IEEE Transactions on Network Science and Engineering最新文献

{"title":"Security Synchronization for Complex Cyber-Physical Networks Under Hybrid Asynchronous Attacks","authors":"Xiaojie Huang;Yingying Ren;Da-Wei Ding","doi":"10.1109/TNSE.2024.3491823","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3491823","url":null,"abstract":"This paper investigates the synchronization of complex cyber-physical networks (CCPNs) under hybrid asynchronous attacks. Firstly, a kind of hybrid asynchronous attack model consisting of DoS attacks in sensor to controller (S-C) channel, DoS attacks in controller to actuator (C-A) channel and connection attacks is proposed, which is a new generalization of traditional synchronous attack model. Secondly, a distributed controller using two combinational measurements of node states and sensor outputs is designed to obtain the synchronization criteria of CCPNs under hybrid asynchronous attacks. Then, two methods are proposed to ensure that all nodes of CCPNs are synchronized based on the designed distributed controller. Meanwhile, the duration time and frequency of attacks that the systems can tolerate are calculated. Finally, two examples are given to illustrate the effectiveness of the proposed method.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"237-251"},"PeriodicalIF":6.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhancing Integrated Gas and Electricity Networks Operation With Coupling Attention-Graph Convolutional Network Under Renewable Energy Variability","authors":"Runze Bai;Xianzhuo Sun;Wen Zhang;Jing Qiu;Yuechuan Tao;Shuying Lai;Junhua Zhao","doi":"10.1109/TNSE.2024.3493247","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3493247","url":null,"abstract":"The growing integration of renewable energy sources into the power grid necessitates innovative approaches to energy system management. Integrated gas and electricity networks offer a promising solution to this challenge, enabling the efficient, reliable, and sustainable operation of energy systems. This paper presents a novel approach to the optimal scheduling of integrated gas and electricity networks, addressing the challenges posed by high penetration of renewable energy sources. First, a learning-assisted methodology is proposed to leverage Graph Convolutional Networks (GCNs) and Bayesian-based uncertainty models to enhance the accuracy and efficiency of scheduling integrated energy systems. The proposed GCN model effectively captures the complex interactions within the integrated network, facilitating accurate power and gas flow predictions. Meanwhile, the Bayesian-based model adeptly manages the inherent uncertainties associated with renewable energy generation, employing a chance-constrained approach to ensure system reliability. The effectiveness of the proposed methodology is demonstrated through extensive simulations on an IEEE 39-bus electricity network coupled with a 22-node hydrogen network. Results indicate significant improvements in computational efficiency and predictive accuracy compared to traditional model-based methods and existing data-driven techniques.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"277-289"},"PeriodicalIF":6.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Enhancing Adaptability and Efficiency of Task Offloading by Broad Learning in Industrial IoT","authors":"Jiancheng Chi;Xiaobo Zhou;Fu Xiao;Tie Qiu;C. L. Philip Chen","doi":"10.1109/TNSE.2024.3493053","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3493053","url":null,"abstract":"In the Multi-access Edge Computing (MEC)-based Industrial Internet of Things (IIoT), a key challenge is to make an efficient task-offloading decision. Machine learning methods have emerged as popular solutions to address this issue. However, in IIoT, it is common for the feature distribution of data to change significantly over time, i.e., data drift, and existing machine learning-based schemes struggle to frequent data drift, failing to maintain consistent high accuracy of task-offloading decisions. This struggle arises because they require extended retraining or extensive model adjustments, which involve significant delays and increased computational overhead due to the complex network structure. In this paper, we propose a \u0000<bold>B</b>\u0000road learning-based task \u0000<bold>OFF</b>\u0000loading scheme (BOFF). In BOFF, a data drift detection method based on statistical features and a sliding window is established to determine the occurrence of data drift in the system, while utilizing the Gini coefficient to enhance feature extraction and improve accuracy of task-offloading decision model under data drift. When data drift is detected, BOFF leverages its fast training and redeployment capabilities based on feature-enhanced broad learning to update the task offloading model and maintain accuracy. In the absence of significant data drift, minor changes in data distribution are addressed through incremental updates to slow the decline in model accuracy. Numerical results demonstrate that BOFF significantly improves the adaptability of data drift, ensuring high accuracy and efficiency of task offloading in dynamic IIoT environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"263-276"},"PeriodicalIF":6.