Intelligent and Converged Networks最新文献_第2页

Secrecy Rate Maximization for STAR-RIS-UAV-IoT with Imperfect CSI via DRL 基于DRL的不完全CSI下STAR-RIS-UAV-IoT保密率最大化

Intelligent and Converged Networks Pub Date : 2026-06-01 Epub Date: 2026-07-03 DOI: 10.23919/ICN.2026.0009

Yujie Huang;Haibin Wan;Xiangcheng Li;Yun Li

{"title":"Secrecy Rate Maximization for STAR-RIS-UAV-IoT with Imperfect CSI via DRL","authors":"Yujie Huang;Haibin Wan;Xiangcheng Li;Yun Li","doi":"10.23919/ICN.2026.0009","DOIUrl":"https://doi.org/10.23919/ICN.2026.0009","url":null,"abstract":"The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can effectively reshape wireless propagation paths, not only alleviating the severe performance degradation caused by blockages in the direct link but also significantly extending coverage. In addition, the high mobility and deployment flexibility of unmanned aerial vehicle (UAV) enable STAR-RIS-equipped UAV to dynamically establish high-quality communication links. In modern intelligent Internet of Things (loT) environments, where wireless connectivity is ubiquitous, ensuring secure data transmission is of critical importance. This paper investigates the maximization of the downlink secrecy rate in an STAR-RIS-UAV-loT system under imperfect channel state information (CSI). Due to the non-convexity and coupling among the optimization variables, solving such optimization problems is highly challenging. To overcome these challenges, we propose an improved deep deterministic policy gradient (DDPG) algorithm incorporating convolutional layers and the prioritized experience replay (PER) mechanism, denoted as DDPG-CP, which jointly optimizes the transmit beamforming matrix, the STAR-RIS phase shift matrices, and the UAV's horizontal position. Simulation results demonstrate that, in the presence of eavesdroppers, the proposed DDPG-CP algorithm can continuously improve the legitimate devices' communication performance while effectively suppressing the eavesdroppers' interception capability, thereby substantially enhancing the overall secrecy rate.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 2","pages":"129-145"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Near-Field Channel Estimation for Movable Antenna Systems via Bidirectional Movement 双向移动可移动天线系统近场信道估计

Intelligent and Converged Networks Pub Date : 2026-06-01 Epub Date: 2026-07-03 DOI: 10.23919/ICN.2026.0006

Shun Yang;Weidong Mei;Xin Wei;Zhi Chen;Boyu Ning

{"title":"Near-Field Channel Estimation for Movable Antenna Systems via Bidirectional Movement","authors":"Shun Yang;Weidong Mei;Xin Wei;Zhi Chen;Boyu Ning","doi":"10.23919/ICN.2026.0006","DOIUrl":"https://doi.org/10.23919/ICN.2026.0006","url":null,"abstract":"Movable antenna (MA) systems have emerged as a transformative approach in the area of wireless communications by enabling dynamic antenna repositioning within a local region. However, to achieve a high design flexibility, a sufficiently large movement region is generally required, which, however, introduces complex spherical wavefront effects. As such, this paper focuses on the near-field channel estimation problem for MA systems, aiming to acquire the channel state information at any position within the antenna movement region under the near-field field-response channel model. To this end, we propose a novel bidirectional movement strategy for joint estimation of both angular and distance parameters of multi-path components. Under this strategy, we develop an adaptive grid orthogonal matching pursuit algorithm that leverages geometric constraints between the bidirectional movement to resolve parameter ambiguities and mitigate grid mismatch effects. The algorithm incorporates adaptive refinement mechanisms, hierarchical search strategies, and convergence monitoring to ensure reliable parameter estimation. Simulation results demonstrate that the proposed approach significantly improves the parameter estimation accuracy in terms of normalized mean square error compared with conventional methods, particularly in the low signal-to-noise ratio regime.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 2","pages":"183-193"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CB-D3QN: Malware Propagation Defense for Edge Intelligence Based Industrial Internet of Things via a Colonel Blotto Game-Enhanced Dueling Double Deep Q-Network Approach CB-D3QN：基于边缘智能的工业物联网恶意软件传播防御，基于上校Blotto游戏增强决斗双深度q -网络方法

