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Enhanced secure lossless image steganography using invertible neural networks
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1016/j.jksuci.2024.102259
Weida Chen , Weizhe Chen
Image steganography is a technique that embeds secret data into cover images in an imperceptible manner, ensuring that the original data can be recovered by the receiver without arousing suspicion. The key challenges currently faced by image steganography are capacity, invisibility, and security. We suggest an invertible neural network-based image steganography technique to concurrently address these three issues. To achieve better invisibility, we adopt a method that avoids the loss of information, thereby preventing ill-posed problems. The learning cost during image embedding can be reduced by only fitting part of the color channels in order to address the issue of high capacity. Additionally, we introduce the concept of a key to constrain the embedding process of the secret information, significantly enhancing the security of the hidden data. According to our experimental results, our method outperforms other image steganography algorithms on DIV2K, COCO, and ImageNet datasets, achieving perfect recovery of the secret images, its PSNR and SSIM can reach the theoretical maximum values.
图像隐写术是一种将秘密数据以不易察觉的方式嵌入封面图像的技术,它能确保接收者在不引起怀疑的情况下恢复原始数据。图像隐写术目前面临的主要挑战是容量、隐蔽性和安全性。我们提出了一种基于可逆神经网络的图像隐写技术,以同时解决这三个问题。为了达到更好的隐蔽性,我们采用了一种避免信息丢失的方法,从而避免了不合理的问题。为了解决高容量问题,我们只拟合了部分颜色通道,从而降低了图像嵌入过程中的学习成本。此外,我们还引入了密钥的概念来约束秘密信息的嵌入过程,从而大大提高了隐藏数据的安全性。根据实验结果,我们的方法在 DIV2K、COCO 和 ImageNet 数据集上的表现优于其他图像隐写算法,实现了秘密图像的完美恢复,其 PSNR 和 SSIM 均达到了理论最大值。
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引用次数: 0
An oversampling FCM-KSMOTE algorithm for imbalanced data classification
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1016/j.jksuci.2024.102248
Hongfang Zhou , Jiahao Tong , Yuhan Liu , Kangyun Zheng , Chenhui Cao
In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose a novel oversampling method named FCM-KSMOTE. The algorithm initially performs a density-based fuzzy clustering on the data, then iterates to partition regions and perform oversampling inside each cluster. Secondly, it merges the clusters and conducts noise detection to obtain a balanced dataset. Finally, we conducted the experiments on 19 public datasets and 3 synthetic datasets. Six evaluation metrics of Recall, Accuracy, G-mean, Specificity, AUC and F1-Score were used in the experiments. The experimental results demonstrate that our method can significantly improve the recognition rate of the minority class while maintaining high accuracy for the majority class. Particularly with the RF classifier, our method ranks first in all evaluation metrics, with a Recall difference of up to 0.2 compared to the least performing method, demonstrating its substantial performance advantage.
近年来,不平衡数据分类已成为一项具有挑战性的任务。针对这一问题,我们提出了一种名为 FCM-KSMOTE 的新型超采样方法。该算法首先对数据进行基于密度的模糊聚类,然后迭代划分区域,并在每个聚类内部进行超采样。其次,该算法合并聚类并进行噪声检测,以获得平衡的数据集。最后,我们在 19 个公共数据集和 3 个合成数据集上进行了实验。实验中使用了 Recall、Accuracy、G-mean、Specificity、AUC 和 F1-Score 六个评价指标。实验结果表明,我们的方法可以显著提高少数人类别的识别率,同时保持多数人类别的高准确率。特别是在 RF 分类器方面,我们的方法在所有评价指标中都名列第一,与表现最差的方法相比,我们的方法的 Recall 差值高达 0.2,显示了其巨大的性能优势。
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引用次数: 0
Deep reinforcement learning-based local path planning in dynamic environments for mobile robot
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1016/j.jksuci.2024.102254
Bodong Tao, Jae-Hoon Kim
Path planning for robots in dynamic environments is a challenging task, as it requires balancing obstacle avoidance, trajectory smoothness, and path length during real-time planning.This paper proposes an algorithm called Adaptive Soft Actor–Critic (ASAC), which combines the Soft Actor–Critic (SAC) algorithm, tile coding, and the Dynamic Window Approach (DWA) to enhance path planning capabilities. ASAC leverages SAC with an automatic entropy adjustment mechanism to balance exploration and exploitation, integrates tile coding for improved feature representation, and utilizes DWA to define the action space through parameters such as target heading, obstacle distance, and velocity In this framework, the action space is defined by DWA’s three weighting parameters: target heading deviation, distance to the nearest obstacle, and velocity. To facilitate the learning process, a non-sparse reward function is designed, incorporating factors such as Time-to-Collision (TTC), heading, and velocity. To validate the effectiveness of the algorithm, experiments were conducted in four different environments, and the algorithm was evaluated based on metrics such as trajectory deviation, smoothness, and time to reach the end point. The results demonstrate that ASAC outperforms existing algorithms in terms of trajectory smoothness, arrival time, and overall adaptability across various scenarios, effectively enabling path planning in dynamic environments.
