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Efficient Analytics and Fuzzy Call Admission Control With Adaptive Scheduling for Enhanced Quality of Service in 6G IoT for Multimedia Streaming 基于自适应调度的高效分析和模糊呼叫接纳控制提高6G物联网多媒体流的服务质量
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70392
G. Nanthakumar, J. Chandra Priya, K. Karthika, A. Jyothi Babu

The increasing demand for stringent quality of service (QoS) guarantees and low latency in mission and time-critical 6G internet of things (IoT) applications necessitates advanced call admission control (CAC) mechanisms. Although 6G networks offer enhanced capabilities, resource limitations like bandwidth and processing power persist. Consequently, efficient resource allocation strategies are crucial to balancing the needs of diverse services, particularly for real-time streaming applications. This paper introduces a novel Analytics & Fuzzy Call Admission Control (AF-CAC) algorithm combined with an Adaptive Leaky Bucket (ALB) scheduling mechanism. The AF-CAC algorithm integrates predictive analytics and fuzzy logic to make informed, intelligent admission decisions, ensuring reliable communication and optimal resource utilization. It prioritizes critical data transmission and prevents network overloading by controlling the number of admitted calls. Concurrently, the ALB mechanism dynamically adapts to changing network conditions, user mobility, and traffic patterns, efficiently allocating resources to meet diverse QoS requirements. The proactive resource allocation is enhanced with a convolutional neural network (CNN) and reinforcement learning (RL) agents, enabling dynamic and efficient resource management to ensure high-quality multimedia streaming, support mission-critical applications, and maintain uninterrupted service during mobility. The average throughput for the AF-CAC technique is 1350 packets/slot, and the average delay over the range of 300 simulated devices is 840 ms. Hence, it exhibits significant enhancements in admitting connections and overall QoS compared to existing approaches for managing multimedia traffic in 6G networks.

在任务和时间关键型6G物联网(IoT)应用中,对严格的服务质量(QoS)保证和低延迟的需求日益增长,需要先进的呼叫接纳控制(CAC)机制。尽管6G网络提供了增强的功能,但带宽和处理能力等资源限制仍然存在。因此,有效的资源分配策略对于平衡各种服务的需求至关重要,特别是对于实时流应用程序。本文介绍了一种结合自适应漏桶调度机制的分析模糊呼叫接纳控制(AF-CAC)算法。AF-CAC算法集成了预测分析和模糊逻辑,以做出明智的录取决策,确保可靠的通信和最佳的资源利用。它可以优先传输关键数据,并通过控制允许的呼叫数量来防止网络过载。同时,ALB机制能够动态适应不断变化的网络状况、用户移动性和流量模式,有效地分配资源,满足不同的QoS需求。通过卷积神经网络(CNN)和强化学习(RL)代理增强了主动资源分配,实现了动态高效的资源管理,以确保高质量的多媒体流,支持关键任务应用程序,并在移动期间保持不间断的服务。AF-CAC技术的平均吞吐量为1350个数据包/插槽,在300个模拟设备范围内的平均延迟为840 ms。因此,与6G网络中管理多媒体流量的现有方法相比,它在允许连接和总体QoS方面表现出显著的增强。
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引用次数: 0
Optimized Resource Allocation and Caching for Integrated Satellite–Terrestrial Networks Using Multiagent Deep Learning 基于多智能体深度学习的星地集成网络资源优化分配与缓存
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70366
Kalyan Kumar G, Sankar P

The amalgamation of terrestrial and satellite networks for nonorthogonal multiple access services presents a viable approach to improve the energy effectiveness and decrease the latency in communication systems. However, existing approaches face drawbacks such as high computational complexity, limited scalability, and difficulties in handling multiagent coordination, especially in large-scale satellite–terrestrial networks. This research presents a hybrid optimization scheme that maximizes energy efficiency and minimizes delays based on vital considerations like base station (BS) satellite placement, caching strategy, user association, and transmission power control. A three-phase approach is suggested to tackle these issues, relying on collaboration Q-learning with convolutional neural network (C-QCNN) and binary meerkat–hippopotamus optimization for power-control enhancement, cache architecture, and user association. It is observed that the proposed C-QCNN achieves the highest resource utilization of 99.23% when compared with existing techniques. This indicates that C-QCNN effectively allocates and utilizes resources to the enhancement of overall network performance. Significant improvements in energy efficiency are found in integrated satellite–terrestrial networks, thereby enabling more reliable and efficient nonorthogonal multiple access services. The significance of the proposed approach lies in its emphasis on advanced techniques in resource allocation and network optimization for next-generation communication systems.

