Intelligent and Converged Networks最新文献

{"title":"Edge-Weighted Anonymity for Directed Network Graph","authors":"Yanze Huang;Jianghong Lin;Wei Li;Jiaru Wang;Limei Lin","doi":"10.23919/ICN.2025.0018","DOIUrl":"https://doi.org/10.23919/ICN.2025.0018","url":null,"abstract":"In social networks, weighted graph anonymity is gaining more and more attention. But fan-following graphs like Twitter's are not protected by anonymity. Privacy protection based on directed graphs should arouse everyone's importance, thus it requires effective anonymization techniques. In this paper, we propose a directed edge weighted graph anonymity scheme to model the shortest path in a directed network graph by Floyd algorithm and Dijkstra algorithm. Also, we execute the experiments on the number of anonymity solutions and time with different edge weights to verify their anonymity feasibility. Finally, we analyze the anonymity of two algorithms and to some extent concluded that Floyd algorithm is more suitable for anonymity of directed network graphs.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 3","pages":"223-233"},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183942","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}

{"title":"Load-Aware Multi-Task Offloading Strategy Based on Optimal Stopping Theory in Mobile Edge Computing","authors":"Chen Zhao;Xiaokui Yue;Mingliang Chen;Gaocai Wang;Tian Wang;Chongben Tao","doi":"10.23919/ICN.2025.0019","DOIUrl":"https://doi.org/10.23919/ICN.2025.0019","url":null,"abstract":"With the wide application of Mobile Edge Computing (MEC) in the Internet of Vehicles and other fields, mobile nodes are no longer limited to smart phones, but also include new mobile devices such as smart vehicles. Faced with the access of a large number of devices, there may be situations where some edge servers are overloaded while others are relatively idle. To address this issue, a load aware multi-task offloading strategy based on optimal stopping theory is proposed, which minimizes the average load of the selected edge servers through sequential decision-making. Firstly, consider the cost of delaying decision-making and introduce a benefit function to constrain the number of observations. Secondly, the optimal stopping time is determined by solving the secretary problem with observation constraints. Finally, extending to the scenario of multi-task offloading, design a phased decision-making multi-task offloading algorithm. The simulation experiment results show that the proposed offloading strategy can significantly reduce the average load of the selected edge servers for task offloading, and can more effectively optimize the workload of edge servers.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 3","pages":"234-246"},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183943","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}

{"title":"Spectral Efficiency Optimization for Millimeter Wave Hybrid Massive MIMO Systems Using Unsupervised Learning","authors":"Ziyang Huang;Maobin Tang;Wei Wang;Weiqiang Tan;Bin Tan","doi":"10.23919/ICN.2025.0020","DOIUrl":"https://doi.org/10.23919/ICN.2025.0020","url":null,"abstract":"Hybrid beamforming is a promising technique for massive multiple-input multiple-output (MIMO) systems because it is able to reduce the hardware cost and power consumption while improving system performance, which is considered a key enabler for the fifth-generation and beyond communications. However, the acquisition of perfect channel state information is a challenging task since the hybrid precoder design utilizes the phase shifters in the analog domain. In this paper, we investigate the spectral efficiency (SE) for millimeter wave hybrid massive MIMO system, where the optimization of precoding adopted unsupervised learning. To maximize the system's SE, this paper proposes an efficient DNN-LSTM-Res network (named as DNN-LSTM-ResNet), which incorporates the deep neural network (DNN), a long short-term memory (LSTM) method and a residual network (ResNet), where the neural network is treated as the basic network and residual neural network is utilized to implement the degradation problem. Finally, numerical simulations are provided to validate the effectiveness of our proposed DNN-LSTM-ResNet for hybrid massive MIMO systems. Results showcased the proposed network greatly decreases the number of training parameters and can effectively improve the system's SE that achieves approximately 20% compared to conventional algorithms. Moreover, the proposed DNN-LSTM-ResNet has the characteristics of fast convergence speed and strong search capability.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 3","pages":"247-263"},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183931","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}

