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A Novel Object Detection Model Using Gazelle White Shark Optimization With Enhanced FRCNN 基于增强FRCNN的瞪羚白鲨优化目标检测模型
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-02 DOI: 10.1002/ett.70305
F. A. Princi Rani, N. Muthukumaran

Object detection has gradually developed into a popular research area because of the rife use of Remote Sensing Images (RSIs) in military and civil domains. However, complex backgrounds and problems with small items are the two biggest obstacles in object detection. Deep learning techniques have a significant advantage for object detection over conventional methods that rely on manually derived characteristics, and they have a lot of attention. Due to the wide range of objects and complex visual backgrounds in RSIs, current deep-learning algorithms in the area of RSI object detection leave much to be desired. For this case, algorithms need to be specifically optimized. In this paper, a novel optimization-driven Enhanced Faster Region Convolutional Neural Network (FRCNN) is proposed for object detection. This approach has five modules, namely pre-processing, data augmentation, segmentation, feature extraction, and object detection. The detection process is performed using Enhanced FRCNN and it is trained by the proposed Gazelle White Shark Optimization Algorithm (GWSOA). Extensive experiments in high-resolution RSI data sets have exposed the efficacy of the proposed approach. The novel approach achieved better accuracy of 97.31%, Mean Average Precision (MAP) of 97.85%, precision of 97.76%, recall of 96.16%, F-score of 95.78%, and error rate of 2.69.

由于遥感图像在军事和民用领域的广泛应用,目标检测逐渐发展成为一个热门的研究领域。然而,复杂的背景和小物体的问题是物体检测的两个最大障碍。深度学习技术在目标检测方面比依赖手动导出特征的传统方法具有显着优势,并且受到了很多关注。由于RSI对象范围广,视觉背景复杂,目前在RSI对象检测领域的深度学习算法还有很大的不足。对于这种情况,需要对算法进行专门的优化。本文提出了一种新的优化驱动的增强快速区域卷积神经网络(FRCNN)用于目标检测。该方法包括预处理、数据增强、分割、特征提取和目标检测五个模块。检测过程采用增强型FRCNN进行,并采用提出的瞪羚白鲨优化算法(GWSOA)进行训练。在高分辨率RSI数据集上进行的大量实验已经揭示了所提出方法的有效性。该方法准确率为97.31%,平均精密度(MAP)为97.85%,精密度为97.76%,召回率为96.16%,f分数为95.78%,错误率为2.69。
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引用次数: 0
Prioritized Constraint Aware Task Offloading Mechanism in Cloud-Fog Computing Using Deep Reinforcement Learning 基于深度强化学习的云雾计算优先约束感知任务卸载机制
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-02 DOI: 10.1002/ett.70299
Sambeet Patro, Sangram Keshari Swain, S. Sudheer Mangalampalli

The rapid emergence of Internet of Things (IoT) applications such as smart cities, intelligent transportation, healthcare, logistics, and wearable systems has increased the demand for scalable and low-latency computing infrastructure. Despite the widespread adoption of traditional cloud computing platforms to handle these applications, offloading all tasks to centralized cloud data centers results in significant latency, high energy consumption, and increased operational costs. Many of these applications are delay-sensitive and computationally intensive, requiring immediate decision-making and localized processing. To address these limitations, we propose a prioritized constraint-aware task offloading mechanism (PCATOM) that efficiently schedules and offloads tasks by considering task-level and resource-level constraints in a fog-cloud computing environment. PCATOM leverages the Deep Deterministic Policy Gradient (DDPG) algorithm, a policy gradient reinforcement learning method designed to balance exploration and exploitation while dynamically learning optimal offloading strategies. The framework reduces overall latency, energy consumption, and execution cost by making intelligent decisions about where and how to offload tasks. PCATOM was implemented using the SimPy simulation framework and evaluated using a combination of statistical workloads and real-world parallel computing traces from NASA and HPC2N. Experimental results show that PCATOM consistently outperforms baseline models such as DQN and A2C, achieving up to 32.5% lower latency, 28.7% lower energy consumption, and 18.4% higher throughput. These results demonstrate the effectiveness and scalability of PCATOM in dynamic and diverse fog-cloud environments.