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引用次数: 0
Semantic similarity on multimodal data: A comprehensive survey with applications
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1016/j.jksuci.2024.102263
Baha Ihnaini , Belal Abuhaija , Ebenezer Atta Mills , Massudi Mahmuddin
Recently, the revival of the semantic similarity concept has been featured by the rapidly growing artificial intelligence research fueled by advanced deep learning architectures enabling machine intelligence using multimodal data. Thus, semantic similarity in multimodal data has gained substantial attention among researchers. However, the existing surveys on semantic similarity measures are restricted to a single modality, mainly text, which significantly limits the capability to understand the intelligence of real-world application scenarios. This study critically reviews semantic similarity approaches by shortlisting 223 vital articles from the leading databases and digital libraries to offer a comprehensive and systematic literature survey. The notable contribution is to illuminate the evolving landscape of semantic similarity and its crucial role in understanding, interpreting, and extracting meaningful information from multimodal data. Primarily, it highlights the challenges and opportunities inherent in different modalities, emphasizing the significance of advancements in cross-modal and multimodal semantic similarity approaches with potential application scenarios. Finally, the survey concludes by summarizing valuable future research directions. The insights provided in this survey improve the understanding and pave the way for further innovation by guiding researchers in leveraging the strength of semantic similarity for an extensive range of real-world applications.
最近,在先进的深度学习架构推动下,利用多模态数据实现机器智能的人工智能研究迅速发展,语义相似性概念也随之复兴。因此,多模态数据中的语义相似性受到了研究人员的极大关注。然而,现有的语义相似性测量研究仅限于单一模态,主要是文本,这极大地限制了理解真实世界应用场景智能的能力。本研究通过从主要数据库和数字图书馆中筛选出 223 篇重要文章,对语义相似性方法进行了批判性评述,从而提供了全面系统的文献调查。本研究的显著贡献在于阐明了语义相似性不断发展的现状及其在理解、解释和从多模态数据中提取有意义信息方面的关键作用。首先,它强调了不同模态固有的挑战和机遇,强调了跨模态和多模态语义相似性方法的进步与潜在应用场景的重要性。最后,调查报告总结了有价值的未来研究方向。本调查报告提供的真知灼见将指导研究人员利用语义相似性的优势为广泛的现实世界应用提供帮助,从而加深理解并为进一步创新铺平道路。
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引用次数: 0
Enhancing Internet of Things communications: Development of a new S-box and multi-layer encryption framework 加强物联网通信:开发新的 S-box 和多层加密框架
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-12-01 DOI: 10.1016/j.jksuci.2024.102265
Adel R. Alharbi , Amer Aljaedi , Abdullah Aljuhni , Moahd K. Alghuson , Hussain Aldawood , Sajjad Shaukat Jamal , Tariq Shah
The growth of IoT applications has revolutionized sectors like security and home automation but raised concerns about data breaches due to device limitations. This research proposes a novel substitution box and cryptographic scheme designed to secure data transmission in IoT devices like smartphones and smartwatches. The proposed research has two phases: (I) generation of a substitution box (S-box) which is proposed by dividing phase space into 256 regions (0–255) using a random initial value and control parameter for the Piecewise Linear Chaotic Map (PWLCM), iterated multiple times, and (ii) a new encryption scheme, which is proposed by employing advanced cryptographic techniques such as bit-plane extraction, diffusion, and a three-stage scrambling process (multiround, multilayer, and recursive). Scrambled data is substituted using multiple S-boxes, followed by XOR operations with random image bit-planes to generate pre-ciphertext. Finally, quantum encryption operations, including Hadamard, CNOT, and phase gates, are applied to produce the fully encrypted image. The research evaluates the robustness of the proposed S-box and encryption scheme through experimental analyses, including nonlinearity, strict avalanche criterion (SAC), linear approximation probability (LAP), bit independence criterion (BIC), key space, entropy, correlation, energy, and histogram variance. The proposed approach demonstrates an impressive statistical performance with key metrics such as nonlinearity of 108.75, SAC of 0.5010, LAP of 0.0903, BIC of 110.65, a key space exceeding 2100, entropy of 7.9998, correlation of 0.0001, and energy of 0.0157. Furthermore, the proposed encryption scheme can encrypt a plaintext image of size 256 × 256 within one second which demonstrates its suitability for IoT devices that require fast computation.
