Pub Date : 2025-11-19DOI: 10.1109/TNSE.2025.3634598
Jing Zhang;Fei Shen;Feng Yan;Jie Li
Edge computing provides low-latency computational services for task offloading in vehicular networks. However, challenges such as dynamic transmission rates, resource limitations, and information-sharing constraints impede efficient offloading. Few studies address these issues concurrently in designing dynamic offloading strategies, often resulting in sub-optimal system utility. This paper aims to achieve efficient vehicular task offloading via an idleness-aware edge server (ES) from a game theory perspective. We propose a Gated Recurrent Unit (GRU) prediction model with an attention mechanism to guide vehicles to the nearest idle ES. The offloading decision process is modeled as a stochastic game, proving the existence of a Nash equilibrium (NE). Additionally, we model it as a multi-agent partially observable Markov decision process (POMDP) to account for limited information access among vehicles. To solve the POMDP and achieve near-optimal NE, we introduce a Multi-Agent Reinforcement Learning-based Task Offloading (MATO) algorithm, combining a Differentiable Neural Computer (DNC) and an Advantageous Actor-Critic (A2C) framework. The DNC’s external memory stores structured representations of past information, enabling deeper exploration of the strategy space. Adjusting the reward representation enhances training efficiency. Experimental results driven by real-world datasets demonstrate that MATO effectively improves the computing offloading utility while increasing the convergence speed compared to existing schemes.
{"title":"Multi-Agent Reinforcement Learning Based Idle-Aware Task Offloading in Dynamic Vehicular Networks With Partial Information","authors":"Jing Zhang;Fei Shen;Feng Yan;Jie Li","doi":"10.1109/TNSE.2025.3634598","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3634598","url":null,"abstract":"Edge computing provides low-latency computational services for task offloading in vehicular networks. However, challenges such as dynamic transmission rates, resource limitations, and information-sharing constraints impede efficient offloading. Few studies address these issues concurrently in designing dynamic offloading strategies, often resulting in sub-optimal system utility. This paper aims to achieve efficient vehicular task offloading via an idleness-aware edge server (ES) from a game theory perspective. We propose a Gated Recurrent Unit (GRU) prediction model with an attention mechanism to guide vehicles to the nearest idle ES. The offloading decision process is modeled as a stochastic game, proving the existence of a Nash equilibrium (NE). Additionally, we model it as a multi-agent partially observable Markov decision process (POMDP) to account for limited information access among vehicles. To solve the POMDP and achieve near-optimal NE, we introduce a Multi-Agent Reinforcement Learning-based Task Offloading (MATO) algorithm, combining a Differentiable Neural Computer (DNC) and an Advantageous Actor-Critic (A2C) framework. The DNC’s external memory stores structured representations of past information, enabling deeper exploration of the strategy space. Adjusting the reward representation enhances training efficiency. Experimental results driven by real-world datasets demonstrate that MATO effectively improves the computing offloading utility while increasing the convergence speed compared to existing schemes.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3499-3515"},"PeriodicalIF":7.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778195","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}
Entanglement routing selects a path to establish entanglement connections between two arbitrary nodes in quantum networks, which plays an important role in quantum communication. In quantum networks, quantum decoherence and limited network performance make it challenging to distribute entangled pairs. Many entanglement routing schemes have been proposed to solve this issue but most of them are in a centralized and synchronized manner. However, they may be infeasible in large-scale quantum networks. Therefore, in this paper, we propose a distributed and asynchronous entanglement routing scheme called DFER in which quantum nodes manage requests autonomously. The major challenge is quantum nodes have little knowledge about entangled pairs, which hinders the ability to establish fidelity guaranteed entanglement connections. To address this challenge, we develop DLFR algorithm which estimates the fidelity of end-to-end entanglement connections based on link-level fidelity and calculates link-level fidelity requirement based on the characteristic of purification. Among nodes which meet link-level fidelity requirement, we design DFPS path selection algorithm to select next hop with the highest expected throughput to distribute entangled pairs. Numerous simulation results demonstrate that DFER can efficiently distribute fidelity-guaranteed entangled pairs with high throughput.
