Pub Date : 2026-01-12DOI: 10.1109/TNSE.2026.3651634
Jinyang Dong;Junqiao Li;Yucheng Li;Bolun Li;Yunan Cui;Zhitao Zhang;Wei Zhang;Jinzhe Liu
Ventilation network diagrams play a vital role in mine safety, enabling simulation, airflow control, and emergency planning. Traditional manual drawing methods are time-consuming, error-prone, and difficult to synchronize with evolving mine structures. To address this, we propose an automated framework that generates schematic ventilation diagrams from spatial models while preserving topological fidelity. The framework integrates a hierarchical node layering algorithm based on Hasse diagrams, a trunk–branch decomposition strategy for structural abstraction, and a deformation-based curve layout model inspired by water droplet geometry to enhance visual clarity and structural alignment. A 3D elevation inference module ensures semantic consistency across 2D diagrams, 3D models, and graph representations. The system further supports reactive multi-view synchronized editing, enabling coherent modifications across different representations while preserving user-defined constraints. Although developed and validated in mine ventilation, the framework is generally applicable to directed-graph–based industrial systems. Its generality is further demonstrated on a schematic exhaust pipeline case, where a tree structure rather than a network diagram provides a more suitable abstraction, highlighting that the method supports domain-adaptive abstraction guided by topological characteristics. This approach has been integrated into real-world mining operations through the “Ventilation Brain System,” offering a scalable solution for intelligent layout generation, adaptive design iteration, and responsive decision-making. By bridging spatial realism with schematic abstraction, the proposed method streamlines diagram construction and reinforces intelligent mine ventilation management.
{"title":"Implementation of Core Algorithms for Automated Mine Ventilation Network Diagram Generation","authors":"Jinyang Dong;Junqiao Li;Yucheng Li;Bolun Li;Yunan Cui;Zhitao Zhang;Wei Zhang;Jinzhe Liu","doi":"10.1109/TNSE.2026.3651634","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3651634","url":null,"abstract":"Ventilation network diagrams play a vital role in mine safety, enabling simulation, airflow control, and emergency planning. Traditional manual drawing methods are time-consuming, error-prone, and difficult to synchronize with evolving mine structures. To address this, we propose an automated framework that generates schematic ventilation diagrams from spatial models while preserving topological fidelity. The framework integrates a hierarchical node layering algorithm based on Hasse diagrams, a trunk–branch decomposition strategy for structural abstraction, and a deformation-based curve layout model inspired by water droplet geometry to enhance visual clarity and structural alignment. A 3D elevation inference module ensures semantic consistency across 2D diagrams, 3D models, and graph representations. The system further supports reactive multi-view synchronized editing, enabling coherent modifications across different representations while preserving user-defined constraints. Although developed and validated in mine ventilation, the framework is generally applicable to directed-graph–based industrial systems. Its generality is further demonstrated on a schematic exhaust pipeline case, where a tree structure rather than a network diagram provides a more suitable abstraction, highlighting that the method supports domain-adaptive abstraction guided by topological characteristics. This approach has been integrated into real-world mining operations through the “Ventilation Brain System,” offering a scalable solution for intelligent layout generation, adaptive design iteration, and responsive decision-making. By bridging spatial realism with schematic abstraction, the proposed method streamlines diagram construction and reinforces intelligent mine ventilation management.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6079-6105"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082024","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}
The application of wireless sensor networks (WSNs) in underground or underwater regions is becoming increasingly prevalent. This specific type of WSNs are referred to as wireless weak-link sensor networks(WWSNs), whose key distribution is identified as a prerequisite for ensuring network security. However, due to the fragile links of WWSNs, it is not feasible to directly transfer existing authenticated Diffie-Hellman (ADH) protocols from traditional WSNs to WWSNs. To address this challenge, we propose a disconnection-resistant authenticated Diffie-Hellman protocol (D-ADH) for key distribution in WWSNs. For mitigating the adverse impact of fragile links, we significantly reduce the number of node interactions in existing ADH protocols by having the sensor node use fixed negotiation public keys. Each sensor node only needs to receive the broadcast message of the base station on a single occasion to generate the session key. The results of simulation experiments and security analysis demonstrate that the proposed D-ADH protocol exhibits the lowest energy consumption, the longest network lifetime, the highest probability of successful key distribution and good network scalability compared to the state-of-the-art protocols, while maintaining an acceptable middle level security.