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 IRS-Enabled Phase Cooperative Framework for Sum Rate Maximization in B5G Networks","authors":"Haleema Sadia;Ahmad Kamal Hassan;Ziaul Haq Abbas;Ghulam Abbas;John M. Cioffi","doi":"10.1109/TNSE.2024.3486733","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3486733","url":null,"abstract":"Intelligent reflecting surfaces (IRSs) improves beyond fifth generation (B5G) systems performance in power- and cost-efficient ways. However, maintaining the performance of multiple IRSs-enabled networks without constraining available resources is challenging. In this paper, we propose a novel IRS-assisted phase cooperative framework to maximize the sum rate of the secondary phase cooperative system (\u0000<inline-formula><tex-math>$mathbf {SPC}_{mathcal {S}ys}$</tex-math></inline-formula>\u0000) located in close proximity of the primary phase cooperative system (\u0000<inline-formula><tex-math>$mathbf {PPC}_{mathcal {S}ys}$</tex-math></inline-formula>\u0000). We exploit transmit beamforming (BF) at base stations (BSs) and phase shift optimization at the IRS with effective phase cooperation between BSs. The maximization problem turns out to be NP-hard, so an alternating optimization is solved for the \u0000<inline-formula><tex-math>$mathbf {PPC}_{mathcal {S}ys}$</tex-math></inline-formula>\u0000 using an exhaustive search method, i.e., the branch-reduce-and-bound (BRB) algorithm, to obtain the optimal solution for active beamformers, and phase optimization is performed using the semidefinite relaxation (SDR) approach. Further, an active BF is carried out at the \u0000<inline-formula><tex-math>$mathbf {SPC}_{mathcal {S}ys}$</tex-math></inline-formula>\u0000 transmitter by utilizing optimal phase shifts of the \u0000<inline-formula><tex-math>$mathbf {PPC}_{mathcal {S}ys}$</tex-math></inline-formula>\u0000. For the proposed framework, the performance of the BRB algorithm is compared with sub-optimal heuristic BF approaches, including transmit minimum-mean-square-error, zero-forcing BF, and maximum-ratio-transmission. The results support the benefits of deploying IRS in wireless networks to improve sum rate performance of \u0000<inline-formula><tex-math>$mathbf {SPC}_{mathcal {S}ys}$</tex-math></inline-formula>\u0000 through effective phase cooperation. The proposed framework significantly reduces the hardware cost of the system without constraining the resources of \u0000<inline-formula><tex-math>$mathbf {PPC}_{mathcal {S}ys}$</tex-math></inline-formula>\u0000.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"134-144"},"PeriodicalIF":6.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Poisoning the Well: Adversarial Poisoning on ML-Based Software-Defined Network Intrusion Detection Systems","authors":"Tapadhir Das;Raj Mani Shukla;Shamik Sengupta","doi":"10.1109/TNSE.2024.3492032","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3492032","url":null,"abstract":"With the usage of Machine Learning (ML) algorithms in modern-day Network Intrusion Detection Systems (NIDS), contemporary network communications are efficiently protected from cyber threats. However, these ML algorithms are starting to be compromised by adversarial attacks that ambush the ML pipeline. This paper demonstrates the feasibility of an adversarial attack called the Cosine Similarity Label Manipulation (CSLM) which is geared toward compromising training labels for ML-based NIDS. The paper develops two versions of CSLM attacks: Minimum CSLM (Min-CSLM) and Maximum CSLM (Max-CSLM). We demonstrate the attacks' efficacy towards single and multi-controller Software-defined Network (SDN) setups. Results indicate that the proposed attacks provide substantial deterioration of classifier performance in single SDNs, specifically, those that utilize Random Forests (RF), which deteriorate \u0000<inline-formula><tex-math>$approx$</tex-math></inline-formula>\u000050% under Min-CSLM attacks, and Support Vector Machines (SVM), which undergo \u0000<inline-formula><tex-math>$approx$</tex-math></inline-formula>\u000060% deterioration from a Max-CSLM attack. We also note that RF, SVM, and Multi-layer Perceptron (MLP) classifiers are also extensively vulnerable to these attacks in Multi-controller SDN setups (MSDN) as they incur the most observed utility deterioration. MLP-based uniform MSDNs incur the most deterioration under both proposed CSLM attacks with \u0000<inline-formula><tex-math>$approx$</tex-math></inline-formula>\u000028% decrease in performance, while SVM and RF-based variable MSDNs incur the most deterioration under both CSLM attacks with \u0000<inline-formula><tex-math>$approx$</tex-math></inline-formula>\u000030% and \u0000<inline-formula><tex-math>$approx$</tex-math></inline-formula>\u0000 35% decrease in performance, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"252-262"},"PeriodicalIF":6.