非正交多址业务中地面网与卫星网的融合是提高通信系统能量利用率和降低通信系统时延的可行途径。然而,现有的方法面临着计算复杂度高、可扩展性有限以及处理多智能体协调困难等缺点,特别是在大规模卫星-地面网络中。本研究提出了一种混合优化方案,该方案基于基站(BS)卫星放置、缓存策略、用户关联和传输功率控制等重要考虑因素,最大限度地提高了能源效率并最小化了延迟。建议采用三阶段方法来解决这些问题,依靠卷积神经网络(C-QCNN)协同q学习和二元猫鼬-河马优化来增强功率控制、缓存架构和用户关联。与现有技术相比,所提出的C-QCNN的资源利用率最高,达到99.23%。这表明C-QCNN有效地分配和利用资源,以提高整体网络性能。卫星-地面综合网络的能源效率显著提高,从而实现更可靠和高效的非正交多址服务。该方法的意义在于强调下一代通信系统的资源分配和网络优化的先进技术。
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引用次数: 0
Differential Spatial Modulation Detection in Uplink Multiuser Massive MIMO Systems Using Optimized Shuffle Attention Convolutional Neural Network With MobileNet V1 基于MobileNet V1优化Shuffle注意卷积神经网络的上行多用户大规模MIMO系统差分空间调制检测
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70373
Poornima R., Sampoornam K.P., Allwyn Clarence A., Madhumathi R.

A significant interference challenge is presented by the upcoming sixth generation (6G) and beyond of wireless communication due to the increasing presence of ultra-scale intelligent factors, such as smart vehicles and mobile robots. Managing this interference can be difficult for detection algorithms in uplink massive multiple-input and multiple-output (MIMO) systems, particularly when dealing with higher-order quadrature amplitude modulation (QAM) signals. Differential spatial modulation (DSM) detection using deep learning (DL) has been a fundamental solution for effectively managing interference in 6G and enhancing the uplink performance of the MIMO system. In this paper, DSM detection in uplink multiuser massive MIMO systems using optimized shuffle attention convolutional neural network with MobileNet V1 (SMD-MIMO-SACNN-MNV1) is proposed. The proposed SACNN-MNV1 technique is used to detect the DSM in uplink multiuser massive multiple-input multiple-output (M-MIMO) systems. Artificial lizard search optimization algorithm (ALSOA) is utilized to enhance the SACNN-MNV1 detector that detects the DSM precisely. The proposed SMD-MIMO-SACNN-MNV1 method is executed in Python. The proposed method achieves 23.54%, 22.65%, and 23.18% higher spectral efficiency when compared with existing techniques respectively.

由于超规模智能因素(如智能车辆和移动机器人)的日益存在,即将到来的第六代(6G)及以上无线通信提出了重大的干扰挑战。在上行链路大规模多输入多输出(MIMO)系统中,管理这种干扰对于检测算法来说是困难的,特别是在处理高阶正交调幅(QAM)信号时。使用深度学习(DL)的差分空间调制(DSM)检测已经成为有效管理6G干扰和增强MIMO系统上行性能的基本解决方案。本文提出了基于MobileNet V1优化洗牌注意卷积神经网络(SMD-MIMO-SACNN-MNV1)的上行多用户大规模MIMO系统DSM检测方法。提出的SACNN-MNV1技术用于上行多用户海量多输入多输出(M-MIMO)系统的DSM检测。利用人工蜥蜴搜索优化算法(Artificial lizard search optimization algorithm, ALSOA)对SACNN-MNV1探测器进行了改进,使其能够准确地检测到DSM。提出的SMD-MIMO-SACNN-MNV1方法在Python中执行。与现有方法相比,该方法的光谱效率分别提高了23.54%、22.65%和23.18%。
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引用次数: 0
Enhanced Malicious Node Detection in Wireless Sensor Networks Using Multimodal Contrastive Domain Sharing Generative Adversarial Networks and Blockchain-Based Distributed Storage 基于多模态对比域共享生成对抗网络和区块链分布式存储的无线传感器网络中增强恶意节点检测
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70398
Mohanapriya R, B. Suganthi, S. Rajeswari, R. Dharani