物联网(IoT)应用(如智慧城市、智能交通、医疗保健、物流和可穿戴系统)的快速出现增加了对可扩展和低延迟计算基础设施的需求。尽管人们广泛采用传统的云计算平台来处理这些应用程序,但将所有任务转移到集中式云数据中心会导致严重的延迟、高能耗和增加的运营成本。这些应用程序中的许多都是延迟敏感和计算密集型的,需要立即决策和本地化处理。为了解决这些限制,我们提出了一种优先约束感知任务卸载机制(PCATOM),该机制通过考虑雾云计算环境中的任务级和资源级约束,有效地调度和卸载任务。PCATOM利用深度确定性策略梯度(DDPG)算法,这是一种策略梯度强化学习方法,旨在平衡探索和开发,同时动态学习最佳卸载策略。该框架通过对在何处以及如何卸载任务做出明智的决策,减少了总体延迟、能耗和执行成本。PCATOM使用SimPy仿真框架实现,并使用统计工作负载和来自NASA和HPC2N的真实并行计算跟踪的组合进行评估。实验结果表明,PCATOM始终优于DQN和A2C等基准模型,延迟降低了32.5%,能耗降低了28.7%,吞吐量提高了18.4%。这些结果证明了PCATOM在动态和多样化雾云环境中的有效性和可扩展性。
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引用次数: 0
Innovative Design of Gingham Pattern Patch Antenna Integrated With Kagome Lattice Photonic Crystal Structure for Terahertz Applications 结合Kagome晶格光子晶体结构的Gingham贴片天线在太赫兹应用中的创新设计
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-02 DOI: 10.1002/ett.70303
Prem Anand Madeswaran, Sivakumar Dhandapani, Thirumaraiselvan Packirisamy

The terahertz (THz) range is perfect for high-throughput applications like enhanced sensing, ultra-fast wireless backhauls, and real-time virtual and augmented reality because of its availability of unoccupied spectrum, which permits unparalleled bandwidths. The novel THz designs and components are being propelled by recent developments in nanomaterials, photonic devices, and intelligent surfaces. Additionally, the integration of THz waves with sensing and security capabilities opens up new paradigms in secure communication, healthcare applications, and intelligent settings. To achieve reliable and extensible THz communication systems, this article delves into the necessary technologies, propagation properties, and future study paths. Photonic crystals (PhC) have recently gained popularity for use in cutting-edge electromagnetic fields because of their remarkable controllability over the transmission of electromagnetic waves. The use of PhC ideas along with old and new antenna designs offers a way to create antennas that are very directional, slim, and adjustable. This article proposes a new design that combines the Kagome lattice PhC structure with a modified patch antenna that uses a Gingham sequence. The air holes in the Kagome lattice are optimized to enhance antenna characteristics like return loss (RL), gain, voltage standing wave ratio (VSWR), directivity, and so on. The proposed Kagome lattice PhC antenna works at THz frequency; hence, it is suitable for medical applications like cancer detection, and so on.

太赫兹(THz)范围非常适合高吞吐量应用,如增强传感、超高速无线回程以及实时虚拟和增强现实,因为它具有未占用频谱的可用性,允许无与伦比的带宽。纳米材料、光子器件和智能表面的最新发展推动了新型太赫兹设计和组件的发展。此外,太赫兹波与传感和安全功能的集成在安全通信、医疗保健应用和智能设置中开辟了新的范例。为了实现可靠和可扩展的太赫兹通信系统,本文深入研究了必要的技术、传播特性和未来的研究方向。光子晶体(PhC)由于其对电磁波传输的显著可控性,最近在尖端电磁场中得到了广泛的应用。将PhC的理念与新旧天线设计相结合,提供了一种创造定向、纤薄和可调节的天线的方法。本文提出了一种将Kagome晶格PhC结构与使用Gingham序列的改进贴片天线相结合的新设计。优化了Kagome晶格中的气孔,以提高天线的回波损耗(RL)、增益、电压驻波比(VSWR)、指向性等特性。所提出的Kagome晶格PhC天线工作在太赫兹频率;因此,它适用于癌症检测等医疗应用。
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引用次数: 0
AI-Enhanced Signal Detection and Channel Estimation for Beyond 5G and 6G Wireless Networks 超5G和6G无线网络的ai增强信号检测和信道估计
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-02 DOI: 10.1002/ett.70306
Muhammad Yunis Daha, Bibin Babu, Rizwan Qureshi, Muhammad Usman Hadi