{"title":"Distributed Entanglement Routing Scheme With Fidelity Guarantee in Quantum Networks","authors":"Jiesheng Tan;Zhonghui Li;Jian Li;Bin Liu;Nenghai Yu","doi":"10.1109/TNSE.2025.3631132","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3631132","url":null,"abstract":"Entanglement routing selects a path to establish entanglement connections between two arbitrary nodes in quantum networks, which plays an important role in quantum communication. In quantum networks, quantum decoherence and limited network performance make it challenging to distribute entangled pairs. Many entanglement routing schemes have been proposed to solve this issue but most of them are in a centralized and synchronized manner. However, they may be infeasible in large-scale quantum networks. Therefore, in this paper, we propose a distributed and asynchronous entanglement routing scheme called DFER in which quantum nodes manage requests autonomously. The major challenge is quantum nodes have little knowledge about entangled pairs, which hinders the ability to establish fidelity guaranteed entanglement connections. To address this challenge, we develop DLFR algorithm which estimates the fidelity of end-to-end entanglement connections based on link-level fidelity and calculates link-level fidelity requirement based on the characteristic of purification. Among nodes which meet link-level fidelity requirement, we design DFPS path selection algorithm to select next hop with the highest expected throughput to distribute entangled pairs. Numerous simulation results demonstrate that DFER can efficiently distribute fidelity-guaranteed entangled pairs with high throughput.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3320-3334"},"PeriodicalIF":7.9,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729368","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}
Pub Date : 2025-11-17DOI: 10.1109/TNSE.2025.3634079
Yuanyuan Zhang;Pei Wang;Jinhu Lü;Tao Zhou
Quantifying structural dissimilarities between networks is a fundamental challenge. This paper introduces a novel measure, $D^{*}$, that quantifies network dissimilarity by analyzing their augmented networks. The augmented network is constructed by adding a virtual leader node that is bidirectionally connected to all other nodes. $D^{*}$ relies on the node distance distributions of the augmented networks, which are essentially determined by the original networks’ degree distributions and sizes. This characteristic makes it a simple and robust measure, insensitive to weighting parameters, and applicable to a wide range of networks–including regular networks and networks of any size, even those containing isolated nodes. $D^{*}$ actually utilizes the second-order truncated shortest-path distance matrix and demonstrates superior performance compared to higher-order truncations. Numerical simulations show that $D^{*}$ accurately quantifies structural differences between networks while overcoming the saturation growth effect induced by increasing edge connection probabilities in random networks. The versatility of $D^{*}$ is further demonstrated by applying it to four distinct scenarios. Specifically, $D^{*}$ is effective in determining optimal correlation cutoff thresholds when constructing bio-molecular co-expression networks, in identifying disease modules in gene-disease and phenotype-disease networks, in performing clustering and layer aggregation for multilayer networks, and in distinguishing networks of different categories. Our findings enhance understanding of the construction and comparison of complex network structures.
{"title":"Quantifying Network Dissimilarity via Augmented Networks","authors":"Yuanyuan Zhang;Pei Wang;Jinhu Lü;Tao Zhou","doi":"10.1109/TNSE.2025.3634079","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3634079","url":null,"abstract":"Quantifying structural dissimilarities between networks is a fundamental challenge. This paper introduces a novel measure, <inline-formula><tex-math>$D^{*}$</tex-math></inline-formula>, that quantifies network dissimilarity by analyzing their augmented networks. The augmented network is constructed by adding a virtual leader node that is bidirectionally connected to all other nodes. <inline-formula><tex-math>$D^{*}$</tex-math></inline-formula> relies on the node distance distributions of the augmented networks, which are essentially determined by the original networks’ degree distributions and sizes. This characteristic makes it a simple and robust measure, insensitive to weighting parameters, and applicable to a wide range of networks–including regular networks and networks of any size, even those containing isolated nodes. <inline-formula><tex-math>$D^{*}$</tex-math></inline-formula> actually utilizes the second-order truncated shortest-path distance matrix and demonstrates superior performance compared to higher-order truncations. Numerical simulations show that <inline-formula><tex-math>$D^{*}$</tex-math></inline-formula> accurately quantifies structural differences between networks while overcoming the saturation growth effect induced by increasing edge connection probabilities in random networks. The versatility of <inline-formula><tex-math>$D^{*}$</tex-math></inline-formula> is further demonstrated by applying it to four distinct scenarios. Specifically, <inline-formula><tex-math>$D^{*}$</tex-math></inline-formula> is effective in determining optimal correlation cutoff thresholds when constructing bio-molecular co-expression networks, in identifying disease modules in gene-disease and phenotype-disease networks, in performing clustering and layer aggregation for multilayer networks, and in distinguishing networks of different categories. Our findings enhance understanding of the construction and comparison of complex network structures.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3566-3579"},"PeriodicalIF":7.9,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778262","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}