{"title":"A Disconnection-Resistant Authenticated Diffie-Hellman Protocol in Wireless Weak-Link Sensor Networks","authors":"Jia Zhang;Guanghua Liu;Chenlong Wang;Zhihao Liu;Tao Jiang","doi":"10.1109/TNSE.2026.3653104","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3653104","url":null,"abstract":"The application of wireless sensor networks (WSNs) in underground or underwater regions is becoming increasingly prevalent. This specific type of WSNs are referred to as wireless weak-link sensor networks(WWSNs), whose key distribution is identified as a prerequisite for ensuring network security. However, due to the fragile links of WWSNs, it is not feasible to directly transfer existing authenticated Diffie-Hellman (ADH) protocols from traditional WSNs to WWSNs. To address this challenge, we propose a disconnection-resistant authenticated Diffie-Hellman protocol (D-ADH) for key distribution in WWSNs. For mitigating the adverse impact of fragile links, we significantly reduce the number of node interactions in existing ADH protocols by having the sensor node use fixed negotiation public keys. Each sensor node only needs to receive the broadcast message of the base station on a single occasion to generate the session key. The results of simulation experiments and security analysis demonstrate that the proposed D-ADH protocol exhibits the lowest energy consumption, the longest network lifetime, the highest probability of successful key distribution and good network scalability compared to the state-of-the-art protocols, while maintaining an acceptable middle level security.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"6010-6026"},"PeriodicalIF":7.9,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082082","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 : 2026-01-05DOI: 10.1109/TNSE.2026.3650814
Yuhan Wang;Yinuo Du;Jian Wang;Yuan Shen
An efficient approximation of the latent soft information (SI) is a key enabler for tracking multiple targets in time-varying non-line-of-sight (NLoS) environments. Such information can be approximated by associating numerous NLoS observations with the targets’ movement, a novel paradigm that transitions from the traditional “tracking after localization” approaches to a more comprehensive “tracking with localization” method. In particular, we establish the extended states (ETs) for targets to associate with their possible measurements in multipath scenarios. The joint distribution of ETs is regarded as an approximation of the latent SI at the sensor level. The information carried by ETs is then transferred to the target level for tracking using a variational message passing approach, and subsequently fed back to the sensor level to estimate the SI for future steps. We establish a factor graph depicting the propagation of information from multi-sensor measurements to multi-target states and vice versa. Numerical results including uncrewed aerial vehicles (UAVs) validate the superior performance of our proposed algorithm compared to alternative approaches.
{"title":"Soft Information Fusion for Tracking Multiple Targets in NLoS Environments","authors":"Yuhan Wang;Yinuo Du;Jian Wang;Yuan Shen","doi":"10.1109/TNSE.2026.3650814","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3650814","url":null,"abstract":"An efficient approximation of the latent soft information (SI) is a key enabler for tracking multiple targets in time-varying non-line-of-sight (NLoS) environments. Such information can be approximated by associating numerous NLoS observations with the targets’ movement, a novel paradigm that transitions from the traditional “tracking after localization” approaches to a more comprehensive “tracking with localization” method. In particular, we establish the extended states (ETs) for targets to associate with their possible measurements in multipath scenarios. The joint distribution of ETs is regarded as an approximation of the latent SI at the sensor level. The information carried by ETs is then transferred to the target level for tracking using a variational message passing approach, and subsequently fed back to the sensor level to estimate the SI for future steps. We establish a factor graph depicting the propagation of information from multi-sensor measurements to multi-target states and vice versa. Numerical results including uncrewed aerial vehicles (UAVs) validate the superior performance of our proposed algorithm compared to alternative approaches.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5663-5679"},"PeriodicalIF":7.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026353","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 : 2026-01-05DOI: 10.1109/TNSE.2025.3650572
Endri Goshi;Ece Nur Şen;Hasanin Harkous;Shohreh Ahvar;Rastin Pries;Fidan Mehmeti;Wolfgang Kellerer