Integrating deep learning (DL) with massive multiple input multiple output (ma-MIMO) technology has provided a framework for designing new communication systems for next-generation technologies such as sixth-generation (6G) networks. However, due to huge transmitting and receiving antenna sizes, channel estimation and signal detection become a very challenging job at the receiver side. To address the channel estimation and signal detection problem in ma-MIMO systems, this paper presents two system frameworks by considering the two scenarios based on channel information at the receiver end. In scenario 1, the Channel matrix is unknown at the base station (BS), and to ensure accurate channel estimation, the pilot symbols are integrated with the transmitted symbol in the ma-MIMO systems. Based on Scenario 1, this paper proposes an optimized pilot-assisted feedforward network for channel estimation called FF-PCNet in the ma-MIMO system. In scenario 2, the channel matrix is fully known at the BS and uses this exact information for signal detection in the ma-MIMO systems. Based on scenario 2, this paper proposes two methods for signal detection in ma-MIMO systems. The proposed method-1 is based on an optimized long short-term memory-based detection network called LSTM-DetNet, and method-2 is based on an optimized and customized feed-forward detection network called FF-DetNet for signal detection in the ma-MIMO systems. Numerical results show that, for channel estimation, the FF-PCNet performs excellently and achieves a 40.2% low average error per symbol compared to the benchmark traditional MIMO estimator known as least squares estimation (LSE). For signal detection, although method 1, known as LSTM-DetNet, achieves better performance than other benchmark MIMO detectors, yet unable to beat the AIDETECT MIMO detector. However, our second proposed method, known as FF-DetNet, not only achieves better SER performance ranging between 73.2% to 99.993% for both MIMO and ma-MIMO systems but has also been able to achieve much lower computational complexity compared to benchmark artificial intelligence (AI)-based MIMO detectors.

将深度学习(DL)与大规模多输入多输出(ma-MIMO)技术相结合,为第六代(6G)网络等下一代技术设计新的通信系统提供了框架。然而,由于发射和接收天线的巨大尺寸,信道估计和信号检测成为接收端非常具有挑战性的工作。为了解决ma-MIMO系统中的信道估计和信号检测问题,本文基于接收端信道信息,考虑了两种场景,提出了两种系统框架。在场景1中,基站(BS)的信道矩阵是未知的,为了保证准确的信道估计,在ma-MIMO系统中将导频符号与发射符号相结合。基于场景1,本文提出了一种优化的导频辅助前馈信道估计网络,称为FF-PCNet。在场景2中,信道矩阵在BS上是完全已知的,并在ma-MIMO系统中使用该精确信息进行信号检测。基于场景2,本文提出了两种多模多输出系统的信号检测方法。提出的方法1基于优化的长短期记忆检测网络LSTM-DetNet,方法2基于优化和定制的前馈检测网络FF-DetNet,用于ma-MIMO系统中的信号检测。数值结果表明,对于信道估计,FF-PCNet表现优异,与传统的MIMO估计器最小二乘估计(LSE)相比,每个符号的平均误差低40.2%。对于信号检测,虽然方法1(称为LSTM-DetNet)的性能优于其他基准MIMO检测器,但无法击败AIDETECT MIMO检测器。然而,我们提出的第二种方法,称为FF-DetNet,不仅在MIMO和ma-MIMO系统中实现了更好的SER性能,范围在73.2%至99.993%之间,而且与基于人工智能(AI)的基准MIMO检测器相比,还能够实现更低的计算复杂度。
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引用次数: 0
Correction to “QDKFFHNet: Quantum Dilated Kronecker Feed Forward Harmonic Net for Intrusion Detection in IoT-Based Smart City Applications” 修正“QDKFFHNet:基于物联网的智慧城市应用中入侵检测的量子扩张Kronecker前馈谐波网”
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-12-01 DOI: 10.1002/ett.70316

S. Ravindran, and V. Sarveshwaran, “QDKFFHNet: Quantum Dilated Kronecker Feed Forward Harmonic Net for Intrusion Detection in IoT-Based Smart City Applications,” Transactions on Emerging Telecommunications Technologies 36 (2025): e70141, https://doi.org/10.1002/ett.70141.