Intelligent and Converged Networks Pub Date : 2026-06-01 Epub Date: 2026-07-03 DOI: 10.23919/ICN.2026.0007

Shigen Shen;Siyu Wei;Xiao-Zhi Gao

{"title":"CB-D3QN: Malware Propagation Defense for Edge Intelligence Based Industrial Internet of Things via a Colonel Blotto Game-Enhanced Dueling Double Deep Q-Network Approach","authors":"Shigen Shen;Siyu Wei;Xiao-Zhi Gao","doi":"10.23919/ICN.2026.0007","DOIUrl":"https://doi.org/10.23919/ICN.2026.0007","url":null,"abstract":"Malware spread in edge intelligence based Industrial Internet of Things (IIoT) systems is a serious challenge. Resources are unequal-attackers can put all their resources on one target, but defenders have to protect everything at once. To solve this challenge, we build CB-D3QN, which is a defense method using asymmetric Colonel Blotto game theory and Dueling Double Deep Q-Network (D3QN). We treat the fight between attackers and defenders as a Colonel Blotto game where both sides have different amounts of resources. This matches what happens in real IIoT malware attacks. CB-D3QN brings together Colonel Blotto games and D3QN, and uses deep reinforcement learning to find the right balance and make defense strategies better. The system considers malware behavior history, how we split resources, and which side has the advantage in each area. We evaluate CB-D3QN against other state-of-the-art methods and experimental results show that it achieves higher malware mitigation success rate, longer system resilience, and lower false intervention rate.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 2","pages":"111-128"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11595157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Adjustment Strategy for Optimal Reception Area Selection for Trans-Air-Sea Optical Communications 跨海空光通信最优接收区选择的自适应调整策略

Intelligent and Converged Networks Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.23919/ICN.2025.0013

Jiehong Wu;Wenna Zhang;Cunqian Yu

{"title":"Adaptive Adjustment Strategy for Optimal Reception Area Selection for Trans-Air-Sea Optical Communications","authors":"Jiehong Wu;Wenna Zhang;Cunqian Yu","doi":"10.23919/ICN.2025.0013","DOIUrl":"https://doi.org/10.23919/ICN.2025.0013","url":null,"abstract":"With the increasing demand for ocean communication, trans-air-sea wireless optical communication (WOC) has attracted growing attention. Due to spatial variations in signal attenuation, low-power regions are more susceptible to signal loss and channel instability, limiting system performance. To address this problem, an adaptive adjustment strategy for selecting the optimal reception area is proposed, which aims to maximize the received power. The strategy is based on the photon transmission model, and a local photon distribution based optimal reception area adjustment (LPD-ORAA) algorithm is designed by combining the properties of photon distribution. The LPD-ORAA consists of two parts, adaptive grid division and GE-based local optimization. By combining these two components, the optimal reception area can be effectively determined. Simulation results show that the average normalized received power (NRP) of the optimal reception area obtained by LPD-ORAA is 1.35 dB higher than that of the traditional alignment under various seawater conditions. Compared with the baseline algorithm, LPD-ORAA achieves higher NRP and lower channel impulse response. Finally, we verify the effectiveness of the LPD-ORAA algorithm in a pool environment. Experimental results show that the optimal reception area determined by LPD-ORAA is more suitable for trans-air-sea WOC than the traditional alignment.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 1","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11449569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topological Modeling and Routing Protocol for In-Vivo Nanosensor Network 活体纳米传感器网络拓扑建模与路由协议

Intelligent and Converged Networks Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.23919/ICN.2026.0003