Federated learning (FL) facilitates decentralized machine learning by enabling participating entities to cooperatively train shared models while retaining local data ownership. To address privacy concerns inherent in distributed training, privacy-preserving mechanisms have been incorporated into FL frameworks to enhance confidentiality and protect sensitive data. However, the implementation of these privacy-enhancing techniques introduces vulnerabilities to poisoning attacks, wherein malicious actors manipulate training processes to degrade model integrity or performance. Current defense strategies rely on statistical methods to mitigate such attacks, but they lack sufficient robustness, demonstrating limited effectiveness against diverse attack types or scenarios where malicious clients exceed a small minority. To overcome these limitations, we propose RobustPPFL, a privacy-preserving federated learning framework designed to withstand multiple poisoning attack types with high resilience, even when malicious participants dominate the client population. Our approach integrates three core innovations. First, we add performance verification of client-encrypted models during the training process to detect malicious clients in-time. Second, a secure model inference protocol is proposed to enable privacy-preserving training. Last but not the least, we design a grouped verification mechanism enhanced by hierarchical aggregation rules to optimize efficiency and minimize interference in malicious client detection. We evaluate RobustPPFL through extensive experiments across diverse datasets and attack scenarios. The experimental results show that our proposed framework achieves privacy preservation and it achieves high robustness against poisoning attacks.
{"title":"RobustPPFL: A Secure and Robust Privacy-Preserving Federated Learning Framework Against Poisoning Attacks","authors":"Kaiping Xue;Jiachen Li;Rui Xue;Yingjie Xue;Jingcheng Zhao","doi":"10.1109/TNSE.2025.3632902","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3632902","url":null,"abstract":"Federated learning (FL) facilitates decentralized machine learning by enabling participating entities to cooperatively train shared models while retaining local data ownership. To address privacy concerns inherent in distributed training, privacy-preserving mechanisms have been incorporated into FL frameworks to enhance confidentiality and protect sensitive data. However, the implementation of these privacy-enhancing techniques introduces vulnerabilities to poisoning attacks, wherein malicious actors manipulate training processes to degrade model integrity or performance. Current defense strategies rely on statistical methods to mitigate such attacks, but they lack sufficient robustness, demonstrating limited effectiveness against diverse attack types or scenarios where malicious clients exceed a small minority. To overcome these limitations, we propose RobustPPFL, a privacy-preserving federated learning framework designed to withstand multiple poisoning attack types with high resilience, even when malicious participants dominate the client population. Our approach integrates three core innovations. First, we add performance verification of client-encrypted models during the training process to detect malicious clients in-time. Second, a secure model inference protocol is proposed to enable privacy-preserving training. Last but not the least, we design a grouped verification mechanism enhanced by hierarchical aggregation rules to optimize efficiency and minimize interference in malicious client detection. We evaluate RobustPPFL through extensive experiments across diverse datasets and attack scenarios. The experimental results show that our proposed framework achieves privacy preservation and it achieves high robustness against poisoning attacks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3351-3368"},"PeriodicalIF":7.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778281","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}
Pub Date : 2025-11-14DOI: 10.1109/TNSE.2025.3632547
Zhongqiang Zhang;Shuhang Zhang;Haoyong Li;Guangming Shi;Jiayin Xue;Bin Li
Remote sensing images play a crucial and indispensable role in many fields such as environmental monitoring and geological disaster detection. With the advancement of satellite remote sensing acquisition technology, the remote sensing data shows explosive growth. However, the current communication bandwidth of the satellite-to-ground channel is difficult to meet the requirements for massive remote sensing data transmission. To this end, this paper proposes an elastic coding and decoding method for remote sensing image semantic transmission. The proposed transmission method includes a global-local feature extraction module, a key semantic feature selection module, a joint source channel coding module, a decoding module, and an analysis module. The global-local feature extraction module can effectively extract global context features and local detailed features via multi-directional mamba block and residual block, respectively. The key semantic feature selection module can elastically select key features according to channel state signal-to-noise ratios (SNRs). The joint source channel coding and decoding modules can further improve the transmission robustness via adding different types of channel conditions. The proposed method only needs to transmit key information in remote sensing images while discarding the redundant information, which significantly improves the transmission efficiency of remote sensing images. The extensive experimental results on the NWPU-RESISC45, UCMerced-LandUse, AID, and RSSCN7 datasets demonstrate that our method obtains higher transmission accuracies and transmission efficiency than state-of-the-art methods.
