IEEE Open Journal of the Communications Society最新文献

{"title":"Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning","authors":"Ramzi Al-Sharawi;Abdelfatah Ali;Mostafa Shaaban;Nasser Qaddoumi;Mohamed S. Abdalzaher","doi":"10.1109/OJCOMS.2025.3564069","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3564069","url":null,"abstract":"Smart grid cybersecurity is a critical research challenge due to society’s dependence on reliable electricity. Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. However, these studies fail to simultaneously consider system cost, loss in system observability, and false data injection attack (FDIA) detection performance. Thus, this paper proposes a novel approach by formulating this issue as a tri-objective functions optimization problem. The proposed approach optimizes PMU allocation to maximize topological observability and minimize system cost while improving the FDIA detection performance using machine learning. Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. To demonstrate the proposed approach’s efficacy, it is tested on the IEEE 38-bus distribution system. To verify the strength of the developed KNN classifier, we examined it using seven different metrics: accuracy, brier loss, F1-score, elapsed time, learning curve, receiver operating characteristic curve (ROC) curve, and confusion matrix. The simulation results show that the KNN model achieved superior attack classification performance with a top accuracy of 99.99% and a minimal Brier loss of <inline-formula> <tex-math>$9.9478 times 10^{-4}$ </tex-math></inline-formula> on the ±0.2% PMU observation tolerance dataset. These results highlight the success of our framework in concurrently optimizing attack detection performance, topological observability, and system cost.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4036-4050"},"PeriodicalIF":6.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073251","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":"Explainable Reinforcement and Causal Learning for Improving Trust to 6G Stakeholders","authors":"Miguel Arana-Catania;Amir Sonee;Abdul-Manan Khan;Kavan Fatehi;Yun Tang;Bailu Jin;Anna Soligo;David Boyle;Radu Calinescu;Poonam Yadav;Hamed Ahmadi;Antonios Tsourdos;Weisi Guo;Alessandra Russo","doi":"10.1109/OJCOMS.2025.3563415","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3563415","url":null,"abstract":"Future telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent’s behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4101-4125"},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072942","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 on the Effectiveness of Existing Smart Home Cyber Attacks Detection Solution: A Broadband Service Providers’ Perspective","authors":"Md Mizanur Rahman;Faycal Bouhafs;Frank den Hartog","doi":"10.1109/OJCOMS.2025.3563270","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3563270","url":null,"abstract":"The Internet of Things (IoT) is rapidly reshaping smart homes with innovative technologies for everyday convenience. Although smart home technologies are becoming more popular, many homeowners lack the expertise and skills to operate them. Particularly mismanaged networking technologies often result in homeowners complaining to their Broadband Service Providers (BSPs) when there are disruptions to the performance of the smart home devices and services. Current remote management solutions used by BSPs offer limited functionality and accuracy in identifying the causes of these disruptions. Causes may include legitimate factors, such as misconfiguration, interference, and congestion, but they could also be the result of cyber attacks. This review exposes knowledge gaps that impede the development of effective troubleshooting tools (particularly related to cyber attacks) for BSPs, including network-related challenges and dataset limitations. We also discuss future research directions, emphasising scalable and data-efficient methods to reduce storage requirements, costs, and privacy concerns, which will enable BSPs to manage millions of customer networks more effectively.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4010-4035"},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072923","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":"Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets","authors":"Gowhar Javanmardi;Ramiro Samano Robles","doi":"10.1109/OJCOMS.2025.3562795","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562795","url":null,"abstract":"This paper presents the study of machine learning (ML) algorithms for the prediction of vehicular channels under impairments that arise in realistic implementations. Emerging vehicular applications will operate in complex settings with potentially abrupt and quick propagation changes. These features can be difficult to capture by ideal (complete) datasets. Therefore, we consider sets of variable length (incomplete) to reflect the rapidly changing vehicular environment. Our assumption is that, in challenging settings, measurements collected by devices or base stations (BSs) might be the only information available to train models. Our approach covers multiple sub-cases including: i) short sets for rapidly changing settings, and ii) large