The affiliation for the authors Selvam Ravindran and Velliangiri Sarveshwaran was incomplete. The complete affiliation is provided below

Selvam Ravindran, Velliangiri Sarveshwaran

Department of Computational Intelligence, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu – 603203, Tamil Nadu, India

We apologize for this error.

S. Ravindran和V. Sarveshwaran,“QDKFFHNet:基于物联网的智慧城市应用中的量子膨胀Kronecker前馈谐波网络”,新兴电信技术学报36 (2025):e70141, https://doi.org/10.1002/ett.70141.The作者的联系不完整。完整的联系如下:selvam Ravindran, Velliangiri sarveshwaran,工程技术学院计算学院计算智能系,SRM科学技术研究所,Kattankulathur校区,Chengalpattu - 603203, Tamil Nadu, india .我们为这个错误道歉。
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引用次数: 0
Correlation Analysis of ADS-B Data Jump and UAV Positioning Error Combined With Data Mining Algorithm 结合数据挖掘算法的ADS-B数据跳变与无人机定位误差相关性分析
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-11-30 DOI: 10.1002/ett.70297
Wei Sun

Aiming at the problem that ADS-B (Automatic Dependent Surveillance Broadcast) data jump characteristics and multi-source heterogeneous errors are difficult to dynamically associate, this paper constructs a data mining framework that combines time series anomaly detection and multi-modal feature fusion. First, this paper performs missing value interpolation, time alignment and standardization preprocessing on the collected ADS-B data, energy status parameters, material environment parameters and flight environment information; secondly, based on the LSTM-AE (Long Short-Term Memory-Autoencoder) model, an unsupervised model for ADS-B data jump detection is constructed, which identifies the jump point by reconstructing the error and extracts jump features with physical significance. Finally, this paper introduces the Transformer architecture, cross-modally fuses the jump features with multi-source heterogeneous parameters, constructs a semantic alignment mechanism, and combines the error evolution modeling strategy to explore the complex influence mechanism of different factors on positioning errors. Experimental results show that the proposed method has better jump recognition accuracy (average 90.7%) and error prediction mean square error (average 5.81) than Isolation Forest and Variational Autoencoder (VAE), providing a new technical path for improving the positioning reliability and safety control of unmanned aerial vehicles (UAVs).

针对ADS-B (Automatic Dependent Surveillance Broadcast)数据跳跃特征与多源异构错误难以动态关联的问题,构建了时间序列异常检测与多模态特征融合相结合的数据挖掘框架。首先,对采集到的ADS-B数据、能量状态参数、材料环境参数和飞行环境信息进行缺失值插值、时间对准和标准化预处理;其次,基于LSTM-AE (Long - short - short记忆- autoencoder)模型,构建ADS-B数据跳变检测的无监督模型,通过重构误差识别跳变点,提取具有物理意义的跳变特征;最后,引入Transformer体系结构,跨模态融合多源异构参数的跳跃特征,构建语义对齐机制,结合误差演化建模策略,探索不同因素对定位误差的复杂影响机制。实验结果表明,该方法比隔离森林和变分自编码器(VAE)具有更好的跳位识别精度(平均90.7%)和误差预测均方误差(平均5.81),为提高无人机的定位可靠性和安全控制提供了新的技术路径。
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引用次数: 0
DSecNet: An Enhanced Cyber Security System for Authentication, Intrusion Detection, and Risk Level Prediction in Information Systems Using Weighted RBM Features-Based DeepCapsNet DSecNet:一种基于加权RBM特征的增强信息系统认证、入侵检测和风险等级预测的网络安全系统
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-11-27 DOI: 10.1002/ett.70298
R. Lakshman Naik, Sourabh Jain