{"title":"An Elastic Coding and Decoding Method for Satellite Remote Sensing Image Semantic Transmission","authors":"Zhongqiang Zhang;Shuhang Zhang;Haoyong Li;Guangming Shi;Jiayin Xue;Bin Li","doi":"10.1109/TNSE.2025.3632547","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3632547","url":null,"abstract":"Remote sensing images play a crucial and indispensable role in many fields such as environmental monitoring and geological disaster detection. With the advancement of satellite remote sensing acquisition technology, the remote sensing data shows explosive growth. However, the current communication bandwidth of the satellite-to-ground channel is difficult to meet the requirements for massive remote sensing data transmission. To this end, this paper proposes an elastic coding and decoding method for remote sensing image semantic transmission. The proposed transmission method includes a global-local feature extraction module, a key semantic feature selection module, a joint source channel coding module, a decoding module, and an analysis module. The global-local feature extraction module can effectively extract global context features and local detailed features via multi-directional mamba block and residual block, respectively. The key semantic feature selection module can elastically select key features according to channel state signal-to-noise ratios (SNRs). The joint source channel coding and decoding modules can further improve the transmission robustness via adding different types of channel conditions. The proposed method only needs to transmit key information in remote sensing images while discarding the redundant information, which significantly improves the transmission efficiency of remote sensing images. The extensive experimental results on the NWPU-RESISC45, UCMerced-LandUse, AID, and RSSCN7 datasets demonstrate that our method obtains higher transmission accuracies and transmission efficiency than state-of-the-art methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3485-3498"},"PeriodicalIF":7.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778328","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}
Pub Date : 2025-11-14DOI: 10.1109/TNSE.2025.3632865
Bingqing Ren;Peng Yang;Miao Du;Dongmei Yang
Multi-access edge computing (MEC) is an important technology to accelerate the response speed of computation-intensive and delay-sensitive tasks, which promotes the development of Artificial Intelligence (AI) and Internet of Things (IoT). Federated learning (FL) over mobile devices, coupled with MEC to build an intelligent network, can avoid the risk of privacy leakage by keeping the sensitive information of each agent locally. However, there exist some problems in implementing FL over mobile devices, such as the shortage of edge bandwidth and computing resources. In addition, network dynamics and the heterogeneity of mobile devices need to be considered. To address these issues, we propose a heterogeneous Stackelberg game approach based on deep reinforcement learning (DRL) to achieve the desired trade-off between computing and communication in the FL system, called Energy Efficient Heterogeneous Federated Learning (EEHFL). Specifically, EEHFL designs a new two-stage Stackelberg game approach based on the heterogeneous FL architecture with convergence guarantee targeting efficient energy, which is modeled separately. Furthermore, DRL algorithms are introduced to solve the problem, realizing the control of heterogeneous parameters in a dynamic environment. Experimental results illustrate that compared with state-of-the-art baselines, our model achieves remarkable improvement, which demonstrates the superiority of our model on saving cost and energy consumption.