Juan Xu;Shuo Li;Hongmin Huang;Xin Li

{"title":"Topological Modeling and Routing Protocol for In-Vivo Nanosensor Network","authors":"Juan Xu;Shuo Li;Hongmin Huang;Xin Li","doi":"10.23919/ICN.2026.0003","DOIUrl":"https://doi.org/10.23919/ICN.2026.0003","url":null,"abstract":"In-vivo Wireless NanoSensor Networks (iWNSNs) have emerged as a promising paradigm for real-time biomedical monitoring, supported by nanosensors capable of detecting biochemical markers at extremely low concentrations. Among various possible application scenarios, gastrointestinal environments represent an important but challenging domain for in-vivo communication. Motivated by these application needs, this paper develops a topological model derived from the static morphology of the stomach and proposes a Multi-hop Energy-Balanced Clustering Routing protocol, termed MEBCR, for THz-based iWNSNs. MEBCR employs an unequal-clustering strategy to optimize cluster-head selection and competition radius while dynamically adjusting cluster sizes to mitigate uneven energy consumption. Simulation results show that MEBCR significantly enhances energy balance, prolongs network lifetime, increases data-delivery reliability, and reduces end-to-end delay. These results illustrate that the proposed approach can effectively support THz-band in-vivo nanosensor applications, with gastric monitoring being one representative use case.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 1","pages":"65-79"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11449567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Quantum Annealing Schedules with Monte Carlo Tree Search Enhanced by MindSpore 利用MindSpore增强的蒙特卡罗树搜索优化量子退火计划

Intelligent and Converged Networks Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.23919/ICN.2025.0015

Chao Wang;Fen Xia;Chunlei Hong;Zhi Pei

{"title":"Optimizing Quantum Annealing Schedules with Monte Carlo Tree Search Enhanced by MindSpore","authors":"Chao Wang;Fen Xia;Chunlei Hong;Zhi Pei","doi":"10.23919/ICN.2025.0015","DOIUrl":"https://doi.org/10.23919/ICN.2025.0015","url":null,"abstract":"One of the key research focuses in quantum annealing is the design and optimization of annealing schedules to enhance computational efficiency, enabling large-scale applications. QuantumZero (QZero) pioneered the integration of Monte Carlo Tree Search (MCTS) with neural networks to autonomously design annealing schedules within a hybrid quantum-classical framework. This approach is distinguished by its ability to enhance the performance of Monte Carlo Tree Search through the integration of neural networks, enabling the efficient design of annealing paths even with limited annealing time. The paper presents an optimized QZero method based on intuitive reasoning theory and MindSpore, which further enhances QZero's ability to conserve computational resources and resist noise. In terms of learning efficiency, the optimized QZero algorithm improves the convergence speed of the neural network by 93% compared to the original algorithm. Notably, the average number of quantum annealing queries required to achieve 99% fidelity is reduced by 45.09%. Regarding noise resistance, the optimized QZero algorithm requires 34.27% fewer quantum annealing queries to reach 990% fidelity compared to the original algorithm. The optimized QZero algorithm demonstrates strong competitiveness in optimizing quantum annealing schedules.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 1","pages":"20-33"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11449564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Learning-Based Contract Mechanism for Privacy-Aware Incentives in UAV-Assisted Federated Learning 无人机辅助联邦学习中隐私感知激励的基于学习的契约机制

Intelligent and Converged Networks Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.23919/ICN.2026.0002

Chaoyang Zhu;Xiangcheng Li;Xiao Zhu

引用次数: 0

CRAFT: Towards Handling Dual Skew in Cross-Domain Multi-Task Federated Edge Learning 工艺：跨领域多任务联邦边缘学习中处理双重倾斜的方法

Intelligent and Converged Networks Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.23919/ICN.2026.0004

Yuzhao Xiang;Bangqi Pan;Shuqin Cao;Jianfeng Lu

{"title":"CRAFT: Towards Handling Dual Skew in Cross-Domain Multi-Task Federated Edge Learning","authors":"Yuzhao Xiang;Bangqi Pan;Shuqin Cao;Jianfeng Lu","doi":"10.23919/ICN.2026.0004","DOIUrl":"https://doi.org/10.23919/ICN.2026.0004","url":null,"abstract":"The application of federated edge learning to cross-domain multi-task learning—where clients hold data from different domains and tasks—introduces significant challenges, specifically the dual skew where the intersection of feature and task heterogeneity creates a vicious circle of internal error propagation. Although prior researches have advanced optimization strategies, they often relies on static parameter isolation or monolithic aggregation, neglecting the potential synergistic relationships among tasks, thereby leading to optimization deadlocks. To bridge this gap, we propose CRAFT, a novel method designed with a two-timescale optimization philosophy that integrates Cross-staggered learning, a coalition foRmation gAme, and selF-adapTive weighting. Specifically, CRAFT mitigates local empirical risk arising from dual skew through a cross-stagger paradigm, which employs a dynamic, alternating optimization strategy to decouple conflicting gradients. It corrects offline gradient bias by modeling item synergy via a coalition formation game for structural stability, while resolving online update conflicts using a causal inference-based mechanism that estimates the true counterfactual contribution of each client to filter real-time noise. Extensive experiments under a challenging unbalanced cross-domain hybrid-task setting demonstrate that CRAFT consistently outperforms existing approaches, achieving up to an improvement of 7.26% in converged accuracy with superior robustness across varying heterogeneity levels.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 1","pages":"80-110"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11449561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Equitable Energy-Efficient Resource Optimization and Task Distribution for Green Multi-UAV Assisted Socially Internet of Things 绿色多无人机辅助社会物联网的公平节能资源优化与任务分配