The traditional cybersecurity solution cannot offer the necessary protection against all forms of cyberattacks for management systems since cyberattacks are of a scattered nature. Thus, an efficient security method is suggested in this work to prevent data loss from the information system using optimization and deep learning. The major aim of this work is to offer suitable recommendations for developing a cybersecurity decision support model for information systems with risk analysis and cybersecurity models. In the information system, the security threats are eliminated using this model. So, the authentication, prediction of the risk levels, and the mitigation of the security threats are regarded as the major objectives of this research work. From the CICIDS 2017 KNN dataset, UNSW-NB15 dataset, and NSL-KDD dataset, the data is collected. The collected data is given to the executed Deep Security Network (DSecNet). In the DSecNet, the Restricted Boltzmann Machine (RBM) is utilized to retrieve the features and then the extracted features from the RBM are fused with optimized weights, which are selected using the Enhanced Sandpiper Optimization Algorithm (ESOA). The obtained weighted features are further provided to the Deep Capsule Neural Network (DeepCapsNet). The implemented DSecNet is used for verifying the authentication of the user, detecting the intrusion, and determining the associated risk levels caused by the attacks on the information system. Once the threats are detected, the mitigation of the threat based on their risk level is carried out. The effectiveness of the proposed model is proven by the validation process. From the simulation outcomes, the accuracy rate of the developed model is 97.11% in the NSL-KDD dataset and 96.70% in the UNSW-NB15 dataset based on intrusion detection performance. Therefore, the efficacy of the developed security system is higher for the performance of the authentication, intrusion detection, and risk prediction, which elevates the security of the cyber networks.

传统的网络安全解决方案无法为管理系统提供必要的保护,防止各种形式的网络攻击,因为网络攻击具有分散的性质。因此,本文提出了一种利用优化和深度学习来防止信息系统数据丢失的有效安全方法。这项工作的主要目的是为开发具有风险分析和网络安全模型的信息系统的网络安全决策支持模型提供合适的建议。在信息系统中,利用该模型可以消除安全威胁。因此,验证、预测风险等级和缓解安全威胁是本研究的主要目标。从CICIDS 2017 KNN数据集、UNSW-NB15数据集和NSL-KDD数据集中收集数据。收集到的数据被提供给执行的深度安全网络(DSecNet)。在DSecNet中,利用受限玻尔兹曼机(Restricted Boltzmann Machine, RBM)检索特征,然后将提取的特征与优化权值进行融合,利用增强矶鹞优化算法(Enhanced Sandpiper Optimization Algorithm, ESOA)选择优化权值。得到的加权特征进一步提供给深度胶囊神经网络(DeepCapsNet)。实现的DSecNet用于验证用户的身份验证、检测入侵、判断信息系统受到攻击所带来的风险等级。一旦检测到威胁,就会根据威胁的风险级别来减轻威胁。验证过程证明了该模型的有效性。仿真结果表明,基于入侵检测性能,该模型在NSL-KDD数据集上的准确率为97.11%,在UNSW-NB15数据集上的准确率为96.70%。因此,所开发的安全系统在身份验证、入侵检测和风险预测等方面的效能更高,提高了网络的安全性。
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引用次数: 0
Hybrid Bio-Inspired Combined Deep Learning Model for DDoS Attack Detection in Cloud: A Big Data Perspective 云中DDoS攻击检测的混合生物启发组合深度学习模型:大数据视角
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-11-25 DOI: 10.1002/ett.70300
Perumal Radhika, Somasundaram Kamalakkannan

One of the most prevalent attacks that cause significant harm and impair cloud performance is Distributed Denial of Service (DDoS). DDoS attacks pose a significant threat to cloud environments, degrading performance and disrupting services. To address this issue, we propose a hybrid bio-inspired deep learning model for DDoS attack detection that leverages big data analytics in the cloud. The proposed model incorporates a MapReduce framework to efficiently process large-scale network traffic data, extracting crucial features such as raw features, packet-based features, improved correlations, and statistical features. These extracted features are further refined using an improved recursive feature elimination (RFE) method, which selects the most relevant attributes for attack detection. The attack detection phase employs a hybrid classifier (HC) that integrates Long Short-Term Memory (LSTM) and Deep MaxOut (DMO) models. To ensure optimal performance, the weights of LSTM and DMO are fine-tuned using the White Shark Updated Remora Optimization (WSU-ROA), enhancing classification accuracy. The proposed HC + WSU-ROA model outperforms other methods, achieving the highest accuracy of 93.98%, compared to the other existing methods, demonstrating its superior effectiveness in DDoS attack detection.