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Energy-Efficient Federated Learning Through UAV Edge Under Location Uncertainties","authors":"Chen Wang;Xiao Tang;Daosen Zhai;Ruonan Zhang;Nurzhan Ussipov;Yan Zhang","doi":"10.1109/TNSE.2024.3489554","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3489554","url":null,"abstract":"Federated Learning (FL) and Mobile Edge Computing (MEC) technologies alleviate the burden of deploying artificial intelligence (AI) on wireless devices with low computational capabilities. However, they also introduce energy consumption challenges in FL model training and data processing. In this paper, we employ Unmanned Aerial Vehicles (UAVs) to collect data from wireless devices and carry edge servers to assist the central server located at the base station in training FL model. We also consider the deviation of UAVs' locations to address its impact on network performance. Specifically, we formulate a robust joint optimization problem to minimize the energy consumption of UAVs, considering the computational resources, transmit power, transmission time, and FL model accuracy. Moreover, Gaussian-distributed uncertainties caused by deviation in UAV locations result in probabilistic constraints on data offloading. We initially employ the Bernstein-type inequality (BTI) to transform probabilistic constraints into deterministic forms. Subsequently, we adopt the Block Coordinate Descent (BCD) to separate the problem into three subproblems. Simulation results demonstrate a significant reduction in energy consumption and superiority in robustness.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"223-236"},"PeriodicalIF":6.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Interventional Causal Structure Discovery Over Graphical Models With Convergence and Optimality Guarantees","authors":"Chengbo Qiu;Kai Yang","doi":"10.1109/TNSE.2024.3487301","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3487301","url":null,"abstract":"Learning causal structure from sampled data is a fundamental problem with applications in various fields, including healthcare, machine learning and artificial intelligence. Traditional methods predominantly rely on observational data, but there exist limits regarding the identifiability of causal structures with only observational data. Interventional data, on the other hand, helps establish a cause-and-effect relationship by breaking the influence of confounding variables. It remains to date under-explored to develop a mathematical framework that seamlessly integrates both observational and interventional data in causal structure learning. Furthermore, existing studies often focus on centralized approaches, necessitating the transfer of entire datasets to a single server, which lead to considerable communication overhead and heightened risks to privacy. To tackle these challenges, we develop a \u0000<bold>b</b>\u0000i\u0000<bold>l</b>\u0000evel p\u0000<bold>o</b>\u0000lynomial \u0000<bold>o</b>\u0000pti\u0000<bold>m</b>\u0000ization (Bloom) framework. Bloom not only provides a powerful mathematical modeling framework, underpinned by theoretical support, for causal structure discovery from both interventional and observational data, but also aspires to an efficient causal discovery algorithm with convergence and optimality guarantees. We further extend Bloom to a distributed setting to reduce the communication overhead and mitigate data privacy risks. It is seen through experiments on both synthetic and real-world datasets that Bloom markedly surpasses other leading learning algorithms.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"156-172"},"PeriodicalIF":6.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in Mobile Edge Computing Network","authors":"Kuan Jia;Dingcheng Yang;Yapeng Wang;Tianyun Shui;Chenji Liu","doi":"10.1109/TNSE.2024.3488839","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3488839","url":null,"abstract":"This paper considers a patrol inspection scenario where multiple autonomous aerial vehicles (AAVs) are adopted to traverse multiple predetermined cruise points for data collection. The AAVs are connected to cellular networks and they would offload the collected data to the ground base stations (GBSs) for data processing within the constrained duration. This paper proposes a balanced task assignment strategy among patrol AAVs and an energy-efficient trajectory design method. Through jointly optimizing the cruise point assignment, communication scheduling, computational allocation, and AAV trajectory, a novel solution can be obtained