IEEE Open Journal of the Communications Society最新文献

{"title":"Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning","authors":"Maryam Abbasalizadeh;Krishnaa Vellamchety;Pranathi Rayavaram;Sashank Narain","doi":"10.1109/OJCOMS.2024.3484948","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3484948","url":null,"abstract":"In this paper, we present the Learning Greedy Link Scheduling (LGLS) algorithm, a novel approach for optimizing link scheduling in wireless networks. By integrating deep learning and fuzzy logic, LGLS predicts link quality probabilities, which provide critical topological information to dynamically manage wireless network interference. This approach enhances resource allocation efficiency, leading to better bandwidth and spectrum usage. Our comprehensive evaluation shows that LGLS outperforms traditional algorithms such as Local Greedy Scheduling (LGS), achieving link scheduling performance improvements ranging from 9.60% to 24.79% and activating up to 24.10% more links. These results demonstrate LGLS’s robustness and efficiency in diverse network conditions, making it a promising solution for future wireless network optimization.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6832-6848"},"PeriodicalIF":6.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10729871","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600270","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":"A Survey of LoRaWAN-Integrated Wearable Sensor Networks for Human Activity Recognition: Applications, Challenges and Possible Solutions","authors":"Nahshon Mokua Obiri;Kristof Van Laerhoven","doi":"10.1109/OJCOMS.2024.3484002","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3484002","url":null,"abstract":"Long-Range Wide Area Networks (LoRaWAN), a prominent technology within Low-Power Wide Area Networks (LPWANs), have gained traction in remote monitoring due to their long-range communication, scalability, and low energy consumption. Compared to other LPWANs like Sigfox, Ingenu Random Phase Multiple Access (Ingenu-RPMA), Long-Term Evolution for Machines (LTE-M), and Narrowband Internet of Things (NB-IoT), LoRaWAN offers superior adaptability in diverse environments. This adaptability makes it particularly effective for Human Activity Recognition (HAR) systems. These systems utilize wearable sensors to collect data for applications in healthcare, elderly care, sports, and environmental monitoring. Integrating LoRaWAN with edge computing and Internet of Things (IoT) frameworks enhances data processing and transmission efficiency. However, challenges such as sensor wearability, data payload constraints, energy efficiency, and security must be addressed to deploy LoRaWAN-based HAR systems in real-world applications effectively. This survey explores the integration of LoRaWAN technology with wearable sensors for HAR, highlighting its suitability for remote monitoring applications such as Activities of Daily Living (ADL), tracking and localization, healthcare, and safety. We categorize state-of-the-art LoRaWAN-integrated wearable systems into body-worn, hybrid, objectmounted, and ambient sensors. We then discuss their applications and challenges, including energy efficiency, sensor scalability, data constraints, and security. Potential solutions such as advanced edge processing algorithms and secure communication protocols are proposed to enhance system performance and user comfort. The survey also outlines specific future research directions to advance this evolving field.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6713-6735"},"PeriodicalIF":6.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142550545","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":"Asymptotic Analysis of the Downlink in Cooperative Massive MIMO Systems","authors":"Itsik Bergel;Siddhartan Govindasamy","doi":"10.1109/OJCOMS.2024.3483176","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3483176","url":null,"abstract":"We consider the downlink of a cooperative cellular communications system, where several base-stations around each mobile cooperate and perform zero-forcing to reduce the received interference at the mobile. We derive, for the first time, closed-form expressions for the asymptotic performance of the network as the number of antennas per base station grows large. These expressions capture the trade-offs between various system parameters, and characterize the joint effect of noise and interference (where either noise or interference is asymptotically dominant and where both are asymptotically relevant). The presented analysis is significantly more challenging than the uplink analysis due to the dependence between beamforming vectors of nearby base stations. This statistical dependence is handled by introducing novel bounds on marked shot-noise point processes with dependent marks, which are also useful in other contexts. The asymptotic results are verified using Monte Carlo simulations, which indicate that they are useful even when the number of antennas per base station is only moderately large. Based on these expressions, we present a novel power allocation algorithm that is asymptotically optimal while significantly reducing the coordination overhead between base stations.