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引用次数: 0
Image stitching algorithm based on two-stage optimal seam line search 基于两阶段最佳缝合线搜索的图像缝合算法
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-23 DOI: 10.1016/j.jksuci.2024.102256
Guijin Han , Yuanzheng Zhang , Mengchun Zhou
Traditional feature matching algorithms often struggle with poor performance in scenarios involving local detail deformations under varying perspectives. Additionally, traditional optimal seamline search-based image stitching algorithms tend to overlook structural and texture information, resulting in ghosting and visible seams. To address these issues, this paper proposes an image stitching algorithm based on a two-stage optimal seamline search. The algorithm leverages a Homography Network as the foundation, incorporating a homography detail-aware network (HDAN) for feature point matching. By introducing a cost volume in the feature matching layer, the algorithm enhances the description of local detail deformation relationships, thereby improving feature matching performance under different perspectives. The two-stage optimal seamline search algorithm designed for image fusion introduces gradient and structural similarity features on top of traditional color-based optimal seamline search algorithms. The algorithm steps include: (1) Searching for structurally similar regions, i.e., high-frequency regions in high-gradient images, and using a color-based graph cut algorithm to search for seamlines within all high-frequency regions, excluding horizontal seamlines; (2) Using a dynamic programming algorithm to complete each vertical seamline, where the pixel energy is comprehensively calculated based on its differences in color and gradient with the surrounding area. The complete seamline energies are then calculated using color, gradient, and structural similarity differences within the seamline neighborhood, and the seamline with the minimum energy is selected as the optimal seamline. A simulation experiment was conducted using 30 image pairs from the UDIS-D dataset (Unsupervised Deep Image Stitching Dataset). The results demonstrate significant improvements in PSNR and SSIM metrics compared to other image stitching algorithms, with PSNR improvements ranging from 5.63% to 11.25% and SSIM improvements ranging from 11.09% to 24.54%, confirming the superiority of this algorithm in image stitching tasks. The proposed image stitching algorithm based on two-stage optimal seamline search, whether evaluated through subjective visual perception or objective data comparison, outperforms other algorithms by enhancing the natural transition of seamlines in terms of structure and texture, reducing ghosting and visible seams in stitched images.
传统的特征匹配算法在涉及不同视角下局部细节变形的情况下往往表现不佳。此外,传统的基于最佳接缝线搜索的图像拼接算法往往会忽略结构和纹理信息,从而导致重影和可见接缝。为了解决这些问题,本文提出了一种基于两阶段最优接缝线搜索的图像拼接算法。该算法以同构网络为基础,结合了用于特征点匹配的同构细节感知网络(HDAN)。通过在特征匹配层引入代价量,该算法增强了对局部细节变形关系的描述,从而提高了不同视角下的特征匹配性能。为图像融合设计的两阶段最优缝合线搜索算法在传统的基于颜色的最优缝合线搜索算法基础上引入了梯度和结构相似性特征。算法步骤包括(1) 搜索结构相似区域,即高梯度图像中的高频区域,使用基于颜色的图切割算法搜索所有高频区域内的接缝线,不包括水平接缝线;(2) 使用动态编程算法完成每条垂直接缝线,根据像素与周围区域的颜色和梯度差异综合计算像素能量。然后利用接缝线邻域内的颜色、梯度和结构相似性差异计算完整的接缝线能量,并选择能量最小的接缝线作为最优接缝线。我们使用 UDIS-D 数据集(无监督深度图像拼接数据集)中的 30 对图像进行了模拟实验。结果表明,与其他图像拼接算法相比,该算法在 PSNR 和 SSIM 指标上有明显改善,PSNR 提高了 5.63% 至 11.25%,SSIM 提高了 11.09% 至 24.54%,这证实了该算法在图像拼接任务中的优越性。所提出的基于两阶段最佳缝合线搜索的图像拼接算法,无论是通过主观视觉感知还是客观数据对比进行评估,都优于其他算法,因为它增强了缝合线在结构和纹理方面的自然过渡,减少了拼接图像中的重影和可见缝。
{"title":"Image stitching algorithm based on two-stage optimal seam line search","authors":"Guijin Han ,&nbsp;Yuanzheng Zhang ,&nbsp;Mengchun Zhou","doi":"10.1016/j.jksuci.2024.102256","DOIUrl":"10.1016/j.jksuci.2024.102256","url":null,"abstract":"<div><div>Traditional feature matching algorithms often struggle with poor performance in scenarios involving local detail deformations under varying perspectives. Additionally, traditional optimal seamline search-based image stitching algorithms tend to overlook structural and texture information, resulting in ghosting and visible seams. To address these issues, this paper proposes an image stitching algorithm based on a two-stage optimal seamline search. The algorithm leverages a Homography Network as the