{"title":"GNSS Signal-to-Noise Snow Depth Inversion Based on Robust Empirical Mode Decomposition","authors":"Dengao Li;Xinyu Luo;Jumin Zhao;Fanming Wu;Hairong Jiang;Danyang Shi","doi":"10.23919/ICN.2025.0007","DOIUrl":"https://doi.org/10.23919/ICN.2025.0007","url":null,"abstract":"Using global navigation satellite system (GNSS) to monitor snow depth helps scientists study the impacts of climate change and predict future climate patterns. In the process of extracting reflection signals from signal-to-noise ratio (SNR) data, traditional methods usually use low order polynomials for detrending terms. However, this traditional algorithm cannot completely eliminate the environmental noise in the SNR during signal decomposition, resulting in other noise sources still existing in the detrended SNR, which affects the inversion results. In order to make the inversion results more accurate, this paper proposes a robust empirical mode decomposition (REMD) based method. In signal decomposition, REMD is applied to improve the algorithm, and the correlation coefficient method is used to denoise the decomposed signal. The proposed algorithm is validated using the data from the U.S. Plate Boundary Observatory network SG27 site from winter 2016 to spring 2017 as the study data. The obtained experimental results are compared with the actual snow depth provided by Snowpack Telemetry. When the improved algorithm is used, the root mean square error and mean absolute error of the snow depth inversion at the SG27 site are improved, respectively.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 2","pages":"115-128"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11051149","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481911","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}

{"title":"Adaptive Load-Based Contention Window-MAC Protocol for Underwater Wireless Sensor Networks","authors":"Meng Shan;Min Ouyang;Jiyi Wu;Tong Wang","doi":"10.23919/ICN.2025.0011","DOIUrl":"https://doi.org/10.23919/ICN.2025.0011","url":null,"abstract":"In Underwater Wireless Sensor Networks (UWSNs), protocols with definite contention windows fail to adapt to changing network conditions, resulting in channel congestion and collisions. This paper introduces the Adaptive Load-based Contention Window Medium Access Control (ALCW-MAC) protocol, which improves the backoff mechanism of the Contention Window Medium Access Control (CW-MAC) protocol. The ALCW-MAC protocol calculates a collision busy factor to evaluate the system load more accurately and dynamically adjusts the contention window size based on the average collision rate and channel busy rate. These dynamic adjustments enable the protocol to reflect real-time network loads, resulting in channel congestion reduction and bandwidth efficiency improvement. Comprehensive experimental results confirm that ALCW-MAC surpasses CW-MAC in throughput and packet loss rate, significantly enhancing network performance and stability.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 2","pages":"164-176"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11051150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481910","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}

{"title":"Call for Papers: Special Issue on Near Field Communications and Sensing for 6G","authors":"","doi":"10.23919/ICN.2025.0012","DOIUrl":"https://doi.org/10.23919/ICN.2025.0012","url":null,"abstract":"Intelligent and Converged Networks is an international specialized journal that focuses on the latest developments in communication technology. The journal is co-published by Tsinghua University Press (TUP) and the International Telecommunication Union (ITU), the United Nations specialized agency for information and communication technology (ICT). Intelligent and Converged Networks draws its name from the accelerating convergence of different fields of communication technology and the growing influence of artificial intelligence and machine learning. The journal is indexed in Ei Compendex, Scopus, Inspec, DOAJ, etc.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 2","pages":"177-178"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11051148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481874","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}

{"title":"Cooperative Communication Resources Scheduling of Satellite Network Using a Mixed Vector Encoding Heuristic Algorithm","authors":"Jiaxuan Xie;Linling Kuang;Shan Gao","doi":"10.23919/ICN.2025.0008","DOIUrl":"https://doi.org/10.23919/ICN.2025.0008","url":null,"abstract":"The communication resources scheduling problem of the satellite network contains the user-satellite association problem, the user-beam matching problem, and the beam power allocation problem. Different optimization problems contain different types of variables. Mult-type variables and three coupling problems cause the cooperative scheduling problem to be complex. In this paper, we propose a mixed vector encoding heuristic algorithm (MVEHA) to optimize the joint resources allocation problem of the multiple beams satellite network. Specifically, we use the 0–1 encoding vector to represent the user-satellite association scheme, the user-beam matching scheme is denoted by the continuous vector with priority, and a normalization vector is designed to encoded the beam power allocation scheme. Due to the mixed vector encoding method, we design two optimization operators to guide the search direction of the population. Compared to the conventional optimization algorithm, the simulation experiment shows MVEHA has better solution performance and robustness to solve the communication resources allocation problem of the satellite network.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 2","pages":"129-140"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11051152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481860","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}