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6972-6986"},"PeriodicalIF":6.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10722858","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650590","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":"Few-Shot Class-Incremental Learning for Network Intrusion Detection Systems","authors":"Davide Di Monda;Antonio Montieri;Valerio Persico;Pasquale Voria;Matteo De Ieso;Antonio Pescapè","doi":"10.1109/OJCOMS.2024.3481895","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3481895","url":null,"abstract":"In today’s digital landscape, critical services are increasingly dependent on network connectivity, thus cybersecurity has become paramount. Indeed, the constant escalation of cyberattacks, including zero-day exploits, poses a significant threat. While Network Intrusion Detection Systems (NIDSs) leveraging machine-learning and deep-learning models have proven effective in recent studies, they encounter limitations such as the need for abundant samples of malicious traffic and full retraining upon encountering new attacks. These limitations hinder their adaptability in real-world scenarios. To address these challenges, we design a novel NIDS capable of promptly adapting to classify new attacks and provide timely predictions. Our proposal for attack-traffic classification adopts Few-Shot Class-Incremental Learning (\u0000<monospace>FSCIL</monospace>\u0000) and is based on the Rethinking Few-Shot (\u0000<monospace>RFS</monospace>\u0000) approach, which we experimentally prove to overcome other \u0000<monospace>FSCIL</monospace>\u0000 state-of-the-art alternatives based on either meta-learning or transfer learning. We evaluate the proposed NIDS across a wide array of cyberattacks whose traffic is collected in recent publicly available datasets to demonstrate its robustness across diverse network-attack scenarios, including malicious activities in an Internet-of-Things context and cyberattacks targeting servers. We validate various design choices as well, involving the number of traffic samples per attack available, the impact of the features used to represent the traffic objects, and the time to deliver the classification verdict. Experimental results witness that our proposed NIDS effectively retains previously acquired knowledge (with over 94% F1-score) while adapting to new attacks with only few samples available (with over 98% F1-score). Thus, it outperforms non-\u0000<monospace>FSCIL</monospace>\u0000 state of the art in terms of classification effectiveness and adaptation time. Moreover, our NIDS exhibits high performance even with traffic collected within short time frames, achieving 95% F1-score while reducing the time-to-insight. Finally, we identify possible limitations likely arising in specific application contexts and envision promising research avenues to mitigate them.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6736-6757"},"PeriodicalIF":6.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142551997","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":"Online-Learning-Based Predictive Optimization of Uplink Scheduling for Industrial Internet-of-Things","authors":"Chenshan Ren;Xinchen Lyu","doi":"10.1109/OJCOMS.2024.3481431","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3481431","url":null,"abstract":"The industrial Internet of Things (IIoT) operates in dynamic environments where wireless channels are subject to rapid changes, posing significant challenges for reliable data transmission. This paper introduces a novel online learning approach to predictively optimize uplink scheduling for IIoT devices. In harsh industrial settings, the unpredictability of channel conditions and data arrivals necessitates immediate data transmission to ensure timeliness and representativeness. We propose a primal-dual online learning framework that integrates stochastic gradient descent (SGD) and online convex optimization (OCO) to generate predictive uplink schedules. By learning only from past channel changes and data arrivals, our predictive schedule can asymptotically minimize the amount of data dropped at the IIoT devices. We also accelerate the online learning by having the IIoT devices oversample their channels to reduce the penalty of the predictive schedule. The optimality loss is proved to asymptotically diminish with the decrease of SGD/OCO stepsizes and the increase of oversampling rate even in fast-changing IIoT environments. Simulation results validate the effectiveness of our approach, showing significant improvements in system throughput compared to state-of-the-art methods, especially in environments with rapidly changing wireless channels.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6817-6831"},"PeriodicalIF":6.