sets for stationary conditions. Measurements are subject to two additional impairments: incorrect sampling and noise. We use a validated synthetic model for vehicular channels to analyze a spectrum of impairment settings that emulates the transition from non-ideal to ideal conditions. This stress test leads to new conclusions on channel prediction: i) how and why algorithms behave in different ways under diverse conditions (optimality region), ii) derivation of new bounds linked to channel features (coherence time, channel correlation, etc.), iii) optimum parameter settings for ML also linked to channel statistics, and iv) proposal of potential improvements. Linear regression (LR) is shown to have a better trade-off between performance and implementation issues when sets are short, oversampled, and with a high signal-to-noise ratio (SNR). A new method to improve the convergence of polynomial LR in sets close to the undersampling regime is proposed here. Results show that neural networks (NNs), particularly deep learning (DL), continuously reduce the mean square error (MSE) as the length of the set increases. They quickly outperform LR, even in sets near the undersampling condition with low SNR. The effectiveness of prediction is severely degraded when sets are undersampled or subject to low SNR. Convolutional NN (CNN) and particularly LSTM (Long Short-Term Memory) show more resilience to these impairments. One key objective of channel prediction is improving resource allocation to reduce latency and increase reliability, which are crucial metrics in applications such as autonomous vehicles. Our analysis contributes to the understanding (explainability) of how AI behaves under multiple impairments, which can also lead to the improvement of advanced vehicular applications.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3964-3980"},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908426","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":"Generative AI-Based Dependency-Aware Task Offloading and Resource Allocation for UAV-Assisted IoV","authors":"Xing Wang;Chao He;Wenhui Jiang;Wanting Wang;Xiaoyan Liu","doi":"10.1109/OJCOMS.2025.3562720","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562720","url":null,"abstract":"In recent years, the Internet of Vehicles (IoV) has emerged as a pivotal driving force within intelligent transportation systems, offering users immersive interactive experiences. Meanwhile, unmanned aerial vehicles (UAVs) have demonstrated substantial potential for widespread application within the IoV domain, attributed to their high flexibility, low cost, and ease of deployment. However, as the complexity of IoV tasks increases, complex dependencies among tasks give rise to notable delay issues, which are further exacerbated by the uneven distribution of computational resources. In response to the previously mentioned challenges, we suggest a strategy for resource distribution and task offloading aided by UAVs for IoV. Firstly, by constructing a complex task dependency model, tasks are topologically sorted to clarify the dependencies among tasks, thereby optimizing task execution order. Secondly, focusing on the core issues of task offloading and resource allocation, we present the multi-agent deep deterministic policy gradient (MADDPG) algorithm to devise a dependency-aware scheduling strategy. This strategy integrates task dependencies and UAV mobility characteristics, enabling intelligent decision-making for UAV trajectory planning and task scheduling by analyzing actor and critic network action rewards at each timeslot. To further tackle non-convex optimization problems, we design a federated learning (FL)-based intelligent data caching and computation offloading (Fed-IDCCO) algorithm, leveraging deep reinforcement learning (DRL) techniques. This approach handles large-scale and continuous state and action spaces to obtain optimal task offloading strategies within IoV environments. This methodology not only effectively reduces task processing delays and energy consumption but also significantly enhances the overall system performance. Extensive experimental results demonstrate that, compared to several existing benchmark algorithms, the suggested method offers unique benefits in diminishing delays in task processing, lowering energy usage, controlling costs, and improving cache hit rates.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3932-3949"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918783","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":"Toward a Time-Bounded Solution for Real-Time Latency Prediction in Dynamic 5G Communication","authors":"Andrea Nota;Lisa Maile;Selma Saidi","doi":"10.1109/OJCOMS.2025.3562726","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562726","url":null,"abstract":"The growing adoption of safety-critical applications in domains such as Industry 4.0 and autonomous driving places strict latency requirements on Fifth Generation (5G) wireless networks. Existing approaches typically rely on latency measurements, detecting violations only after they occur, or on pre-planned network configurations, which are impractical in highly dynamic environments such as mobile device communication. In this work, we propose a proactive