foundation, incorporating a homography detail-aware network (HDAN) for feature point matching. By introducing a cost volume in the feature matching layer, the algorithm enhances the description of local detail deformation relationships, thereby improving feature matching performance under different perspectives. The two-stage optimal seamline search algorithm designed for image fusion introduces gradient and structural similarity features on top of traditional color-based optimal seamline search algorithms. The algorithm steps include: (1) Searching for structurally similar regions, i.e., high-frequency regions in high-gradient images, and using a color-based graph cut algorithm to search for seamlines within all high-frequency regions, excluding horizontal seamlines; (2) Using a dynamic programming algorithm to complete each vertical seamline, where the pixel energy is comprehensively calculated based on its differences in color and gradient with the surrounding area. The complete seamline energies are then calculated using color, gradient, and structural similarity differences within the seamline neighborhood, and the seamline with the minimum energy is selected as the optimal seamline. A simulation experiment was conducted using 30 image pairs from the UDIS-D dataset (Unsupervised Deep Image Stitching Dataset). The results demonstrate significant improvements in PSNR and SSIM metrics compared to other image stitching algorithms, with PSNR improvements ranging from 5.63% to 11.25% and SSIM improvements ranging from 11.09% to 24.54%, confirming the superiority of this algorithm in image stitching tasks. The proposed image stitching algorithm based on two-stage optimal seamline search, whether evaluated through subjective visual perception or objective data comparison, outperforms other algorithms by enhancing the natural transition of seamlines in terms of structure and texture, reducing ghosting and visible seams in stitched images.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102256"},"PeriodicalIF":5.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723733","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}
引用次数: 0
CRNet: Cascaded Refinement Network for polyp segmentation CRNet:用于息肉分割的级联细化网络
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-22 DOI: 10.1016/j.jksuci.2024.102250
Xiaolan Wen , Anwen Zhang , Chuan Lin , Xintao Pang
Technology for automatic segmentation plays a crucial role in the early diagnosis and treatment of ColoRectal Cancer (CRC). Existing polyp segmentation methods often focus on advanced feature extraction while neglecting detailed low-level features, This somewhat limits the enhancement of segmentation performance. This paper proposes a new technique called the Cascaded Refinement Network (CRNet), designed to improve polyp segmentation performance by combining low-level and high-level features through a cascaded contextual network structure. To accurately capture the morphological variations of polyps and enhance the clarity of segmentation boundaries, we have designed the Multi-Scale Feature Optimization (MFO) module and the Contextual Edge Guidance (CEG) module. Additionally, to further enhance feature fusion and utilization, we introduced the Cascaded Local Feature Fusion (CLFF) module, which effectively integrates cross-layer correlations, allowing the network to understand complex polyp structures better. By conducting a large number of experiments, our model achieved a 0.3% and 3.1% higher mDice score than the latest MMFIL-Net in the two main datasets of Kvasir-SEG and CVC-ClinicDB, respectively. Ablation studies show that MFO improves the baseline score by 4%, and the network without CLFF and CEG results in a reduction of 2.4% and 1.7% in mDice scores, respectively. This further validates the contribution of each module to the polyp segmentation performance. CRNet enhances model performance through the introduction of multiple modules but also increases model complexity. Future work will explore how to reduce computational complexity and improve inference speed while maintaining high performance. The source code for this paper can be found at https://github.com/l1986036/CRNet.