{"title":"Energy Efficient Heterogeneous Federated Learning Over Mobile Devices: A Deep Reinforcement Learning Based Stackelberg Game Approach","authors":"Bingqing Ren;Peng Yang;Miao Du;Dongmei Yang","doi":"10.1109/TNSE.2025.3632865","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3632865","url":null,"abstract":"Multi-access edge computing (MEC) is an important technology to accelerate the response speed of computation-intensive and delay-sensitive tasks, which promotes the development of Artificial Intelligence (AI) and Internet of Things (IoT). Federated learning (FL) over mobile devices, coupled with MEC to build an intelligent network, can avoid the risk of privacy leakage by keeping the sensitive information of each agent locally. However, there exist some problems in implementing FL over mobile devices, such as the shortage of edge bandwidth and computing resources. In addition, network dynamics and the heterogeneity of mobile devices need to be considered. To address these issues, we propose a heterogeneous Stackelberg game approach based on deep reinforcement learning (DRL) to achieve the desired trade-off between computing and communication in the FL system, called Energy Efficient Heterogeneous Federated Learning (EEHFL). Specifically, EEHFL designs a new two-stage Stackelberg game approach based on the heterogeneous FL architecture with convergence guarantee targeting efficient energy, which is modeled separately. Furthermore, DRL algorithms are introduced to solve the problem, realizing the control of heterogeneous parameters in a dynamic environment. Experimental results illustrate that compared with state-of-the-art baselines, our model achieves remarkable improvement, which demonstrates the superiority of our model on saving cost and energy consumption.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3534-3550"},"PeriodicalIF":7.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778196","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}
Pub Date : 2025-11-14DOI: 10.1109/TNSE.2025.3632560
Zijian Bao;Debiao He;Qi Feng;Min Luo
Group signatures provide anonymity for signers while allowing a group manager to reveal identities when necessary. However, traditional schemes lack mechanisms to automatically enforce protocol compliance, requiring trusted authorities to detect and penalize violations. This paper introduces Self-Enforcing Group Signatures (SEGS), a novel cryptographic primitive that maintains the anonymity of group signatures while incorporating automatic self-enforcement properties. SEGS ensures that if a group member signs two messages that share the same address but have different payloads—referred to as colliding messages—then anyone can efficiently extract the member's secret signing key from the two signatures without trusted intervention. We demonstrate SEGS's practical utility through a privacy-preserving voting application that prevents double voting while maintaining anonymity. Experimental evaluation on computational cost, signature size, and smart contract performance confirms the practicality of our SEGS and voting system. Our work bridges the gap between passive detection and active enforcement in anonymous authentication systems, offering a new direction for self-enforcing cryptographic protocols.
组签名为签名者提供匿名性，同时允许组管理器在必要时显示身份。然而，传统方案缺乏自动执行协议遵从性的机制，需要可信的权威机构来检测和惩罚违规行为。本文介绍了一种新的加密原语SEGS (self-enforcement Group signature)，它在保持群签名的匿名性的同时结合了自动自我执行的特性。SEGS确保，如果一个组成员签署了共享相同地址但具有不同有效负载的两条消息（称为冲突消息），那么任何人都可以在没有可信干预的情况下有效地从两个签名中提取成员的秘密签名密钥。我们通过一个保护隐私的投票应用程序来演示SEGS的实际用途，该应用程序可以在保持匿名的同时防止重复投票。对计算成本、签名大小和智能合约性能的实验评估证实了我们的SEGS和投票系统的实用性。我们的工作弥合了匿名认证系统中被动检测和主动执行之间的差距，为自我执行加密协议提供了新的方向。
{"title":"SEGS: Self-Enforcing Group Signature for Voting Systems","authors":"Zijian Bao;Debiao He;Qi Feng;Min Luo","doi":"10.1109/TNSE.2025.3632560","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3632560","url":null,"abstract":"Group signatures provide anonymity for signers while allowing a group manager to reveal identities when necessary. However, traditional schemes lack mechanisms to automatically enforce protocol compliance, requiring trusted authorities to detect and penalize violations. This paper introduces Self-Enforcing Group Signatures (SEGS), a novel cryptographic primitive that maintains the anonymity of group signatures while incorporating automatic self-enforcement properties. SEGS ensures that if a group member signs two messages that share the same address but have different payloads—referred to as <italic>colliding messages</i>—then anyone can efficiently extract the member's secret signing key from the two signatures without trusted intervention. We demonstrate SEGS's practical utility through a privacy-preserving voting application that prevents double voting while maintaining anonymity. Experimental evaluation on computational cost, signature size, and smart contract performance confirms the practicality of our SEGS and voting system. Our work bridges the gap between passive detection and active enforcement in anonymous authentication systems, offering a new direction for self-enforcing cryptographic protocols.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3625-3644"},"PeriodicalIF":7.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778159","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}
To better capture real-world epidemic dynamics, it is essential to develop models that incorporate diverse, realistic factors. In this study, we propose a coupled disease-information spreading model on multiplex networks that simultaneously accounts for three critical dimensions: media influence, higher-order interactions, and population mobility. This integrated framework enables a systematic analysis of synergistic spreading mechanisms under practical constraints and facilitates the exploration of effective epidemic containment strategies. Our results show that both mass media dissemination and higher-order network structures contribute to suppressing disease transmission by enhancing public awareness. However, the containment effect of higher-order interactions weakens as the order of simplices increases. We also explore the influence of subpopulation characteristics, revealing that increasing inter-subpopulation connectivity in a connected metapopulation network leads to lower disease prevalence under moderate disease transmission rates. Furthermore, guiding individuals to migrate toward less accessible or more isolated subpopulations is shown to effectively mitigate epidemic spread. These findings offer valuable insights for designing targeted and adaptive intervention strategies in complex epidemic settings.