analytical approach for predicting Worst-Case Latencies (WCLs) in 5G networks before violations occur. Our method derives upper bounds on latency analytically, ensuring both explainability and computational efficiency. Additionally, we introduce the concept of a Validity Interval (VI), which quantifies how long a latency prediction remains valid. We extensively evaluate our solution in synthetic and realistic simulations to define the impact of key parameters of our new model on global pessimism, computational overhead, and accuracy. Our results show that with complete knowledge of future channel conditions, our analytical model can consistently upper-bound the simulated worst-case latencies. Even with limited knowledge about future channel conditions, using state-of-the-art forecasting methods, our approach still bounds 99.8% of actual latencies, demonstrating its robustness. With this, our works demonstrate that future 5G networks can enable safe and reliable real-time applications even in highly dynamic environments.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3853-3867"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10971387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896415","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":"Modeling Local Demand for Mobile Spectrum: An Interpretable Machine Learning Approach","authors":"Janaki Parekh;Elizabeth Yackoboski;Amir Ghasemi;Halim Yanikomeroglu","doi":"10.1109/OJCOMS.2025.3562794","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562794","url":null,"abstract":"With the expansion of 5G networks and the ongoing development of future 6G networks, the demand for mobile spectrum is expected to continue to grow, particularly at a local level. In response, spectrum regulators globally are exhibiting growing interest in enhancing their understanding of current mobile spectrum demand. The goal is twofold: to maximize the socioeconomic benefits of this finite resource and to ensure that spectrum policy and licensing decisions continue to drive innovation within the wireless industry. Despite its importance, research in modeling mobile spectrum demand has been notably scarce, particularly at the granularity required in the spectrum regulatory domain. To address this gap, this paper presents a data-driven approach to estimate localized mobile spectrum demand within the context of spectrum regulation. A novel demand proxy is first introduced, derived from a large and diverse dataset of crowdsourced commercial mobile measurements. Subsequently, spectrum demand modeling is formulated as a regression task and a variety of classical machine learning models are explored, leveraging publicly available geospatial data as input features. The top-performing model successfully achieves an R2 of 0.76 and a Root Mean Square Error of 51.02 on the hold-out test set. Finally, a machine learning interpretability technique is applied to demonstrate how these models can be used for regulatory decision-making, particularly in scenarios requiring transparency and accountability.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4063-4082"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072943","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":"Source Localization via Doppler Shifts Using Mobile Sensors in ICNets Within Industry 5.0","authors":"Lina Wang;Xiaoting Mao;Kai Fang;Ali Kashif Bashir;Marwan Omar;Xiaoping Wu;Wei Wang","doi":"10.1109/OJCOMS.2025.3526925","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3526925","url":null,"abstract":"Source localization plays a significant role in industrial 5.0 applications by availing of the communication networks. For the industrial communication networks (ICNets), Doppler shifts can be measured inexpensively by equipping with some mobile sensors. This paper investigates the localization problem of an unknown source using only Doppler shift (DS) when the signal carrier frequency is unavailable. To deal with the DS-only localization under unknown knowledge of carrier frequency, we first propose a semidefinite programming (SDP) solution by applying the convex relaxation technique. The complexity of the SDP solution is high. We also propose a closed-form solution for estimating both the source position and the carrier frequency. Using the weighted least squares (WLS) method, the closed-form solution is segmented into two stages. A bias-compensated scheme is incorporated to reduce the bias of the estimates in the stage-one WLS solution. Subsequently, the root mean square error (RMSE) performance is improved in the stage-two WLS solution, and we design the bias-compensated two-stage WLS (BCTSWLS) solution. Experiments have demonstrated that, compared to traditional localization methods with known carrier frequency, our approach–utilizing SDP and BCTSWLS–effectively solves the localization problem in high-noise environments. This results in greater robustness and accuracy in practical industrial applications. Specifically, in scenarios with fewer sensors or unknown signal frequency, our method effectively reduces bias, achieving accuracy levels close to the Cramér-Rao Lower Bound (CRLB), thereby demonstrating significant performance advantages.