The development of 5G networks has enabled diverse applications with stringent communication and computation requirements. Ensuring timely operation for these applications requires proximity to compute and network resources, such as edge servers and User Plane Functions. With control over network resources and partial ownership of compute resources, Mobile Network Operators (MNOs) can assume a larger role in service orchestration within a multi-owner edge infrastructure. However, the increasing demand for edge applications often exceeds MNO capacity. To address this, some user requests can be offloaded to third-party cloud operators, expanding capacity but incurring costs and reducing profitability. Beyond profit, energy consumption is a key concern, particularly as we transition toward 6G. Addressing high energy consumption adds complexity to resource orchestration, as it directly conflicts with the goal of maximizing profit. Moreover, in dynamic edge environments, frequent service migrations increase orchestration overhead and can negatively impact user experience. Navigating these trade-offs requires an effective optimization approach. To this end, we formulate a multi-objective optimization problem for demand placement and routing, aiming to maximize MNO profit while minimizing energy consumption and migration overhead. Given its NP-hard nature, we propose a ranked greedy heuristic to effectively balance these objectives. Simulation results show that our approach outperforms benchmark algorithms in both static and dynamic scenarios, achieving near-optimal performance and deviating less than 7% from the optimal solution.
{"title":"Profitable Energy-Efficient UPF and Application Placement and Routing in Dynamic 5G Edge","authors":"Endri Goshi;Ece Nur Şen;Hasanin Harkous;Shohreh Ahvar;Rastin Pries;Fidan Mehmeti;Wolfgang Kellerer","doi":"10.1109/TNSE.2025.3650572","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3650572","url":null,"abstract":"The development of 5G networks has enabled diverse applications with stringent communication and computation requirements. Ensuring timely operation for these applications requires proximity to compute and network resources, such as edge servers and User Plane Functions. With control over network resources and partial ownership of compute resources, Mobile Network Operators (MNOs) can assume a larger role in service orchestration within a multi-owner edge infrastructure. However, the increasing demand for edge applications often exceeds MNO capacity. To address this, some user requests can be offloaded to third-party cloud operators, expanding capacity but incurring costs and reducing profitability. Beyond profit, energy consumption is a key concern, particularly as we transition toward 6G. Addressing high energy consumption adds complexity to resource orchestration, as it directly conflicts with the goal of maximizing profit. Moreover, in dynamic edge environments, frequent service migrations increase orchestration overhead and can negatively impact user experience. Navigating these trade-offs requires an effective optimization approach. To this end, we formulate a multi-objective optimization problem for demand placement and routing, aiming to maximize MNO profit while minimizing energy consumption and migration overhead. Given its NP-hard nature, we propose a ranked greedy heuristic to effectively balance these objectives. Simulation results show that our approach outperforms benchmark algorithms in both static and dynamic scenarios, achieving near-optimal performance and deviating less than 7% from the optimal solution.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5698-5720"},"PeriodicalIF":7.9,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11328872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid advancement of generative artificial intelligence (GAI) has opened new avenues for semantic communication (SemCom). In this paper, we propose CG-SemCom, a unified cross-modal generative semantic communication framework powered by shared semantic knowledge bases (SKBs). CG-SemCom leverages GAI technologies as semantic extractor at the transmitter and generative reconstructor at the receiver, enabling flexible and interpretable cross-modal transmission. We further develop VCG-SemCom, a visual transmission-oriented implementation of CG-SemCom. Specifically, the transmitter employs a vision-language large model (vLLM) to extract concise semantic information in the form of visual difference description, which is transmitted via the joint source-channel coding (JSCC). At the receiver, the diffusion model (DM)-based generative reconstructor synthesizes the target image. The knowledge retrieval mechanism tailored for shared SKBs is introduced to guide semantic extraction and ensure consistency. Additionally, a deep reinforcement learning (DRL)-driven inference agent is proposed to dynamically optimize the generation process at the receiver. To address semantic noise caused by knowledge misalignment and module mismatch, a dual-level error detection and retransmission mechanism is introduced. Moreover, we propose a novel generation similarity metric to evaluate reconstruction quality without requiring access to the original image. Extensive experiments demonstrate that the proposed VCG-SemCom achieves superior information transfer efficiency compared to SemCom benchmark schemes with up to 6% improvement in semantic fidelity and over 4 times reduction in bandwidth consumption.