分布式拒绝服务(DDoS)是造成重大伤害和损害云性能的最常见攻击之一。DDoS攻击对云环境造成严重威胁,会导致性能下降和业务中断。为了解决这个问题,我们提出了一种混合生物启发的深度学习模型,用于DDoS攻击检测,利用云中的大数据分析。该模型结合MapReduce框架有效地处理大规模网络流量数据,提取关键特征,如原始特征、基于数据包的特征、改进的相关性和统计特征。这些提取的特征使用改进的递归特征消除(RFE)方法进一步细化,该方法选择最相关的属性进行攻击检测。攻击检测阶段采用混合分类器(HC),该分类器集成了LSTM (Long - Short-Term Memory)和DMO (Deep MaxOut)模型。为了保证最优的性能,LSTM和DMO的权重使用了White Shark Updated Remora Optimization (WSU-ROA)进行微调,提高了分类精度。本文提出的HC + WSU-ROA模型优于其他方法,与其他现有方法相比,准确率最高,达到93.98%,证明了其在DDoS攻击检测中的优越性。
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引用次数: 0
RIS-Aided mmWave MIMO Channel Estimation Using Rotation-Invariant Coordinate Convolutional Neural Network and Compressive Sensing 基于旋转不变坐标卷积神经网络和压缩感知的ris辅助毫米波MIMO信道估计
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-11-23 DOI: 10.1002/ett.70289
Madhu Kumar Vanteru, Martin Margala, Sri Hari Nallamala, Prasun Chakrabarti

Channel State Information (CSI) is necessary for wireless systems supported by reconfigurable intelligent surfaces to regulate wireless channels and increase bandwidth along energy efficiency. In this paper, RIS-Aided mmWave MIMO Channel Estimation with Rotation-Invariant Coordinate Convolutional Neural Network and Compressive Sensing (RIS-MIMO-CE-RICCNN) is proposed. Reconfigurable Intelligent Surface (RIS) is utilized to estimate frequency-flat and frequency-selective cascaded channels with minimal overhead in multi-user millimeter wave big multiple inputs multiple outputs (MIMO) systems. The unique angle cascaded channels visible to different users have totally shared non-zero rows including user-specific column supports; it makes use of both the double-structured sparsity property of angular cascaded channel (ACC) matrices and the common sparsity property among the various subcarriers. Rotation-Invariant Coordinate Convolutional Neural Networks (RICCNN) is used to accurately detect channel supports. Then, channel estimation is done by Harbor Seal Whiskers Optimization Algorithm (HSWOA). The performance metrics like Signal to Noise Ratio (SNR), Normalized mean square error (NMSE), Bit error rate (BER), Peak-signal to noise ratio (PSNR), Computational complexity, Loss function, BER versus SNR and energy consumption are evaluated. The performance of the RIS-MIMO-CE-RICCNN technique is evaluated against existing methods. The RIS-MIMO-CE-RICCNN achieves 16.28%, 30.78% and 25.29% lower SNR, 15.08%, 20.58%, and 15.25% lower NMSE, 28.96%, 30.21%, and 23.89% lower BER, and 26.28%, 31.26%, 19.66% lower PSNR when compared to the existing models: RIS-assisted mmWave MIMO channel estimation utilizing deep learning with compressive sensing (RIS-MIMO-CE-CS), channel estimation for reconfigurable intelligent surface enabled multiple user mmWave MIMO systems (RIS-MIMO-CE-MU), Beam Pattern along Reflection Pattern Design of Channel Estimation in RIS-Assisted mmWave MIMO Schemes (BPRPD-CE-RIS-MIMO)respectively.