{"title":"Modeling Coupled Epidemic-Information Dynamics via Reaction-Diffusion Processes on Multiplex Networks with Media and Mobility Effects","authors":"Guangyuan Mei;Yao Cai;Su-Su Zhang;Ying Huang;Chuang Liu;Xiu-Xiu Zhan","doi":"10.1109/TNSE.2025.3632506","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3632506","url":null,"abstract":"To better capture real-world epidemic dynamics, it is essential to develop models that incorporate diverse, realistic factors. In this study, we propose a coupled disease-information spreading model on multiplex networks that simultaneously accounts for three critical dimensions: media influence, higher-order interactions, and population mobility. This integrated framework enables a systematic analysis of synergistic spreading mechanisms under practical constraints and facilitates the exploration of effective epidemic containment strategies. Our results show that both mass media dissemination and higher-order network structures contribute to suppressing disease transmission by enhancing public awareness. However, the containment effect of higher-order interactions weakens as the order of simplices increases. We also explore the influence of subpopulation characteristics, revealing that increasing inter-subpopulation connectivity in a connected metapopulation network leads to lower disease prevalence under moderate disease transmission rates. Furthermore, guiding individuals to migrate toward less accessible or more isolated subpopulations is shown to effectively mitigate epidemic spread. These findings offer valuable insights for designing targeted and adaptive intervention strategies in complex epidemic settings.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3369-3390"},"PeriodicalIF":7.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778125","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}
Pub Date : 2025-11-13DOI: 10.1109/TNSE.2025.3632296
Muhammad Adil;Tie Qiu;Xiaobo Zhou;Prabhat Kumar;Danish Javeed
The integration of ubiquitous 5G cellular networks with deterministic Ethernet, such as Time-Sensitive Networking (TSN), is essential for future industrial applications, offering high flexibility and strict determinism. A key challenge in this integration is the dynamic mapping of TSN traffic to 5G QoS profiles, especially given the diverse QoS requirements across flows. While existing methods based on static mapping or approximations can be effective under stable conditions, they fail to adapt to fluctuating network loads and evolving QoS demands, leading to delays and inaccurate profile selection. To overcome these limitations, we propose DQMARS — a Dynamic QoS Mapping Approach with Resource Slicing. In DQMARS, 5G QoS resources are partitioned into $n$ resource slices aligned with TSN traffic types. Each resource slice is associated with multiple 5G QoS profiles and supports flexible selection based on flow-level QoS requirements at admission time. Within each slice, a Bayesian-optimized learning model leveraging feature and attention transformers is employed for dynamic mapping. This model identifies the most appropriate QoS profile for each TSN traffic flow by evaluating multiple QoS attributes, such as bandwidth, packet delay budget, and packet error rate. We evaluate DQMARS across various industrial scenarios, achieving a mapping accuracy exceeding 99% and minimal delay averaging $1.63 times 10^{-3}$ ms per traffic flow. Compared to state-of-the-art methods, our approach significantly reduces mapping delay while exhibiting superior adaptability to dynamic network conditions, making it highly suitable for time-critical industrial applications.