{"title":"Cross-Modal Generative Semantic Communications Powered by Semantic Knowledge Base","authors":"Zechuan Fang;Mengying Sun;Sen Wang;Xiaodong Xu;Haixiao Gao;Jinghong Huang;Shujun Han;Ping Zhang","doi":"10.1109/TNSE.2025.3650269","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3650269","url":null,"abstract":"The rapid advancement of generative artificial intelligence (GAI) has opened new avenues for semantic communication (SemCom). In this paper, we propose CG-SemCom, a unified cross-modal generative semantic communication framework powered by shared semantic knowledge bases (SKBs). CG-SemCom leverages GAI technologies as semantic extractor at the transmitter and generative reconstructor at the receiver, enabling flexible and interpretable cross-modal transmission. We further develop VCG-SemCom, a visual transmission-oriented implementation of CG-SemCom. Specifically, the transmitter employs a vision-language large model (vLLM) to extract concise semantic information in the form of visual difference description, which is transmitted via the joint source-channel coding (JSCC). At the receiver, the diffusion model (DM)-based generative reconstructor synthesizes the target image. The knowledge retrieval mechanism tailored for shared SKBs is introduced to guide semantic extraction and ensure consistency. Additionally, a deep reinforcement learning (DRL)-driven inference agent is proposed to dynamically optimize the generation process at the receiver. To address semantic noise caused by knowledge misalignment and module mismatch, a dual-level error detection and retransmission mechanism is introduced. Moreover, we propose a novel generation similarity metric to evaluate reconstruction quality without requiring access to the original image. Extensive experiments demonstrate that the proposed VCG-SemCom achieves superior information transfer efficiency compared to SemCom benchmark schemes with up to 6% improvement in semantic fidelity and over 4 times reduction in bandwidth consumption.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5568-5585"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026487","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 : 2026-01-01DOI: 10.1109/TNSE.2025.3650377
Xiaoyu Ma;Fang Fang;Tianlong Liu;Xianbin Wang
With the increasing number of connected devices and complex networks involved, current domain-specific authentication mechanisms become inadequate for the dynamic access demands of large-scale Internet of Things (IoT) systems. Cross-domain authentication and authorization could enhance device interoperability across different security domains. However, they face new challenges including increased authentication latency, non-transferable trust, and excessive authorization computational overhead. To address these issues, in this paper, we propose Dynamic Authentication and Granularized Authorization (DAGA), a cross-domain Zero Trust Architecture (ZTA)-based framework that enables efficient, adaptive, and scalable authentication and authorization for large-scale IoT systems. Rather than relying on full authentication for every request, DAGA introduces a cross-domain pre-authentication phase that estimates device trustworthiness in advance. Specifically, DAGA leverages Federated Learning (FL) and Long Short-Term Memory (LSTM)-based dynamic trust management to minimize unnecessary trust score computations, thereby accelerating cross-domain pre-authentication. At the same time, DAGA employs multi-level and cached fast authorization to achieve adaptive granularized access control, enhancing the accuracy and efficiency. Experimental results demonstrate that DAGA achieves notable reductions in communication and computation overhead compared to ZTA, blockchain-based, and lightweight mechanisms, significantly reducing authentication latency. Despite its efficiency gains, DAGA achieves a superior accuracy–latency trade-off over other baseline approaches, effectively balancing security and efficiency through adaptive optimization.