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3429-3442"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974419","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860780","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 Blockchain-Based Architecture of Web3.0: A Comprehensive Decentralized Model With Relay Nodes, Unique IDs and P2P","authors":"Hyunjoo Yang;Sejin Park","doi":"10.1109/OJCOMS.2025.3562706","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562706","url":null,"abstract":"The advent of the Internet’s Web2.0 era provided users with vast data and services but relied heavily on centralized servers, leading to data ownership limitations, privacy concerns, and security vulnerabilities such as data breaches and service disruptions. As Web3.0 emerges with a focus on decentralization, there is a growing need for efficient architectures that ensure scalability, security, and low-latency communication. This paper introduces a decentralized architecture for Web3.0, leveraging blockchain, relay nodes, unique IDs, and P2P communication. The proposed model integrates a blockchain-based unique ID management system for secure node identification and employs a multistep TPM-based verification algorithm to validate node authenticity in real time. Additionally, the model introduces a TPM+Reputation system that combines hardware-backed node attestation with blockchain-stored historical reputation scores, ensuring robust trust evaluations. This dual-layered approach mitigates risks associated with reputation inflation, identity manipulation, and malicious node behavior. When faced with malicious requests, the system significantly reduces verification latency to an average of 0.0106 ms, ensuring rapid rejection and preserving network stability, while legitimate requests are processed efficiently with an average latency of 0.4125 ms.Experiments comparing latency-based and random-based relay selection methods across various network configurations (public-public, public-private, private-private) demonstrate that latency-based selection optimizes connection setup times in low-latency environments, while random-based selection provides robust performance in complex network scenarios. The integration of the reputation system further enhances the reliability of relay selection by prioritizing trustworthy nodes and dynamically penalizing malicious ones. In experimental evaluations, the system demonstrated a detection rate improvement of up to 1.9 times faster compared to traditional models in identifying and isolating malicious nodes, while maintaining consistent trust evaluations for legitimate nodes. These findings highlight the model’s adaptability and efficiency, offering a secure, scalable, and resilient solution for decentralized networks while addressing critical challenges in trust and reputation management.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3981-4009"},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072946","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":"Analysis of Unavailability in Middleboxes With Multiple Backup Servers Under Shared Protection","authors":"Naohide Wakuda;Ryuta Shiraki;Eiji Oki","doi":"10.1109/OJCOMS.2025.3562234","DOIUrl":"https://doi.org/10.1109/OJCOMS.2025.3562234","url":null,"abstract":"Middlebox functions, implemented as software on general-purpose servers via network function virtualization, require reliable protection mechanisms to ensure service continuity. Assessing the unavailability of these functions is critical, as failures can lead to significant service disruptions. However, existing analytical models primarily assume that a function is protected by at most one or two backup servers, limiting their applicability in scenarios requiring higher resilience. To address this limitation, this paper proposes an analytical model for evaluating the unavailability of middlebox functions under a multiple-backup shared protection strategy, where multiple backup servers protect one or more functions. Our model allows each function to be protected by multiple backup servers, ensuring availability while ensuring that each backup server can simultaneously recover at most one function. Utilizing a Markov chain, we analyze state transitions and establish equilibrium-state equations, providing an analytical foundation for evaluating the performance of the multiple-backup shared protection strategy. Numerical results demonstrate that this strategy significantly enhances availability, reducing unavailability by up to 72.3% compared to the single-backup shared protection strategy in the scenarios examined. Our study provides a detailed analysis of backup allocation strategies, focusing on their impact on function availability and offering more profound insights into their effectiveness through theoretical properties and performance comparisons with existing strategies. Our evaluation reveals that the multiple-backup shared protection strategy reduces unavailability by up to 64.8% compared to the single-backup shared protection strategy in the examined allocation cases.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"3868-3881"},"PeriodicalIF":6.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10970014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896344","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}