IEEE Transactions on Network Science and Engineering最新文献

{"title":"Privacy-Preserving Graph Inference Network for Multi-Entity Wind Power Forecast: A Federated Learning Approach","authors":"Xinxin Long;Yizhou Ding;Yuanzheng Li;Yang Li;Liang Gao;Zhigang Zeng","doi":"10.1109/TNSE.2025.3547227","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3547227","url":null,"abstract":"Data sharing is considered by many wind farm stakeholders as the cause of privacy issues and further financial risks, despite its potential to enhance the accuracy of multi-entity wind power forecasting (MWPF). Federated learning (FL) serves as a possible solution to preserve the privacy in MWPF, while the existing FL-based methods still struggle to obtain accurate prediction due to the intricate spatial dependencies and heterogeneous temporal dependencies. In response to these two challenges, this paper proposes a collaborative privacy-preserving framework (CPLF) for MWPF. Within the CPLF, a graph learning-based local model named graph inference network (GIN) is developed to learn local features and obtain the global ones through aggregation. In terms of the spatial dependencies, a structure-independent dynamic graph inference (SiDGI) block is designed to extract spatial features via learnable directed graph representation. Regarding the heterogeneous temporal dependencies, the GIN, with its encoder-decoder to distill general temporal pattern, is trained following a customized FL procedure to effectively extract entity-specific temporal features. This customization can mitigate the communication burden and reverse-engineer risks while yielding improvements in MWPF accuracy. Finally, the extensive experiments are implemented based on two datasets collected from the Northwest and Southeast of California. The superiority of the proposed privacy-preserving MWPF method is verified compared with some classical methods. Specially, for graph attention, MWPF achieves 6.8% and 14.9% average improvements in mean absolute percentage error (MAPE).","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2428-2444"},"PeriodicalIF":6.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492341","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":"DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices","authors":"Hai Xue;Yun Xia;Neal N. Xiong;Di Zhang;Songwen Pei","doi":"10.1109/TNSE.2025.3550251","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3550251","url":null,"abstract":"Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (<inline-formula><tex-math>$U_{text{server}}$</tex-math></inline-formula>, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% <inline-formula><tex-math>$U_{text{server}}$</tex-math></inline-formula> on average with different ES computation capacities.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2549-2565"},"PeriodicalIF":6.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492257","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":"Accelerated Optimized Topology Design in Affine Formation Control Using ADMM","authors":"Yumeng Wang;Qingkai Yang;Fan Xiao;Hao Fang;Jie Chen","doi":"10.1109/TNSE.2025.3552979","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3552979","url":null,"abstract":"This paper studies the problem of topology design for activating affine formation control schemes. The affine formation control exhibits its unique feature as it relies on the stress matrix to dynamically maneuver the whole formation by controlling a small number of agents. Network properties of interest for this design problem generally give rise to optimization formulations within the framework of mixed-integer semidefinite programming (MISDP), resulting in computational inefficiency and NP-hardness. Firstly, to avoid introducing binary variables, the optimization of communication cost is modeled as an <inline-formula><tex-math>$l_{1}$</tex-math></inline-formula>-regularized network sparsity problem. In this way, an optimized topology design method accelerated by the alternating direction method of multipliers (ADMM) is proposed to obtain the stress matrix with low communication cost, fast convergence speed and high tolerance to time-delay. Furthermore, addressing scenarios irrespective of whether the minimum eigenvalue of the stress matrix is prescribed, we propose two enhanced ADMM-based algorithms with closed-form solutions. This is achieved through the transformation of semi-definite constraints in the subproblem into equality constraints. Finally, comparative simulations demonstrate the accelerated effects of the proposed scheme, showcasing its effectiveness in interaction topology construction and optimization for large-scale networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2694-2707"},"PeriodicalIF":6.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492419","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":"Quantum-Resistant Secure Communication Protocol for Digital Twin-Enabled Context-Aware IoT-Based Healthcare Applications","authors":"Basudeb Bera;Ashok Kumar Das;Biplab Sikdar","doi":"10.1109/TNSE.2025.3553044","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3553044","url":null,"abstract":"Digital Twins (DTs) play a crucial role in context-aware Internet of Things (IoT) applications within the healthcare sector, including the industrial healthcare domain, by facilitating the continuous sharing of sensitive and confidential patient data from physical objects in real time. This shared data is essential for treatment planning and decision-making processes, often being accessed remotely by authorized users. However, traditional security mechanisms, which rely on the integer factorization problem (IFP) and the elliptic curve discrete logarithm problem (ECDLP), are vulnerable to quantum attacks using algorithms like Shor's, posing significant risks to data protection. As a result, the healthcare sector faces several security challenges, including the vulnerability of sensitive patient data to cyberattacks, quantum threats, the risk of unauthorized access to medical devices and IoT systems, and the increasing sophistication of cybercriminals exploiting weak authentication methods. To address these issues, we propose a quantum-resistant protocol that safeguards data privacy in DT-enabled IoT healthcare applications, ensures secure transmission of information, maintains patient trust, supports long-term data confidentiality, and protects medical devices and IoT systems from potential breaches. By employing lattice-based cryptographic techniques, particularly the ring learning with errors (RLWE) problem, the proposed scheme effectively addresses contemporary security challenges, including those posed by quantum computing. Real-time experiments conducted on Raspberry Pi 4 devices, along with computational overhead analysis, demonstrate the protocol's efficiency. Additionally, formal security validation using the Scyther tool and security analysis with the RoR model reinforce the robustness of the proposed protocol. A comprehensive comparative evaluation against existing schemes highlights its lightweight, scalable, and efficient nature. Furthermore, performance evaluations in the context of unknown attacks show that the proposed scheme significantly outperforms current alternatives in terms of effectiveness.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2722-2738"},"PeriodicalIF":6.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492408","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":"A Unified Software-Defined Autonomous Vehicle Network and Urban Congestion Prediction Method","authors":"Lu Yang;Jiujun Cheng;Yue Zhao;Zhangkai Ni;Qichao Mao;Shangce Gao","doi":"10.1109/TNSE.2025.3553028","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3553028","url":null,"abstract":"Urban traffic congestion is worsening and accurate traffic congestion prediction is essential to address this issue. Current studies mainly concentrate on manned vehicles, overlooking the burgeoning traffic flow that includes both manned and autonomous vehicles. While road infrastructures and autonomous vehicles could alleviate congestion through information exchange, current infrastructure and vehicle diversity hinder effective data collection and management. This paper proposes a unified Software-Defined Autonomous Vehicle Network (SDAVN) to consistently compute traffic parameters such as average velocity, traffic flow, and occupancy using real-time mobility data from autonomous vehicles and connected manned vehicles. Additionally, we propose an effective SDAVN congestion prediction method featuring a Transformer-based traffic parameter prediction module and a congestion detection module employing an extended Spatio-Temporal Self-Organizing Mapping (STSOM). We optimize the 2D SOM to a 3D model to learn more effectively spatio-temporal characteristics. Furthermore, we introduce an asymmetric loss function to address the imbalance between congested and uncongested samples. Experimental results demonstrate the superior long-term congestion prediction performance of our method compared to existing approaches at both road and lane levels across traditional traffic datasets and simulations of real automated driving environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2708-2721"},"PeriodicalIF":6.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492253","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":"Impact of Fake Agents on Information Cascades","authors":"Pawan Poojary;Randall Berry","doi":"10.1109/TNSE.2025.3550459","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3550459","url":null,"abstract":"In online markets, agents often learn from other's actions in addition to their private information. Such observational learning can lead to <italic>herding</i> or <italic>information cascades</i> in which agents eventually ignore their private information and “follow the crowd”. Models for such cascades have been well studied for Bayes-rational agents that arrive sequentially and choose pay-off optimal actions. This paper additionally considers the presence of <italic>fake agents</i> that take a fixed action in order to influence subsequent rational agents towards their preferred action. We characterize how the fraction of fake agents impacts the behavior of rational agents. Our model results in a Markov chain with a countably infinite state space, for which we give an iterative method to compute an agent's chances of herding and its welfare. Our result shows a counter-intuitive phenomenon: there exist infinitely many scenarios where an increase in the fraction of fake agents reduces the chances of their preferred outcome. Moreover, this increase causes a significant improvement in the welfare of every rational agent. Hence, this increase is not only counter-productive for the fake agents but is also beneficial to the rational agents.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2593-2605"},"PeriodicalIF":6.