{"title":"Dynamic Authentication and Granularized Authorization With a Cross-Domain Zero Trust Architecture in Large-Scale IoT Networks","authors":"Xiaoyu Ma;Fang Fang;Tianlong Liu;Xianbin Wang","doi":"10.1109/TNSE.2025.3650377","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3650377","url":null,"abstract":"With the increasing number of connected devices and complex networks involved, current domain-specific authentication mechanisms become inadequate for the dynamic access demands of large-scale Internet of Things (IoT) systems. Cross-domain authentication and authorization could enhance device interoperability across different security domains. However, they face new challenges including increased authentication latency, non-transferable trust, and excessive authorization computational overhead. To address these issues, in this paper, we propose Dynamic Authentication and Granularized Authorization (DAGA), a cross-domain Zero Trust Architecture (ZTA)-based framework that enables efficient, adaptive, and scalable authentication and authorization for large-scale IoT systems. Rather than relying on full authentication for every request, DAGA introduces a cross-domain pre-authentication phase that estimates device trustworthiness in advance. Specifically, DAGA leverages Federated Learning (FL) and Long Short-Term Memory (LSTM)-based dynamic trust management to minimize unnecessary trust score computations, thereby accelerating cross-domain pre-authentication. At the same time, DAGA employs multi-level and cached fast authorization to achieve adaptive granularized access control, enhancing the accuracy and efficiency. Experimental results demonstrate that DAGA achieves notable reductions in communication and computation overhead compared to ZTA, blockchain-based, and lightweight mechanisms, significantly reducing authentication latency. Despite its efficiency gains, DAGA achieves a superior accuracy–latency trade-off over other baseline approaches, effectively balancing security and efficiency through adaptive optimization.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5888-5904"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082006","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 : 2025-12-31DOI: 10.1109/TNSE.2025.3649680
Xuanbo Huang;Kaiping Xue;Lutong Chen;Zixu Huang;Jiangping Han;Jian Li;Ruidong Li
Zero Trust Network (ZTN) has become a widely adopted security architecture in enterprise and campus networks. A typical implementation involves Software-Defined Perimeter (SDP), which deploys proxy-based gateways to mediate user-service communication and block unauthorized access. However, existing deployment approaches rely on empirical placement and static user binding, overlooking user mobility and dynamic traffic patterns. This limitation can lead to suboptimal connection paths, increased delays, and potential performance bottlenecks. This paper presents the first formulation of the SDP gateway placement problem under user mobility. We model this challenge as a multiobjective optimization problem that jointly minimizes user-server connection delay and balances load across gateways. To address it, we propose a two-stage solution comprising offline deployment and online adaptation. The offline stage introduces PathHeatMap-guided SDP embedding, which aggregates user-server flow paths into a heatmap to identify convergence hotspots and guide SDP placement at high-impact nodes. The online stage integrates mobility tracking and deferred SDP rebinding, which dynamically responds to user mobility by temporarily assigning displaced users to nearby gateways and triggering global reassignment only when necessary. Extensive simulations demonstrate that our method attains near-Pareto-front delay and load-balance trade-offs while running orders of magnitude faster than heavy solvers (MILP, Convex, NSGA-II).
{"title":"Adaptive Software-Defined Perimeter Placement for Dynamic User Distributions in Zero-Trust Networks","authors":"Xuanbo Huang;Kaiping Xue;Lutong Chen;Zixu Huang;Jiangping Han;Jian Li;Ruidong Li","doi":"10.1109/TNSE.2025.3649680","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649680","url":null,"abstract":"Zero Trust Network (ZTN) has become a widely adopted security architecture in enterprise and campus networks. A typical implementation involves Software-Defined Perimeter (SDP), which deploys proxy-based gateways to mediate user-service communication and block unauthorized access. However, existing deployment approaches rely on empirical placement and static user binding, overlooking user mobility and dynamic traffic patterns. This limitation can lead to suboptimal connection paths, increased delays, and potential performance bottlenecks. This paper presents the first formulation of the SDP gateway placement problem under user mobility. We model this challenge as a multiobjective optimization problem that jointly minimizes user-server connection delay and balances load across gateways. To address it, we propose a two-stage solution comprising offline deployment and online adaptation. The offline stage introduces <italic>PathHeatMap-guided SDP embedding</i>, which aggregates user-server flow paths into a heatmap to identify convergence hotspots and guide SDP placement at high-impact nodes. The online stage integrates <italic>mobility tracking</i> and <italic>deferred SDP rebinding</i>, which dynamically responds to user mobility by temporarily assigning displaced users to nearby gateways and triggering global reassignment only when necessary. Extensive simulations demonstrate that our method attains near-Pareto-front delay and load-balance trade-offs while running orders of magnitude faster than heavy solvers (MILP, Convex, NSGA-II).","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5505-5521"},"PeriodicalIF":7.9,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026422","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 : 2025-12-30DOI: 10.1109/TNSE.2025.3647918