Wireless sensor networks (WSNs) suffer from the risk of security breaches because of limited node resources, dynamic topologies, and lack of security mechanisms. These limitations affect the integrity of the data and the reliability of the network. Therefore, this paper proposes an enhanced malicious node detection in wireless sensor networks using multimodal contrastive domain sharing generative adversarial networks and blockchain based distributed storage (MND-MCDSGAN-WSN). Firstly, the sensor data gathered from the wireless sensor networks dataset are used. The collected data are cleaned with the Multi-Observation Fusion Kalman Filter (MOFKF) to remove the noise and to make the data more consistent. The Multimodal Contrastive Domain Sharing Generative Adversarial Networks (MCDSGAN) framework gets preprocessed data as input and classifies the malicious activities as Normal, Grayhole, Blackhole, TDMA, and Flooding. Meanwhile, the Adaptive Marine Predator Optimization Algorithm (AMPOA) selects the MCDSGAN model parameters to improve the stability and the generalization of the model. The Interplanetary File System (IPFS) is used to keep the verified node data safe, and a fair proof-of-reputation (FPoR) consensus mechanism is employed to ensure trustful nodes' participation and updates that are resistant to tampering in the blockchain. The combined architecture enhances detection reliability, thus helping trust management and giving a scalable security solution to WSNs that are underresourced. The proposed MND-MCDSGAN-WSN method achieves higher accuracy of 99.07% when evaluated with other existing methods.

无线传感器网络由于节点资源有限、拓扑结构动态、缺乏安全机制等原因,存在安全漏洞的风险。这些限制影响了数据的完整性和网络的可靠性。因此,本文提出了一种基于多模态对比域共享生成对抗网络和基于区块链的分布式存储(MND-MCDSGAN-WSN)的无线传感器网络中增强的恶意节点检测方法。首先,使用从无线传感器网络数据集收集的传感器数据。采用多观测融合卡尔曼滤波(MOFKF)对采集到的数据进行清洗,去除噪声,使数据更加一致。多模态对比域共享生成对抗网络(MCDSGAN)框架将预处理数据作为输入,并将恶意活动分类为Normal、Grayhole、Blackhole、TDMA和Flooding。同时,采用自适应海洋捕食者优化算法(AMPOA)选择MCDSGAN模型参数,提高模型的稳定性和泛化能力。星际文件系统(IPFS)用于保证已验证节点数据的安全,并采用公平的声誉证明(FPoR)共识机制来确保可信节点的参与和更新,从而抵抗区块链中的篡改。这种组合架构增强了检测可靠性,从而有助于信任管理,并为资源不足的wsn提供可扩展的安全解决方案。与现有方法比较,MND-MCDSGAN-WSN方法的准确率达到99.07%。
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引用次数: 0
Development of Lightweight Image Encryption Algorithm for Ensuring Confidentiality and Privacy in Internet of Things Devices 面向物联网设备保密性和隐私性的轻量级图像加密算法的开发
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70389
B. Maheswari, S. Ambika, T. P. Dayana Peter, R. Siva Subramanian

Lightweight image encryption for the Internet of Things (IoT) enables secure image data transmission between connected devices while accommodating the limited processing memory and power of IoT systems. A general drawback of lightweight image encryption is that it often sacrifices security strength for efficiency, making it more vulnerable to attacks compared to more robust encryption methods. In this manuscript, the development of a lightweight image encryption algorithm for ensuring confidentiality and privacy in Internet of Things devices (DLWIE-ECP-IoTD) is proposed. Firstly, the input image is gathered from the Caltech-101 dataset. Then, a chaotic pattern generation using a one-dimensional discrete chaos mapping system is applied to generate unpredictable patterns to secure image encryption, followed by a chaotic model of information encryption used for securing the image through unpredictable transformations. Then, a Uniform Physics Informed Neural Network (UPINN) is adapted to produce pseudo-random encryption keys from chaotic parameters, providing an efficient and secure key-generation mechanism. Finally, Fully Dynamic Advanced Encryption Standard (FDAES) is utilized for lightweight encryption of image data. The proposed DLWIE-ECP-IoTD approach is implemented in Python. The proposed DLWIE-ECP-IoTD approach achieves 1.120 s of computational time and 0.03% mean squared error (MSE) with existing methods, such as a lightweight multichaos-based image encryption scheme for IoT networks (LWMC-IES-IoTN), a lightweight image encryption scheme for IoT environments and machine learning-driven robust S-box selection (LWIE-IoTE-ML-DRSBS), and a lightweight image encryption scheme for the safe Internet of Things using a novel chaotic technique (LWIE-NCT-SIoT).