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600374","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":"Detection of Zero-Day Attacks in a Software-Defined LEO Constellation Network Using Enhanced Network Metric Predictions","authors":"Dennis Agnew;Ashlee Rice-Bladykas;Janise Mcnair","doi":"10.1109/OJCOMS.2024.3481965","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3481965","url":null,"abstract":"SATCOM is crucial for tactical networks, particularly submarines with sporadic communication requirements. Emerging SATCOM technologies, such as low-earth-orbit (LEO) satellite networks, provide lower latency, greater data reliability, and higher throughput than long-distance geostationary (GEO) satellites. Software-defined networking (SDN) has been introduced to SATCOM networks due to its ability to enhance management while strengthening network control and security. In our previous work, we proposed a SD-LEO constellation for naval submarine communication networks, as well as an extreme gradient boosting (XGBoost) machine-learning (ML) approach for classifying denial-of-service attacks against the constellation. Nevertheless, zero-day attacks have the potential to cause major damage to the SATCOM network, particularly the controller architecture, due to the scarcity of data for training and testing ML models due to their novelty. This study tackles this challenge by employing a predictive queuing analysis of the SD-SATCOM controller design to rapidly generate ML training data for zero-day attack detection. In addition, we redesign our singular controller architecture to a decentralized controller architecture to eliminate singular points of failure. To our knowledge, no prior research has investigated using queuing analysis to predict SD-SATCOM controller architecture network performance for ML training to prevent zero-day attacks. Our queuing analysis accelerates the training of ML models and enhances data adaptability, enabling network operators to defend against zero-day attacks without precollected data. We utilized the CatBoost algorithm to train a multi-output regression model to predict network performance statistics. Our method successfully identified and classified normal, non-attack samples and zero-day cyberattacks with over 94% accuracy, precision, recall, and f1-scores.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6611-6634"},"PeriodicalIF":6.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142516850","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":"Dynamic Pricing in Multi-Tenant MANO With Resource Sharing: A Stackelberg Game Approach","authors":"Mohammad Reza Abedi;Mehdi Fasanghari;Mohammad Akbari;Nader Mokari;Halim Yanikomeroglu","doi":"10.1109/OJCOMS.2024.3480987","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3480987","url":null,"abstract":"Network slicing is used to support the stringent requirements of sixth generation (6G) services by dividing an infrastructure network into multiple logical networks that can enable service-oriented resource allocation. However, there are several orchestration issues when considering multiple infrastructure providers (InPs) and multiple tenants in a recursive architecture. There are also challenging issues in designing efficient auction mechanisms for such multi-domain and multi-tenant network slicing. To address these challenges, we consider multi-tenant management and orchestration as a multi-buyer, multi-seller scenario, and propose a novel two-stage auction mechanism that aims to increase the overall utility of all participants while mitigating the overall cost of the network. We formulate this two-stage auction mechanism as a multi-leader multi-follower (MLMF) Stackelberg game approach that converges to a Stackelberg equilibrium. In this game, there are multiple InPs that lease network, computing, and storage infrastructure resources to multiple Tier1 tenants in the first stage of the auction mechanism. Next, Tier1 tenants instantiate triple 6G slices as extremely reliable and low-latency communications (eURLLC), ultra-massive machine-type communications (umMTC), and further enhanced mobile broadband (FeMBB) slices, and lease smaller slices to Tier2 tenants through the second step of the auction mechanism. Tier2 tenants then serve different eURLLC, umMTC, and FeMBB users who have specific and mostly different requirements and constraints, while Tier2 tenants manage their own resources to maximize their utility. Due to the distributed nature of the proposed problem, we consider distributed reinforcement learning (DRL) as a solution. Simulation results show that our DRL-based solution increases the average profit of the network by 19% compared to the existing state-of-the-art benchmark.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"7002-7021"},"PeriodicalIF":6.