Dini Wang;Peng Yi;Gang Yan;Feng Fu
Understanding how cooperation evolves in structured populations remains a fundamental question across diverse disciplines. The problem of cooperation typically involves pairwise or group interactions among individuals. While prior studies have extensively investigated the role of networks in shaping cooperative dynamics, the influence of tie or connection strengths between individuals has not been fully understood. Here, we introduce a quenched mean-field based framework for analyzing both pairwise and group dilemmas on any weighted network, providing interpretable conditions required for favoring cooperation. Our theoretical advances further motivate us to find that the degree-inverse weighted social ties – reinforcing tie strengths between peripheral nodes while weakening those between hubs – robustly promote cooperation in both pairwise and group dilemmas. Importantly, this configuration enables heterogeneous networks to outperform homogeneous ones in fixation of cooperation, thereby adding to the conventional view that degree heterogeneity inhibits cooperative behavior under the local stochastic strategy update. We further test the generality of degree-inverse weighted social ties in promoting cooperation on $30, 000$ random networks and 13 empirical networks drawn from real-world systems. Finally, we unveil the underlying mechanism by examining the formation and evolution of cooperative ties under social ties with degree-inverse weights. Our systematic analyses provide new insights into how the network adjustment of tie strengths can effectively steer structured populations toward cooperative outcomes in biological and social systems.
{"title":"Evolutionary Dynamics of Pairwise and Group Cooperation in Heterogeneous Social Networks","authors":"Dini Wang;Peng Yi;Gang Yan;Feng Fu","doi":"10.1109/TNSE.2025.3647918","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3647918","url":null,"abstract":"Understanding how cooperation evolves in structured populations remains a fundamental question across diverse disciplines. The problem of cooperation typically involves pairwise or group interactions among individuals. While prior studies have extensively investigated the role of networks in shaping cooperative dynamics, the influence of tie or connection strengths between individuals has not been fully understood. Here, we introduce a quenched mean-field based framework for analyzing both pairwise and group dilemmas on any weighted network, providing interpretable conditions required for favoring cooperation. Our theoretical advances further motivate us to find that the degree-inverse weighted social ties – reinforcing tie strengths between peripheral nodes while weakening those between hubs – robustly promote cooperation in both pairwise and group dilemmas. Importantly, this configuration enables heterogeneous networks to outperform homogeneous ones in fixation of cooperation, thereby adding to the conventional view that degree heterogeneity inhibits cooperative behavior under the local stochastic strategy update. We further test the generality of degree-inverse weighted social ties in promoting cooperation on <inline-formula><tex-math>$30, 000$</tex-math></inline-formula> random networks and 13 empirical networks drawn from real-world systems. Finally, we unveil the underlying mechanism by examining the formation and evolution of cooperative ties under social ties with degree-inverse weights. Our systematic analyses provide new insights into how the network adjustment of tie strengths can effectively steer structured populations toward cooperative outcomes in biological and social systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5074-5091"},"PeriodicalIF":7.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929376","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 : 2025-12-30DOI: 10.1109/TNSE.2025.3649386
Wagner A. Junior;Fábio Ramos;Alex B. Vieira;José Augusto M. Nacif;Ricardo S. Ferreira
Graph Neural Networks (GNNs) have gained popularity as an efficient choice for learning on graph-structured data. However, most methods are node or graph-centered, often overlooking valuable information that can be encoded in edge features and relations. In this survey, we present a comprehensive review and a novel taxonomy of Edge-Aware Graph Learning Methods, i.e., models that explicitly leverage edge information in the learning process. We trace the evolution of these methods from classical approaches through random walks to modern GNN architectures, including the emerging paradigm of Edge-Aware Graph Transformers. Through a comparative analysis, we demonstrate the consistent performance gains of these models over traditional node-centric approaches across a wide range of real-world applications and benchmarks. However, many challenges arise in this field. As such, we provide an explicit discussion of key limitations, particularly the scalability issues and computational overhead associated with many current architectures. Finally, by synthesizing the state-of-the-art and identifying open problems, this survey provides a clear roadmap to guide future research toward developing more efficient, scalable, and robust edge-aware models.
