlink system, which combines OFDM modulation and NOMA technique. In the system, the central user (CU) is an entrusted user and the cell-edge user (EU) is a potential eavesdropper. Due to the use of OFDM modulation, the CU may have weak subcarriers and the EU may have strong subcarriers. Our objective is to maximize both the achievable secrecy rate of CU and achievable rate of EU through power allocation optimization. For the non-convex optimization problem, we propose a sequential parametric convex approximation (SPCA)-based algorithm, a Lagrangian dual transformation (LDT)-based algorithm, and a prime decomposition-based algorithm. Simulation results demonstrate that the proposed LDT-based and prime decomposition-based algorithms, while have much lower computation complexity than the SPCA-based algorithm, achieve the same performance as the latter. Furthermore, it is found that our proposed OFDM-NOMA system outperforms the conventional OFDM-orthogonal multiple access system.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"7555-7566"},"PeriodicalIF":6.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial Special Section on Trustworthy AI-Enabled Edge Computing in Next-Generation Wireless Networks","authors":"Shaohua Wan;Zhipeng Cai;Quanyan Zhu;Sotirios K. Goudos;Athanasios V. Vasilakos;Carla Fabiana Chiasserini","doi":"10.1109/OJCOMS.2025.3597376","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3597376","url":null,"abstract":"","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"7233-7236"},"PeriodicalIF":6.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165230","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Head-On Vehicle Collision Prevention With Machine Learning and a Fully Centralized Radio Sensing Approach","authors":"Jorge D. Cárdenas;Miguel A. Díaz-Ibarra;Carlos A. Gutiérrez;Francisco R. Castillo-Soria;Cesar A. Azurdia-Meza","doi":"10.1109/OJCOMS.2025.3610396","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3610396","url":null,"abstract":"Head-on vehicle collision prevention remains a critical challenge in autonomous and manual driving, particularly for complex vehicular scenarios where conventional sensors face line-of-sight limitations. In this work, we propose a novel fully centralized warning system platform using continuous waveform (CW) signals and Doppler signature analysis. We use a propagation model to analyze Doppler effects in vehicular communication systems, validated empirically across two distinct driving environments (high-speed highway and medium-speed rural road). Our platform was developed using general-purpose equipment to generate a data set of spectrograms computed with the received radio-frequency (RF) CW signals. Furthermore, machine learning models (SVM/KNN/Boosted Trees) applied to spectrogram features reduced via Principal Component Analysis are used to classify five different vehicular events related to head-on collision. Our system achieves up to 99% classification accuracy while demonstrating that Doppler signatures in communication signals can be used to extract high-quality information for safety-critical sensing. Our results show robust performance in both test scenarios, with high-precision for oncoming vehicles at different speeds. The system’s success in using CW RF signals for sensing, establishes a foundation for Integrated Sensing and Communication (ISAC) implementations.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"7720-7735"},"PeriodicalIF":6.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiation Pattern Design for SINR Maximization in Multi-User Reconfigurable MISO Communications","authors":"Tao Zhong;Boxi Zhang;Ang Li;Yonghui Li;Lingyang Song","doi":"10.1109/OJCOMS.2025.3610118","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3610118","url":null,"abstract":"Reconfigurable antennas (RAs) are capable of actively adjusting their radiation patterns to fulfill different needs of the wireless communication network. In this paper, we investigate the performance of RAs for a multi-user multiple-input single-output (MU-MISO) downlink system, where we propose an active beamforming scheme that exploits the effect of RAs to further improve the system performance. The joint design problem of beamforming and radiation pattern to maximize the minimum received signal-to-interference-plus-noise-ratio (SINR) is firstly formulated. Since the optimization variables are softly-coupled, we adopt an alternating optimization (AO) framework to decompose the joint design problem into the beamforming design sub-problem and the pattern design sub-problem, where we obtain the optimal beamformer and the radiation pattern of RAs that can achieve a desired communication performance. We further show that existing closed-form beamforming schemes can also benefit from RAs by designing proper radiation patterns, where the singular value optimization (SVO) approach and the successive convex approximation (SCA) method are proposed to obtain the desired radiation pattern. Both of them effectively address the optimization problem by dealing with the non-convexity in the objective function. Numerical results demonstrate that the proposed reconfigurable pattern design can further enhance the performance by manipulating the wireless channels in a judicious way.