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716743","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664378","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":"Joint Power Allocation for Transmitter and Relay in a Full-Duplex Relay Covert Communication System","authors":"Gui Fang;Jin Chen;Guoxin Li;Rongrong He;Haichao Wang;Yang Yang","doi":"10.1109/OJCOMS.2024.3481264","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3481264","url":null,"abstract":"This paper considers a full-duplex relay-assisted system for covert communication, in which both the transmitter and the relay are the sender of covert messages. Different from current works that only consider the transmitter power optimization or relay power optimization individually, we propose a joint optimization approach for the transmitter and relay power, which is expected to enhance the system’s covert performance. We establish the minimum error detection probability using the Kullback-Leibler (KL) divergence, which serves as the foundation for formulating our joint optimization problem. The objective is to achieve the highest covert transmission rate within the constraints of covertness requirement and total power limitation. Employing a graphical method, we effectively transform inequality constraints into equality constraints, leading to the derivation of an optimal closed-form solution. The simulation results confirm the accuracy of the theoretical derivation and demonstrate that the proposed power allocation method is effective in determining the optimal power for both the transmitter and the full-duplex relay within a two-hop covert communication system.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6674-6685"},"PeriodicalIF":6.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555115","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":"Model-Driven Channel Estimation for MIMO Monostatic Backscatter System With Deep Unfolding","authors":"Yulin Zhou;Xiaoting Li;Xianmin Zhang;Xiaonan Hui;Yunfei Chen","doi":"10.1109/OJCOMS.2024.3479234","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3479234","url":null,"abstract":"Monostatic backscatter has garnered significant interest due to its distinct benefits in low-cost passive sensing. Observing and sensing with backscatter necessitates determining the phase and amplitude of the backscatter channel to identify the state of the target of interest. In the detection of multiple targets, colliding signals can distort the backscatter channel, complicating channel state recovery. It becomes even more challenging when multiple backscattering devices (BDs) are used. This paper proposes a novel channel estimation scheme to tackle the challenge, which is applied to a monostatic backscatter communication system with multiple reader antennas (RAs) and backscatter devices. Specifically, we propose a backscatter communication model and subsequently develop a de-interfering channel estimation framework that considers the ambient interference in the channel, named model-driven unfolded channel estimation (MUCE). To validate the effectiveness and advantages of the MUCE method, it is compared with the least square (LS) algorithm and convolutional neural network (CNN). The results prove that MUCE requires lower computational costs for the same channel estimation performance and achieves an optimal balance between estimation performance and computational expense.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6697-6712"},"PeriodicalIF":6.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10715725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555114","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":"Improving Recognition of Sub-GHz LPWANs: A Deep Learning Approach With the UPC-LPWAN-1 Dataset","authors":"Edgar Maya-Olalla;Mario García-Lozano;David Pérez-Díaz-de-Cerio;Silvia Ruiz-Boqué","doi":"10.1109/OJCOMS.2024.3480856","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3480856","url":null,"abstract":"Deep neural networks (DNNs) have emerged as an effective technique for modulation/system recognition but rely heavily on representative datasets. This paper introduces the “UPC-LPWAN-1” dataset, a comprehensive collection of 40 Sub-GHz LPWAN transmission modes acquired using real hardware. Publicly available to the scientific community, this dataset includes raw and pre-processed samples across different Signal-to-Noise Ratios (SNRs) and features multi-carrier modulations, which are typically underrepresented in existing datasets. The variability in studies using different neural network architectures and small, unrepresentative datasets complicates research comparisons. To address this, this paper compares seven proposed architectures using UPC-LPWAN-1, providing a standardized evaluation. To further enhance accuracy, we propose four new convolutional neural network (CNN) architectures adapted to four forms of signal representation. Our results demonstrate that while some existing models perform well under high SNR conditions, their performance degrades significantly in low SNR environments. The proposed spectrogram-based CNN consistently outperforms other models, achieving a classification accuracy of 99.71% at SNR = 0 dB, above 90% at SNR =−10 dB, and above 70% at SNR =−15 dB, while still being able to differentiate between systems.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6635-6654"},"PeriodicalIF":6.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716536","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518031","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}