物联网(IoT)的轻量级图像加密能够在连接设备之间安全传输图像数据,同时适应物联网系统有限的处理内存和功率。轻量级图像加密的一个普遍缺点是,它经常为了效率而牺牲安全强度,与更健壮的加密方法相比,它更容易受到攻击。在本文中,提出了一种轻量级图像加密算法的开发,以确保物联网设备的机密性和隐私性(dlwie - epc - iotd)。首先,从Caltech-101数据集中收集输入图像。然后,使用一维离散混沌映射系统生成混沌模式以生成不可预测的模式以保护图像加密,然后使用混沌信息加密模型通过不可预测的转换来保护图像。然后,采用统一物理信息神经网络(UPINN)从混沌参数生成伪随机加密密钥,提供了一种高效、安全的密钥生成机制。最后,采用全动态高级加密标准(FDAES)对图像数据进行轻量加密。提出的dlwie - epc - iotd方法是在Python中实现的。与现有方法相比,所提出的dlwie - epc - iotd方法的计算时间为1.120秒,均方误差(MSE)为0.03%。现有方法包括:用于物联网网络的基于多混沌的轻量级图像加密方案(lwmc - ie - iotn),用于物联网环境和机器学习驱动的鲁强s盒选择的轻量级图像加密方案(LWIE-IoTE-ML-DRSBS),以及使用新型混沌技术的安全物联网的轻量级图像加密方案(LWIE-NCT-SIoT)。
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引用次数: 0
An Adaptive and Resilient Trust Management Model for Social IoT: A Reinforcement Approach 社会物联网的适应性和弹性信任管理模型:一种强化方法
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70391
Santhosh Kumari, Dilip Kumar S M

The Social Internet of Things (SIoT) allows smart devices to form social relationships for efficient service discovery and resource sharing. However, trust management is challenged by attacks like ballot stuffing and bad mouthing, which manipulate trust scores through biased recommendations. Existing approaches often evaluate recommenders based on their service provider role, overlooking their behavior as recommenders due to data sparsity. This paper presents a resilient trust management model using reinforcement learning. It combines multiple trust features—direct trust, service reliability, social ties, recommendation benevolence, and referred trust—to compute trust-based rewards. A filtering mechanism is also introduced to detect dishonest recommenders and reduce the impact of biased feedback. Experimental results demonstrate strong resilience against Bad mouthing attack (BMA), ballot stuffing attack (BSA), and Sybil attacks compared to state-of-the-art models, along with faster convergence on both SIoT/IoT network and Epinions datasets. These findings confirm the model's effectiveness in preserving trust and resisting attacks in SIoT environments.

社交物联网(Social Internet of Things, SIoT)允许智能设备之间形成社交关系,实现高效的服务发现和资源共享。然而,信任管理受到诸如选票填塞和诽谤等攻击的挑战,这些攻击通过有偏见的推荐来操纵信任分数。现有的方法通常基于推荐人的服务提供者角色来评估推荐人,由于数据稀疏性而忽略了推荐人作为推荐人的行为。本文提出了一种基于强化学习的弹性信任管理模型。它结合了直接信任、服务可靠性、社会联系、推荐仁慈和推荐信任等多种信任特征,计算基于信任的奖励。还引入了过滤机制来检测不诚实的推荐人,并减少偏见反馈的影响。实验结果表明,与最先进的模型相比,对Bad mouth攻击(BMA),选票填充攻击(BSA)和Sybil攻击具有较强的弹性,并且在SIoT/IoT网络和Epinions数据集上具有更快的收敛速度。这些发现证实了该模型在SIoT环境中保持信任和抵抗攻击方面的有效性。
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引用次数: 0
Tunable Terahertz MIMO/Self-Diplexing Dielectric Resonator Antenna 可调谐太赫兹MIMO/自双工介质谐振器天线
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70414
Ravikanti Vinay kumar, Pinku Ranjan, Gaurav Kaushal