{"title":"A Survey on Edge-Aware Graph Learning Methods","authors":"Wagner A. Junior;Fábio Ramos;Alex B. Vieira;José Augusto M. Nacif;Ricardo S. Ferreira","doi":"10.1109/TNSE.2025.3649386","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649386","url":null,"abstract":"Graph Neural Networks (GNNs) have gained popularity as an efficient choice for learning on graph-structured data. However, most methods are node or graph-centered, often overlooking valuable information that can be encoded in edge features and relations. In this survey, we present a comprehensive review and a novel taxonomy of Edge-Aware Graph Learning Methods, i.e., models that explicitly leverage edge information in the learning process. We trace the evolution of these methods from classical approaches through random walks to modern GNN architectures, including the emerging paradigm of Edge-Aware Graph Transformers. Through a comparative analysis, we demonstrate the consistent performance gains of these models over traditional node-centric approaches across a wide range of real-world applications and benchmarks. However, many challenges arise in this field. As such, we provide an explicit discussion of key limitations, particularly the scalability issues and computational overhead associated with many current architectures. Finally, by synthesizing the state-of-the-art and identifying open problems, this survey provides a clear roadmap to guide future research toward developing more efficient, scalable, and robust edge-aware models.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5680-5697"},"PeriodicalIF":7.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11319233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cooperative spectrum sensing (CSS) is a technique that exploits spatial diversity to enhance spectrum detection in cognitive radio (CR) networks. It involves multiple cognitive users to collaboratively sense spectrum bands and report their raw sensed data or local detection results to a fusion center to make spectrum allocation decisions for CR. The performance of CSS is often degraded by the non-stationarity of the wireless channels and limited computation and communication resources in the CR network. To address these challenges, we propose a novel joint optimization framework tailored for multidimensional resource-constrained CR networks in this paper, by simultaneously determining the user selection, the sensing-transmission-computation duration, and the allocation of communication and computation resources involved in the system, with the aim of maximizing the spectrum sensing performance under strict resource constraints. The proposed framework first derives a closed-form solution for optimal sensing-transmission-computation duration and then develops an efficient iterative algorithm for joint user selection and resource allocation. Simulation results show that the proposed framework significantly outperforms existing solutions without jointly considering the sensing, transmission, and computation processes and/or multidimensional resource limitations.
{"title":"Joint Optimization of Sensing, Communication, and Computation for Cooperative Spectrum Sensing","authors":"Xuesong Liu;Junkang Ge;Xiaoqian Li;Yansong Liu;Haoyu Tang;Gang Feng","doi":"10.1109/TNSE.2025.3649240","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649240","url":null,"abstract":"Cooperative spectrum sensing (CSS) is a technique that exploits spatial diversity to enhance spectrum detection in cognitive radio (CR) networks. It involves multiple cognitive users to collaboratively sense spectrum bands and report their raw sensed data or local detection results to a fusion center to make spectrum allocation decisions for CR. The performance of CSS is often degraded by the non-stationarity of the wireless channels and limited computation and communication resources in the CR network. To address these challenges, we propose a novel joint optimization framework tailored for multidimensional resource-constrained CR networks in this paper, by simultaneously determining the user selection, the sensing-transmission-computation duration, and the allocation of communication and computation resources involved in the system, with the aim of maximizing the spectrum sensing performance under strict resource constraints. The proposed framework first derives a closed-form solution for optimal sensing-transmission-computation duration and then develops an efficient iterative algorithm for joint user selection and resource allocation. Simulation results show that the proposed framework significantly outperforms existing solutions without jointly considering the sensing, transmission, and computation processes and/or multidimensional resource limitations.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5453-5470"},"PeriodicalIF":7.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026410","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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