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492405","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":"DM-FedMF: A Recommendation Model of Federated Matrix Factorization With Detection Mechanism","authors":"Xiaoyao Zheng;Xianmin Jia;Xiongchao Cheng;Wenxuan He;Liping Sun;Liangmin Guo;Qingying Yu;Yonglong Luo","doi":"10.1109/TNSE.2025.3551923","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3551923","url":null,"abstract":"Items are recommended to users by the federated recommendation system while protecting user privacy, but there is a risk of the performance of the global model being seriously affected by malicious clients through the tampering of local data and model parameters. In this paper, a federated matrix factorization recommendation model with a detection mechanism(DM-FedMF) is proposed. The experimental analysis concludes that there is a gradient difference in item preference parameters between malicious and benign clients. Accordingly, an objective function is designed to measure item preference differences as a means of identifying malicious clients on the server. Secondly, a malicious client reporting mechanism is proposed to count the reported frequency of all clients and set a threshold. Based on the number of honest clients, the list of attackers is updated. Finally, the malicious client is detected and eliminated based on the list of attackers. The other three defense algorithms are compared with two public datasets in this paper. The experimental results show that the detection mechanism can effectively defend against data poisoning attacks, category attacks, noise attacks, and sign flipping attacks, and the performance of the model's recommendations is better than that achieved by applying other defense methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2679-2693"},"PeriodicalIF":6.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492402","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":"TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models","authors":"Juan Liu;Guofeng Mei;Yuanqing Xia;Xiaoqun Wu;Jinhu Lü","doi":"10.1109/TNSE.2025.3551767","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3551767","url":null,"abstract":"The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed <inline-formula><tex-math>$L_{1}$</tex-math></inline-formula>-regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2666-2678"},"PeriodicalIF":6.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492403","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":"LEO Satellite Assisted Edge Computing With Latency and Energy Optimization","authors":"Ao Li;Ting Zhou;Tianheng Xu;Yuling Ouyang;Honglin Hu;Celimuge Wu","doi":"10.1109/TNSE.2025.3551273","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3551273","url":null,"abstract":"Low earth orbit (LEO) satellite networks hold significant promise for delivering global communication services in next-generation mobile communication networks. The integration of edge computing with LEO satellite networks enables stable and reliable communication and computation services for ground user equipment (UE). This paper proposes an LEO satellite-assisted cooperative edge computing framework, where UEs and the LEO satellite collaboratively process divisible computational tasks. A system cost function is proposed to quantify both latency and energy consumption during task execution. Building on this, we formulate an optimization problem to minimize the system cost function by optimizing offloading decisions, power control, task scheduling, local computational capacity, and LEO satellite computing resource allocation. To solve this problem, we propose a discrete whale optimization algorithm with a nonlinear convergence factor and adaptive weight (NAWOA), characterized by low computational complexity. The superiority and validity of the proposed algorithm are demonstrated via numerical simulations that compare different algorithms and computational offloading schemes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2640-2653"},"PeriodicalIF":6.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492406","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":"Joint Request Offloading and Resource Allocation for Long-Term Utility Optimization in Collaborative Edge Inference With Time-Coupled Resources","authors":"Jiale Huang;Jigang Wu;Yalan Wu;Jiaxin Wu","doi":"10.1109/TNSE.2025.3551148","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3551148","url":null,"abstract":"Extensive research on edge inference has devoted in optimizing service performance for users. However, recent studies have overlooked the desired utility of application service provider (ASP), which is crucial for achieving long-term service provisioning. Besides, efficient request offloading and resource allocation are essential for optimizing long-term utility of ASP in dynamic networks with time-coupled resources. To address these issues, this paper formulates a long-term utility optimization problem in collaborative edge inference system. The objective is to maximize the long-term average utility of ASP, by jointly optimizing request offloading and resource allocation, under the time-coupled resource constraints. To solve the problem, a Lyapunov based online algorithm is proposed to decompose it into a series of one-slot deterministic problems by decoupling the time-coupled resource constraints. Only the current network states are required for one-slot problem. Then, the one-slot problem is converted into a master request offloading problem with an inner resource allocation problem. A distributed algorithm is proposed to derive the optimal decision to inner problem, while a coalition based algorithm is proposed to seek the stable solution to master problem. Experimental results show that, the proposed algorithm outperforms baseline algorithms for most cases, in terms of long-term average utility of ASP.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2622-2639"},"PeriodicalIF":6.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492412","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}