自动分割技术在结直肠癌(CRC)的早期诊断和治疗中发挥着至关重要的作用。现有的息肉分割方法往往侧重于高级特征提取,而忽略了详细的低级特征,这在一定程度上限制了分割性能的提高。本文提出了一种名为级联细化网络(CRNet)的新技术,旨在通过级联上下文网络结构结合低级和高级特征来提高息肉分割性能。为了准确捕捉息肉的形态变化并提高分割边界的清晰度,我们设计了多尺度特征优化(MFO)模块和上下文边缘引导(CEG)模块。此外,为了进一步提高特征融合和利用率,我们还引入了级联局部特征融合(CLFF)模块,有效整合了跨层相关性,使网络能够更好地理解复杂的息肉结构。通过大量实验,我们的模型在 Kvasir-SEG 和 CVC-ClinicDB 两个主要数据集中的 mDice 得分分别比最新的 MMFIL-Net 高出 0.3% 和 3.1%。消融研究表明,MFO 可将基线分数提高 4%,而不含 CLFF 和 CEG 的网络可将 mDice 分数分别降低 2.4% 和 1.7%。这进一步验证了每个模块对息肉分割性能的贡献。CRNet 通过引入多个模块提高了模型性能,但也增加了模型的复杂性。未来的工作将探索如何在保持高性能的同时降低计算复杂度和提高推理速度。本文的源代码见 https://github.com/l1986036/CRNet。
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引用次数: 0
Enhancing foreign exchange reserve security for central banks using Blockchain, FHE, and AWS 利用区块链、FHE 和 AWS 加强中央银行的外汇储备安全
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-20 DOI: 10.1016/j.jksuci.2024.102251
Khandakar Md Shafin , Saha Reno
In order to maintain the value of the national currency and control foreign debt, central banks are vital to the management of a nation’s foreign exchange reserves. These reserves, however, are vulnerable to a variety of hazards, including as money laundering, fraud, theft, and cyberattacks. These are issues that traditional financial systems frequently face because of their vulnerabilities and inefficiency. Using modern innovations in a blockchain-based solution can help tackle these serious issues. To protect data privacy, the Microsoft SEAL library is utilized for homomorphic encryption (FHE). For the development of smart contracts, Solidity is employed within the Ethereum blockchain ecosystem. Additionally, Amazon Web Services (AWS) is leveraged to provide a scalable and powerful infrastructure to support our solution. To guarantee safe and effective transaction validation, our method incorporates a hybrid consensus process that combines Proof of Authority (PoA) with Byzantine Fault Tolerance (BFT). The administration of foreign exchange reserves by central banks is made more secure, transparent, and operationally efficient by this all-inclusive approach.
为了保持本国货币的价值和控制外债,中央银行对国家外汇储备的管理至关重要。然而,这些储备容易受到各种危害的影响,包括洗钱、欺诈、盗窃和网络攻击。这些都是传统金融系统因其脆弱性和低效率而经常面临的问题。在基于区块链的解决方案中使用现代创新技术有助于解决这些严重问题。为了保护数据隐私,微软 SEAL 库被用于同态加密(FHE)。为了开发智能合约,在以太坊区块链生态系统中使用了 Solidity。此外,亚马逊网络服务(AWS)为支持我们的解决方案提供了可扩展的强大基础设施。为了保证安全有效的交易验证,我们的方法采用了混合共识流程,将权威证明(PoA)与拜占庭容错(BFT)相结合。通过这种包罗万象的方法,中央银行对外汇储备的管理变得更加安全、透明和高效。
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引用次数: 0
Improving cache-enabled D2D communications using actor–critic networks over licensed and unlicensed spectrum 在许可和非许可频谱上利用行为批评网络改进支持缓存的 D2D 通信
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-19 DOI: 10.1016/j.jksuci.2024.102249
Muhammad Sheraz , Teong Chee Chuah , Kashif Sultan , Manzoor Ahmed , It Ee Lee , Saw Chin Tan
Cache-enabled Device-to-Device (D2D) communications is an effective way to improve data sharing. User Equipment (UE)-level caching holds the potential to reduce the data traffic burden on the core network. Licensed spectrum is utilized for D2D communications, but due to spectrum scarcity, exploring unlicensed spectrum is essential to enhance network capacity. In this paper, we propose caching at the UE-level and exploit both licensed and unlicensed spectrum for optimizing throughput. First, we propose a reinforcement learning-based data caching scheme leveraging an actor–critic network to improve cache-enabled D2D communications. Besides, licensed and unlicensed spectrum are devised for D2D communications considering interference from existing cellular and Wi-Fi users. A duty cycle-based unlicensed spectrum access algorithm is employed, guaranteeing the Signal-to-Interference and Noise Ratio (SINR) required by the users. The unlicensed spectrum is prone to data packets collisions. Therefore, Request-to-Send/Clear-to-Send (RTS/CTS) mechanism is utilized in conjunction with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to alleviate both the interference and packets collision problems of the unlicensed spectrum. Extensive simulations are performed to analyze the performance gain of our proposed scheme compared to the benchmarks under different network scenarios. The obtained results demonstrate that our proposed scheme possesses the potential to optimize network performance.