{"title":"Dynamic QoS Mapping in Integrated 5G-TSN Networks With Programmable Resource Slicing","authors":"Muhammad Adil;Tie Qiu;Xiaobo Zhou;Prabhat Kumar;Danish Javeed","doi":"10.1109/TNSE.2025.3632296","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3632296","url":null,"abstract":"The integration of ubiquitous 5G cellular networks with deterministic Ethernet, such as Time-Sensitive Networking (TSN), is essential for future industrial applications, offering high flexibility and strict determinism. A key challenge in this integration is the dynamic mapping of TSN traffic to 5G QoS profiles, especially given the diverse QoS requirements across flows. While existing methods based on static mapping or approximations can be effective under stable conditions, they fail to adapt to fluctuating network loads and evolving QoS demands, leading to delays and inaccurate profile selection. To overcome these limitations, we propose DQMARS — a <italic>Dynamic QoS Mapping Approach with Resource Slicing</i>. In DQMARS, 5G QoS resources are partitioned into <inline-formula><tex-math>$n$</tex-math></inline-formula> resource slices aligned with TSN traffic types. Each resource slice is associated with multiple 5G QoS profiles and supports flexible selection based on flow-level QoS requirements at admission time. Within each slice, a Bayesian-optimized learning model leveraging feature and attention transformers is employed for dynamic mapping. This model identifies the most appropriate QoS profile for each TSN traffic flow by evaluating multiple QoS attributes, such as bandwidth, packet delay budget, and packet error rate. We evaluate DQMARS across various industrial scenarios, achieving a mapping accuracy exceeding 99% and minimal delay averaging <inline-formula><tex-math>$1.63 times 10^{-3}$</tex-math></inline-formula> ms per traffic flow. Compared to state-of-the-art methods, our approach significantly reduces mapping delay while exhibiting superior adaptability to dynamic network conditions, making it highly suitable for time-critical industrial applications.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3516-3533"},"PeriodicalIF":7.9,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778128","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}
Pub Date : 2025-11-11DOI: 10.1109/TNSE.2025.3631526
Ratun Rahman;Sina Shaham;Dinh C. Nguyen
Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum machine learning (QML) offers powerful tools for effectively processing high-dimensional data, but centralized QML systems face considerable challenges, including data privacy concerns and the need for massive quantum resources at a single node. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminating the requirement for centralized quantum storage and processing.However, in real-life quantum networks, clients frequently differ in terms of hardware capabilities, circuit designs, noise levels, and how classical data is encoded or preprocessed into quantum states. These differences create inherent heterogeneity across clientsnot just in their data distributions, but also in their quantum processing behaviors. As a result, training a single global model becomes ineffective, especially when clients handle imbalanced or non-identically distributed (non-IID) data.To address this, we propose a new framework called personalized quantum federated learning (PQFL) for anomaly detection. PQFL enhances local model training at quantum clients using parameterized quantum circuits and classical optimizers, while introducing a quantum-centric personalization strategy that adapts each client's model to its own hardware characteristics and data representation. This balances local customization with global coordination.Extensive experiments show that PQFL significantly improves anomaly detection accuracy under diverse and realistic conditions. Compared to state-of-the-art methods, PQFL reduces false errors by up to 23%, and achieves gains of 24.2% in AUROC and 20.5% in AUPR, highlighting its effectiveness and scalability in practical quantum federated settings.
{"title":"Toward Personalized Quantum Federated Learning for Anomaly Detection","authors":"Ratun Rahman;Sina Shaham;Dinh C. Nguyen","doi":"10.1109/TNSE.2025.3631526","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3631526","url":null,"abstract":"Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum machine learning (QML) offers powerful tools for effectively processing high-dimensional data, but centralized QML systems face considerable challenges, including data privacy concerns and the need for massive quantum resources at a single node. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminating the requirement for centralized quantum storage and processing.However, in real-life quantum networks, clients frequently differ in terms of hardware capabilities, circuit designs, noise levels, and how classical data is encoded or preprocessed into quantum states. These differences create inherent heterogeneity across clientsnot just in their data distributions, but also in their quantum processing behaviors. As a result, training a single global model becomes ineffective, especially when clients handle imbalanced or non-identically distributed (non-IID) data.To address this, we propose a new framework called personalized quantum federated learning (PQFL) for anomaly detection. PQFL enhances local model training at quantum clients using parameterized quantum circuits and classical optimizers, while introducing a quantum-centric personalization strategy that adapts each client's model to its own hardware characteristics and data representation. This balances local customization with global coordination.Extensive experiments show that PQFL significantly improves anomaly detection accuracy under diverse and realistic conditions. Compared to state-of-the-art methods, PQFL reduces false errors by up to 23%, and achieves gains of 24.2% in AUROC and 20.5% in AUPR, highlighting its effectiveness and scalability in practical quantum federated settings.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3335-3350"},"PeriodicalIF":7.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729386","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}
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