to balance the multiple AAVs' task completion time and minimize the total energy consumption. Firstly, we propose a novel clustering method that considers geometry topology, communication rate, and offload volume; it can determine each AAV's cruise points and balance the AAVs' patrol task. Secondly, a hybrid Time-Energy traveling salesman problem is formulated to analyze the cruise point traversal sequence, and the energy-efficient AAV trajectory can be designed by adopting the successive convex approximation (SCA) technique and block coordinate descent (BCD) scheme. The numerical results demonstrate that the proposed balanced task assignment strategy can efficiently balance the multiple AAVs' tasks. Moreover, the min-max task completion time and total energy consumption performance of the proposed solution outperform that of the current conventional approach.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"210-222"},"PeriodicalIF":6.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Robust Multi-View Clustering via Graph-Oriented High-Order Correlations Learning","authors":"Wenzhe Liu;Jiongcheng Zhu;Huibing Wang;Yong Zhang","doi":"10.1109/TNSE.2024.3485646","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3485646","url":null,"abstract":"Multi-view clustering aims to partition data into corresponding clusters by leveraging features from various views to reveal the underlying structure of the data fully. However, existing multi-view clustering methods, particularly graph-based techniques, face two main issues: 1) They often construct similarity matrices directly from low-quality and inflexible graphs, resulting in inadequate fusion of multi-view information and impacting clustering performance; 2) Most methods focus only on consensus or pairwise associations between views, neglecting more complex higher-order correlations among multiple views, which limits improvements in clustering performance. To address these issues, we propose a novel multi-view clustering method called Robust Multi-View Clustering via Graph-Oriented High-Order Correlations Learning (GHCL). GHCL first learns latent embeddings for each view and stacks these embeddings into a third-order tensor. Then, Tucker decomposition and regularization constraints are applied to optimize the tensor and error terms, producing high-quality denoised graphs. Additionally, GHCL introduces an adaptive confidence mechanism that integrates the learned similarity matrix and consensus representation into a unified step, enhancing multi-view information fusion and clustering effectiveness. Extensive experiments demonstrate that GHCL significantly outperforms current state-of-the-art techniques on multiple datasets. It effectively integrates multi-view information and captures higher-order correlations between views, improving clustering accuracy and robustness in handling complex data, thereby showcasing its practical value in multi-view data analysis.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"559-570"},"PeriodicalIF":6.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Incorporating Mobility Prediction in Handover Procedure for Frequent-Handover Mitigation in Small-Cell Networks","authors":"Syed Maaz Shahid;Jee-Hyeon Na;Sungoh Kwon","doi":"10.1109/TNSE.2024.3487415","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3487415","url":null,"abstract":"Small cells are deployed in high-density environments to provide additional capacity and improve network coverage, supporting high-speed, high-quality mobile broadband services. However, the deployment of small cells increases the impact of user mobility on handover performance. Trends in the different movements of users at the edge of small cells lead to an excessive number of unnecessary handovers. Since user mobility is not purely random, and the overlapping coverage areas of small cells are very limited, handover management in small cells is direction-dependent. This paper proposes a handover algorithm incorporating user mobility information into the handover procedure to mitigate frequent handovers in a small-cell network. The proposed algorithm observes the pattern in the reference signal received power (RSRP) of a candidate target cell during the time to trigger to detect the change in the users' movements. Based on the RSRP pattern, the algorithm makes an optimal handover decision by selecting a target cell in the user's path. The proposed algorithm does not require information on users' previous movements because A3 event-based measurement reporting tracks user mobility. Via simulations, we show that the proposed algorithm reduces the number of handovers without sacrificing the network throughput in different network environments and performs satisfactorily in high-shadowing environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"186-197"},"PeriodicalIF":6.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}