IEEE Transactions on Network Science and Engineering最新文献_第10页

A Unified Software-Defined Autonomous Vehicle Network and Urban Congestion Prediction Method 一种统一的软件定义自动驾驶汽车网络及城市拥堵预测方法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-18 DOI: 10.1109/TNSE.2025.3553028

Lu Yang;Jiujun Cheng;Yue Zhao;Zhangkai Ni;Qichao Mao;Shangce Gao

{"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}

引用次数: 0

Impact of Fake Agents on Information Cascades 虚假代理人对信息级联的影响

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-18 DOI: 10.1109/TNSE.2025.3550459

Pawan Poojary;Randall Berry

{"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}

引用次数: 0

DM-FedMF: A Recommendation Model of Federated Matrix Factorization With Detection Mechanism 带检测机制的联邦矩阵分解推荐模型DM-FedMF

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-17 DOI: 10.1109/TNSE.2025.3551923

Xiaoyao Zheng;Xianmin Jia;Xiongchao Cheng;Wenxuan He;Liping Sun;Liangmin Guo;Qingying Yu;Yonglong Luo

{"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}

引用次数: 0

TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models 时变指数族分布图形模型的模型选择

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-17 DOI: 10.1109/TNSE.2025.3551767

Juan Liu;Guofeng Mei;Yuanqing Xia;Xiaoqun Wu;Jinhu Lü

{"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}

引用次数: 0

Dynamic Event-Triggered Resilient Filtering for Networked 2D FMLSS Systems: Tackling Bit Flips and Asynchronous Delays 网络二维FMLSS系统的动态事件触发弹性滤波：处理位翻转和异步延迟

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-16 DOI: 10.1109/TNSE.2025.3571001

Yu Chen;Chunyan Han;Juanjuan Xu;Wei Wang

{"title":"Dynamic Event-Triggered Resilient Filtering for Networked 2D FMLSS Systems: Tackling Bit Flips and Asynchronous Delays","authors":"Yu Chen;Chunyan Han;Juanjuan Xu;Wei Wang","doi":"10.1109/TNSE.2025.3571001","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3571001","url":null,"abstract":"In this paper, the dynamic event-triggered filtering problem is investigated for a class of two-dimensional (2D) Fornasini-Marchesini local state-space (FMLSS) delayed systems under binary encoding-decoding schemes with probabilistic bit flips. To reduce unnecessary communications and computations in complex network systems, alleviate network energy consumption, and optimize the use of network resources, a novel dynamic event-triggered mechanism with bidirectional evolutionary characteristics is proposed. To enhance the reliability of digital communication, a binary encoding-decoding scheme is employed, considering the scenario in which transmitted binary bits may be flipped in a noisy memoryless symmetric channel. To leverage delayed decoded measurements, a measurement reconstruction approach is introduced. Subsequently, a recursive resilient filtering framework is developed to mitigate the effects of event-triggering errors, encoding errors, and bit-flip errors on filtering accuracy. The filter gain parameter is obtained by minimizing an upper bound on the filtering error covariance. Furthermore, through rigorous mathematical analysis, the monotonicity of filtering performance with respect to triggering parameters is discussed. Finally, the feasibility of the designed filtering framework is verified through a case simulation.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"4255-4274"},"PeriodicalIF":7.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891236","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}

引用次数: 0

Information Equilibrium Maximization Problem in Social Networks Based on Entropy 基于熵的社会网络信息均衡最大化问题

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-16 DOI: 10.1109/TNSE.2025.3571165

Runzhi Li;Jianming Zhu;Guoqing Wang

{"title":"Information Equilibrium Maximization Problem in Social Networks Based on Entropy","authors":"Runzhi Li;Jianming Zhu;Guoqing Wang","doi":"10.1109/TNSE.2025.3571165","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3571165","url":null,"abstract":"Personal cognition, product advertising and social recommendations are important factors to develop brand preferences, which directly influence consumer purchasing behaviors. A diversified brand preference landscape is conductive to preventing market monopolization, thus promoting healthier market competition. The entry of a new brand enhances market diversity and contributes to equilibrium of consumers' brand preferences. The new entrant stimulates competitive responses from incumbent brands because of catfish effect. Then market shares undergo redistribution as all brands increase their operational vitality in response to the competitive pressure. To select <inline-formula><tex-math>$k$</tex-math></inline-formula> users in a social network as advertisers of a new brand, this paper proposes the information equilibrium maximization (IEM) problem, and proves that the IEM is NP-hard, computing the objective function is #P-hard, and the objective function is neither modular nor monotonic. Then the entropy-based equilibrium degree maximization (EEDM) algorithm is proposed. In experiments, based on three methods of selecting seed nodes, E_qedm shows its superiority. It has strong robustness when the size of seedsets is large enough, and activation probability and update probability are more than 0.5. Besides, the number of initial preferences has little influence on the performance of E_qedm.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"4287-4298"},"PeriodicalIF":7.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891170","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}