支持缓存的设备到设备(D2D)通信是改善数据共享的有效方法。用户设备(UE)级缓存有可能减轻核心网络的数据流量负担。许可频谱可用于 D2D 通信,但由于频谱稀缺,探索非许可频谱对提高网络容量至关重要。在本文中,我们提出了 UE 级缓存,并利用许可和非许可频谱优化吞吐量。首先,我们提出了一种基于强化学习的数据缓存方案,利用行为批判网络改善缓存支持的 D2D 通信。此外,考虑到现有蜂窝和 Wi-Fi 用户的干扰,我们还为 D2D 通信设计了许可和非许可频谱。采用了基于占空比的非授权频谱接入算法,保证了用户所需的信噪比(SINR)。未授权频谱容易发生数据包碰撞。因此,请求发送/清除发送(RTS/CTS)机制与带碰撞避免功能的载波侦测多路访问(CSMA/CA)相结合,可减轻非授权频谱的干扰和数据包碰撞问题。我们进行了广泛的仿真,分析了在不同网络场景下,我们提出的方案与基准方案相比的性能增益。结果表明,我们提出的方案具有优化网络性能的潜力。
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引用次数: 0
L2-MA-CPABE: A ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof L2-MA-CPABE:一种集成了区块链和链外计算、具有零知识证明的密文访问控制方案
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-19 DOI: 10.1016/j.jksuci.2024.102247
Zhixin Ren, Yimin Yu, Enhua Yan, Taowei Chen
To enhance the security of ciphertext-policy attribute-based encryption (CP-ABE) and achieve fully distributed key generation (DKG), this paper proposes a ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof based on Layer-2 and multi-authority CP-ABE. Firstly, we enhance the system into two layers and construct a Layer-2 distributed key management service framework. This framework improves system efficiency and scalability while reducing costs. Secondly, we design the proof of trust contribution (PoTC) consensus algorithm to elect high-trust nodes responsible for DKG and implement an incentive mechanism for key computation through smart contract design. Finally, we design a non-interactive zero-knowledge proof protocol to achieve correctness verification of off-chain key computation. Security analysis and simulation experiments demonstrate that our scheme achieves high security while significantly improving system performance. The time consumption for data users to obtain attribute private keys is controlled at tens of milliseconds.
为了增强基于密文策略属性的加密(CP-ABE)的安全性,实现全分布式密钥生成(DKG),本文提出了一种基于Layer-2和多授权CP-ABE的集区块链和链外计算与零知识证明于一体的密文访问控制方案。首先,我们将系统增强为两层,并构建了第二层分布式密钥管理服务框架。该框架提高了系统效率和可扩展性,同时降低了成本。其次,我们设计了信任贡献证明(PoTC)共识算法,选出负责 DKG 的高信任节点,并通过智能合约设计实现了密钥计算的激励机制。最后,我们设计了一种非交互式零知识证明协议,以实现链外密钥计算的正确性验证。安全分析和仿真实验证明,我们的方案在显著提高系统性能的同时实现了高安全性。数据用户获取属性私钥的时间消耗控制在几十毫秒。
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引用次数: 0
期刊
Journal of King Saud University-Computer and Information Sciences
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