{"title":"Leveraging Text Semantics for Enhanced Scene Text Image Super-Resolution","authors":"Li Chen;Jinsong Wu;Yicheng Liu","doi":"10.23919/ICN.2025.0009","DOIUrl":"https://doi.org/10.23919/ICN.2025.0009","url":null,"abstract":"In recent years, due to the development of neural networks, super-resolution technology has made unprecedented progress. However, most existing super-resolution methods treat scene text images as normal images, ignoring the text information within them. This paper proposes to incorporate categorical priors specific to the text in scene text image super-resolution model training process, called two-stage text prior super-resolution (TTPSR) framework. The TTPSR framework first employs a parallel context attention network to restore the low-resolution image without incorporating text priors. Then, the obtained image is used for text recognition to obtain the text prior. The attention mechanism is then used to fuse the text prior and image features to guide the generation of the final high-resolution text image. Experiments have shown that the TTPSR model outperforms relevant state-of-the-art models in relevant metrics, peak signal-to-noise ratio, structural similarity index, and text accuracy, on the Textzoom dataset.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 2","pages":"141-150"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481908","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}

{"title":"MEIS-YOLO: Improving YOLOv11 for Efficient Aerial Object Detection with Lightweight Design","authors":"Yicheng Liu;Jinsong Wu;Li Chen","doi":"10.23919/ICN.2025.0010","DOIUrl":"https://doi.org/10.23919/ICN.2025.0010","url":null,"abstract":"With the advancement of aerial technologies like drones and satellites, deep learning-driven object detection has seen considerable improvements in the processing of aerial images. Nevertheless, conventional object detection algorithms continue to encounter performance limitations, particularly when handling complex backgrounds and small objects. To tackle this problem, this paper presents an enhanced You Only Look Once version 11 (YOLOv11)-based model designed to boost both the detection accuracy and computational efficiency in aerial image processing, named MEIS-YOLO. The core innovation of the model lies in the introduction of a Multi-scale Edge Information Selection (MEIS) module, which selects key features highly relevant to the target detection task from multi-scale features, strengthening the representation of edge information and significantly improving detection performance under conditions of small targets and complex backgrounds. Additionally, the cross bi-level routing attention module, which incorporates the cross-stage partial structure, optimizes the attention mechanism, further enhancing the model's detection ability and computational efficiency. To further optimize multi-scale feature fusion, this paper introduces the asymptotic feature pyramid network. The experimental results show that MEIS-YOLO outperforms YOLOv11 on both VisDrone-DET2019 and DOTA datasets, especially on small target detection and complex backgrounds, with AP<inf>s</inf>increasing by 4% and 8%, respectively. At the same time, floating point operations are reduced by 8%, and the number of parameters decreases by 25%, demonstrating its substantial potential for practical applications. This study provides an efficient, accurate, and lightweight solution for unmanned aerial vehicle object detection tasks.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 2","pages":"151-163"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11008526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481873","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}

{"title":"Reconfigurable Intelligent Surfaces for 6G: Engineering Challenges and the Road Ahead","authors":"Xianming Zhao;Mengnan Jian;Yijian Chen;Yajun Zhao;Lin Mu","doi":"10.23919/ICN.2025.0004","DOIUrl":"https://doi.org/10.23919/ICN.2025.0004","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RISs) have emerged as a pivotal technology for the Sixth-Generation (6G) communication system, showcasing the ability to configure wireless environment dynamically. Acknowledged as a breakthrough in enhancing network coverage, augmenting system capacity, and facilitating advanced applications such as Integrated Communication and Sensing (ISAC), RISs present a concrete approach to molding the future network evolution. The advancement of RIS technology necessitates a departure from idealistic assumptions and oversimplifications, compelling a progression towards models that more accurately reflect the physical attributes of hardware and the characteristics of propagation. In this paper, we delve into the practical constraints and limitations of current RIS design methodologies, conducting a comprehensive analysis based on the latest technological research advancements and product realizations. Our exploration is broad-ranging, encompassing the engineering challenges of single-point RISs, such as hardware impairments, intricacies of algorithm design, frequency spectrum-specific difficulties. A concentrated discourse is presented on novel near-field channel designs, the restrictions imposed by low-bit quantization, and the intricacies of amplitude-phase correlation constraints. This discussion aims to unearth the challenges, opportunities, and paradigmatic shifts induced by the practical deployment of RISs. The deployment challenges, networking dilemmas, simulation, and product evaluation is provided for RISs in large-scale networks from a broader system perspective. Furthermore, this paper highlights the critical need for accelerated efforts towards the commercialization of RISs. We explore the practical application revolution of RISs, encompassing engineering aspects and standardization processes. Our discussion aims to establish a foundational framework for introducing RISs into the market, acknowledging their significant potential as a game-changing technology in 6G communications.","PeriodicalId":100681,"journal":{"name":"Intelligent and Converged Networks","volume":"6 1","pages":"53-81"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949779","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777926","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}