信道状态信息(CSI)对于可重构智能表面支持的无线系统来说是必要的,以调节无线信道并增加带宽和能效。提出了基于旋转不变坐标卷积神经网络和压缩感知的ris -MIMO辅助毫米波MIMO信道估计方法(RIS-MIMO-CE-RICCNN)。在多用户毫米波大多输入多输出(MIMO)系统中,利用可重构智能表面(RIS)以最小开销估计频率平坦和频率选择性级联信道。不同用户可见的独特角度级联通道完全共享非零行,包括用户特定的列支持;它既利用了角级联信道(ACC)矩阵的双结构稀疏性,又利用了各子载波之间的共同稀疏性。采用旋转不变坐标卷积神经网络(RICCNN)精确检测通道支撑点。然后,利用斑海豹须优化算法(HSWOA)进行信道估计。对信噪比(SNR)、归一化均方误差(NMSE)、误码率(BER)、峰值信噪比(PSNR)、计算复杂度、损失函数、误码率与信噪比以及能耗等性能指标进行了评估。对比现有方法对RIS-MIMO-CE-RICCNN技术的性能进行了评价。与现有模型相比,RIS-MIMO-CE-RICCNN的信噪比分别降低了16.28%、30.78%和25.29%,NMSE分别降低了15.08%、20.58%和15.25%,BER分别降低了28.96%、30.21%和23.89%,PSNR分别降低了26.28%、31.26%和19.66%。利用压缩感知深度学习的ris辅助毫米波MIMO信道估计(RIS-MIMO-CE-CS),可重构智能表面多用户毫米波MIMO系统的信道估计(RIS-MIMO-CE-MU), ris辅助毫米波MIMO方案中沿反射方向波束方向的信道估计设计(BPRPD-CE-RIS-MIMO)。
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引用次数: 0
AI-Driven Anomaly Detection and Traffic Management in Software-Defined IoT Networks for Smart Agriculture 智能农业软件定义物联网网络中人工智能驱动的异常检测和流量管理
IF 2.5 4区 计算机科学 Q3 TELECOMMUNICATIONS Pub Date : 2025-11-23 DOI: 10.1002/ett.70301
Muzammal Majeed, Muhammad Ashfaq, Preeti Rani, Afzaal Hussain, Muhammad Azam Zia

Smart agriculture uses Internet of Things (IoT) and sensors to increase productivity, optimize resource use, and enhance decision-making. However, the large and varied agricultural data sets pose challenges for traffic management and network security. Traditional software-defined networking (SDN) offers centralized, flexible traffic control, but it cannot detect malicious or abnormal activities in real-time. To fill this gap, we introduce an Artificial Intelligence (AI)-based anomaly detection and traffic management framework for SD-IoT networks in smart agriculture. This system combines a machine learning (ML) Intrusion Detection System (IDS) with the SDN controller, enabling real-time identification of abnormal traffic and adaptive prioritization of critical data flows. The IDS detects unusual traffic patterns, while the SDN controller allocates resources and routes flows based on quality of service (QoS) and security needs. Simulations demonstrate that our system achieves high accuracy in anomaly detection, reduces latency for emergency flows, enhances throughput, and reduces packet loss compared to traditional methods. This work highlights the importance of integrating AI-powered IDS with SDN traffic management to enhance security and communication in smart agricultural networks.

智慧农业利用物联网(IoT)和传感器来提高生产力、优化资源利用和加强决策。然而,庞大而多样的农业数据集给交通管理和网络安全带来了挑战。传统的软件定义网络(SDN)提供集中、灵活的流量控制,但无法实时检测恶意或异常活动。为了填补这一空白,我们为智能农业中的SD-IoT网络引入了基于人工智能(AI)的异常检测和流量管理框架。该系统将机器学习(ML)入侵检测系统(IDS)与SDN控制器相结合,能够实时识别异常流量并自适应确定关键数据流的优先级。IDS检测异常流量模式,SDN控制器根据QoS (quality service)和安全需求分配资源和路由流。仿真结果表明,与传统方法相比,该系统在异常检测方面具有较高的准确性,减少了紧急流的延迟,提高了吞吐量,减少了丢包。这项工作强调了将人工智能驱动的IDS与SDN流量管理集成在一起以增强智能农业网络的安全性和通信的重要性。
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Transactions on Emerging Telecommunications Technologies
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