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"7795-7809"},"PeriodicalIF":6.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164873","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STING: A Stealthy Backdoor Attack on GNN-Based Malicious Domain Detection via DNS Perturbations","authors":"Muhammad Anan;Mahmoud Nazzal;Abdallah Khreishah;Issa Khalil;Nhathai Phan;Ahmad Sawalmeh","doi":"10.1109/OJCOMS.2025.3610784","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3610784","url":null,"abstract":"Detecting malicious Internet domains is essential for safeguarding against various online threats. The current approach to detecting malicious domains (MDD) employs a graph neural network (GNN) method, which uses DNS logs to construct heterogeneous graphs for determining the maliciousness of unknown domains. Despite its success, this method is vulnerable to data poisoning attacks where an adversary can manipulate specific graph nodes to implant a backdoor into the model during training. To showcase the vulnerability, we propose a <underline>s</u>tealthy <underline>t</u>rigger <underline>i</u>njection attack on <underline>n</u>ode features and <underline>g</u>raph structure in MDD, dubbed (<monospace>STING</monospace>). The attacker carefully manipulates selected features and edges of its nodes in the graph to create backdoor trigger patterns designed to evade detection by the MDD system, without knowing the model or other parts of the graph. Results from experiments conducted on real-world GNN-based MDD approaches show that the proposed attack is highly effective, with a success rate of over 88% in launching backdoor attacks and only a slight decrease in the model’s accuracy on legitimate domains (not exceeding 4%). Furthermore, the attack bypasses established defenses such as graph purification, adversarial training, and outlier detection, making it a major threat to the security of MDD systems. This study serves as a warning and stresses the importance of continuous vigilance and proactive efforts by both researchers and security experts to secure GNN-based MDD systems and maintain their trustworthiness and stability.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"7823-7841"},"PeriodicalIF":6.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Autonomous Anomaly Detection for Security in SDN-IoT Networks","authors":"Tharindu Lakshan Yasarathna;Madhusanka Liyanage;Nhien-An Le-Khac","doi":"10.1109/OJCOMS.2025.3610365","DOIUrl":"10.1109/OJCOMS.2025.3610365","url":null,"abstract":"Converging Software-Defined Networking (SDN) and the Internet of Things (IoT) has directed innovative network architectures and applications. However, this fusion exposes security vulnerabilities due to SDN-IoT networks’ expanded attack surface and dynamic nature. Autonomous Anomaly Detection (AAD) plays a pivotal role in swiftly identifying and mitigating real-time security threats in this landscape. This survey thoroughly investigates AAD approaches within SDN-IoT networks, with a particular focus on applying advanced Deep Learning (DL) techniques. It explores the significance of SDN-IoT architecture, articulates the motivations driving AAD, and highlights its critical role in ensuring the availability, confidentiality, authentication, integrity, and authorization of networked resources. Furthermore, it defines the threat vectors across different layers of SDN-IoT, facilitating a comprehensive validation of existing attack and defence approaches. The paper provides an extensive review of DL-based AAD methods in SDN-IoT networks, examining their strengths, limitations, and practical implications. Notable contributions include an in-depth taxonomy of DL models—such as CNNs, RNNs, LSTMs, and autoencoders—designed to detect and mitigate various security threats, including DDoS attacks, data breaches, and abnormal traffic patterns. The performance of these techniques is evaluated using measures like accuracy, recall, and F1-score, with some methods achieving detection accuracy exceeding 99%. Additionally, this survey identifies key challenges in implementing AAD systems, such as scalability, real-time processing, and integration within resource-constrained IoT environments, while proposing future research directions to address these issues. In conclusion, this comprehensive survey summarizes the multifaceted landscape of DL-based AAD for Security in SDN-IoT Networks, providing a foundation for further exploration and innovation that can advance security within the evolving realm of interconnected networks.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"8007-8048"},"PeriodicalIF":6.3,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}