引用次数: 0

Staying Fresh: Efficient Algorithms for Timely Social Information Distribution 保持新鲜：社会信息及时分发的有效算法

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-16 DOI: 10.1109/TNSE.2025.3571129

Songhua Li;Lingjie Duan

{"title":"Staying Fresh: Efficient Algorithms for Timely Social Information Distribution","authors":"Songhua Li;Lingjie Duan","doi":"10.1109/TNSE.2025.3571129","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3571129","url":null,"abstract":"In location-based social networks (LBSNs), users sense urban point-of-interest (PoI) information in the vicinity and share such information with friends in online social networks. Given users' limited social connections and severe lags in disseminating fresh PoI to all, major LBSNs aim to enhance users' social PoI sharing by selecting <inline-formula><tex-math>$k$</tex-math></inline-formula> out of <inline-formula><tex-math>$m$</tex-math></inline-formula> users as hotspots and broadcasting their fresh PoI information to the entire user community. This motivates us to study a new combinatorial optimization problem that involves the interplay between an urban sensing network and an online social network. We prove that this problem is NP-hard and also renders existing approximation solutions not viable. Through analyzing the interplay effects between the two networks, we successfully transform the involved PoI-sharing process across two networks to matrix computations for deriving a closed-form objective to hold desirable properties (e.g., submodularity and monotonicity). This finding enables us to develop a polynomial-time algorithm that guarantees a (<inline-formula><tex-math>$1-frac{m-2}{m}(frac{k-1}{k})^{k}$</tex-math></inline-formula>) approximation of the optimum. Furthermore, we allow each selected user to move around and sense more PoI information to share and propose an augmentation-adaptive algorithm with decent performance guarantees. Finally, our theoretical results are corroborated by our simulation findings using both synthetic and real-world datasets.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"4275-4286"},"PeriodicalIF":7.9,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891091","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}

引用次数: 0

Mitigating Evasion Attacks in Federated Learning Based Signal Classifiers 基于联邦学习的信号分类器规避攻击研究

IF 7.9 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-14 DOI: 10.1109/TNSE.2025.3566954

Su Wang;Rajeev Sahay;Adam Piaseczny;Christopher G. Brinton

{"title":"Mitigating Evasion Attacks in Federated Learning Based Signal Classifiers","authors":"Su Wang;Rajeev Sahay;Adam Piaseczny;Christopher G. Brinton","doi":"10.1109/TNSE.2025.3566954","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3566954","url":null,"abstract":"Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversarial attacks. These adversarial attacks mislead the ML model training process, damaging ML models across the network and leading to lower SC performance. In this work, we seek to mitigate model poisoning adversarial attacks on FL-based SC by proposing the Underlying Server Defense of Federated Learning (USD-FL). Unlike existing server-driven defenses, USD-FL does not rely on perfect network information, i.e., knowing the quantity of adversaries, the adversarial attack architecture, or the start time of the adversarial attacks. Our proposed USD-FL methodology consists of deriving logits for devices' ML models on a reserve dataset, comparing pair-wise logits via 1-Wasserstein distance and then determining a time-varying threshold for adversarial detection. As a result, USD-FL effectively mitigates model poisoning attacks introduced in the FL network. Specifically, when baseline server-driven defenses do have perfect network information, USD-FL outperforms them by (i) improving final ML classification accuracies by at least 6%, (ii) reducing false positive adversary detection rates by at least 10%, and (iii) decreasing the total number of misclassified signals by over 8%. Moreover, when baseline defenses do not have perfect network information, we show that USD-FL achieves accuracies of approximately 74.1% and 62.5% in i.i.d. and non-i.i.d. settings, outperforming existing server-driven baselines, which achieve 52.1% and 39.2% in i.i.d. and non-i.i.d. settings, respectively.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 5","pages":"3933-3947"},"PeriodicalIF":7.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891122","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}

引用次数: 0

LEO Satellite Assisted Edge Computing With Latency and Energy Optimization 低轨道卫星辅助边缘计算的时延和能量优化

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-14 DOI: 10.1109/TNSE.2025.3551273

Ao Li;Ting Zhou;Tianheng Xu;Yuling Ouyang;Honglin Hu;Celimuge Wu

{"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}

引用次数: 0

Joint Request Offloading and Resource Allocation for Long-Term Utility Optimization in Collaborative Edge Inference With Time-Coupled Resources 时间耦合资源协同边缘推理中长期效用优化的联合请求卸载和资源分配

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-14 DOI: 10.1109/TNSE.2025.3551148

Jiale Huang;Jigang Wu;Yalan Wu;Jiaxin Wu

{"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}

引用次数: 0