Intelligent and Converged Networks Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.23919/ICN.2026.0001

Rachit Patel;Rajeev Arya

{"title":"Equitable Energy-Efficient Resource Optimization and Task Distribution for Green Multi-UAV Assisted Socially Internet of Things","authors":"Rachit Patel;Rajeev Arya","doi":"10.23919/ICN.2026.0001","DOIUrl":"https://doi.org/10.23919/ICN.2026.0001","url":null,"abstract":"Mobile edge computing (MEC), powered by renewable energy, is expected to be an environmentally benign computing solution for the Internet of Things (IoT). However, the erratic needs for computation and renewable energy typically lead to a mismatch between system requirements and energy supply, which wastes excess energy or results in a deficit. Hence, it is essential to minimize energy usage to enhance the system's performance. A multi-objective problem has been considered to reduce the overall energy usage and computational cost in an unmanned aerial vehicle (UAV) assisted social aware Internet of Things (SAIoT) network. Resources are optimized and allocated with a cluster head selection using particle swarm optimization (PSO), and task distribution is done using artificial bee colony (ABC) optimization. The simulated results show that the proposed approach, equitable energy-efficient task splitting and resource optimization (EE-TSRO), minimizes energy usage and latency. The proposed approach may be used in vehicle-to-vehicle communication, natural disaster management, robotic surgery, etc.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"7 1","pages":"34-48"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11449557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Criteria Optimization for Service Placement and Migration in Collaborative Edge Computing 协同边缘计算中服务放置和迁移的多准则优化

Intelligent and Converged Networks Pub Date : 2025-12-29 DOI: 10.23919/ICN.2025.0022

Jean de la Croix Kiguigouleli Ki;Boureima Zerbo;Mifiamba Soma

{"title":"Multi-Criteria Optimization for Service Placement and Migration in Collaborative Edge Computing","authors":"Jean de la Croix Kiguigouleli Ki;Boureima Zerbo;Mifiamba Soma","doi":"10.23919/ICN.2025.0022","DOIUrl":"https://doi.org/10.23919/ICN.2025.0022","url":null,"abstract":"The rapid growth of latency-sensitive applications in edge computing requires efficient strategies for service placement and migration. However, existing approaches often prioritize a single objective (e.g., latency) while neglecting the associated trade-offs in terms of energy, cost, and resource utilization. In this study, we examine the combined issues of service placement and migration within a Collaborative Edge Computing (CEC) environment. In this environment, conflicting objectives such as end-to-end latency, energy consumption, and migration cost must be balanced under capacity (Central Processing Unit (CPU), memory, and bandwidth) and Quality of Service (QoS) constraints such as maximum delay. We propose a multi-objective formulation that addresses these criteria simultaneously, and we introduce a distributed evolutionary optimization method based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in an island model. We evaluated our method using real-life scenarios involving cloud-region-micro-edge topologies, heterogeneous loads, and differentiated deadlines, and compared it with a centralized NSGA-II approach and a weighted sum approach. Our results reveal significantly broader Pareto coverage and greater solution diversity. This results in larger hypervolumes and more favorable trade-offs at the extremes of the front (minimum latency, minimum energy, or contained migration). Two-dimensional projections confirm that the proposed method outperforms the baselines on several objective planes. Spatial visualization of the mapping reveals local service anchoring that improves latency without increasing migration costs.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 4","pages":"278-288"},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11318123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0