A tunable terahertz (THz) multi-input, multi-output (MIMO) dielectric resonator (DR) antenna (DRA) with the self-diplexing ability is implemented. Two separated annular disc DRs with the top coated graphene material connected with two individual ports. And, these graphene coating can be connected to the separate DC power supplies to find the tunable response through individual port. The narrow-intensified resonance spectrum of annular disc DRs operating with the HEM40δ$$ HE{M}_{40delta } $$ mode can provide the highly sensitive tunable MIMO response. This can also help in offering the tunable self-diplexing capability to antenna. The usage of DRs can provide the antenna with the high gain and efficiency in the THz frequency range. The antenna operation is validated using the circuit theory approach. Moreover, antenna operation is validated to work with the four ports and radiators, which can provide the operation with four port MIMO or self-multiplexing capability. The usage of array of the four radiators enhances the directivity significantly by reducing the minor lobe and confining the radiated power as in pencil beam. The multiport antenna operation is validated by calculating envelop correlation coefficient and diversity gain which remain around 0.06 and 10 dB in the passband, respectively. These prove the proposed antenna suitable for working in the multiport systems.

实现了一种具有自双工能力的可调谐太赫兹(THz)多输入多输出(MIMO)介质谐振器(DR)天线。两个分离的环形圆盘DRs,顶部涂覆石墨烯材料,连接两个单独的端口。并且,这些石墨烯涂层可以连接到单独的直流电源,通过单独的端口找到可调谐的响应。在HE m40 δ $$ HE{M}_{40delta } $$模式下工作的环形圆盘dr窄增强共振谱可以提供高灵敏度的可调谐MIMO响应。这也有助于为天线提供可调谐的自双工能力。在太赫兹频率范围内,DRs的使用可以为天线提供高增益和高效率。利用电路理论方法对天线的工作进行了验证。此外,验证了天线操作与四个端口和散热器一起工作,可以提供四端口MIMO或自复用能力。四个辐射体阵列的使用通过减少小瓣和限制铅笔波束的辐射功率,显著提高了指向性。通过计算包络相关系数和分集增益分别在0.06和10 dB左右,验证了多端口天线的工作性能。结果表明,该天线适用于多端口系统。
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引用次数: 0
Network Traffic Prediction Using Integrated Deep Graph Neural Network Based on Big Data 基于大数据的集成深度图神经网络网络流量预测
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70390
Gangadhar Yalaga, S. Lokesh

Big data are difficult to process because of its volume and frequent updates. Big data are used to predict network traffic, which allows for further analysis at the application level. Network traffic prediction is essential for effective network planning and management. Deep learning (DL) has emerged as an effective way of capturing complex spatiotemporal relationships, with graph neural network (GNN) models being especially popular in this area. Nonetheless, conventional GNN techniques have inefficiencies in long-term forecasting in network traffic prediction, resulting in suboptimal predictive performance. To overcome the difficulties in forecasting network traffic, an integrated deep graph neural network (DeepGNN) model is presented in this work. First, create an integrated learning module that takes advantage of spatial correlation. Furthermore, sequence convolutional neural networks (sequence convolutional neural network [CNN]) are used for nonlinear dependencies, whereas attention mechanism incorporation is designed for heterogeneous features. In this study, integrated DeepGNN is evaluated on two network traffic datasets, Milan and Trentino. Three services, SMS, call, and internet, are also included for evaluation services in the first dataset and cumulative services in the second. Integrated DeepGNN is compared with the various existing models considering mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The proposed technique achieves a 4.923 MAE rate, which is lower than other techniques. The performance of the proposed technique is analyzed and compared with some related techniques to describe the superiority of the proposed model.

由于数据量大且更新频繁,大数据很难处理。大数据用于预测网络流量,从而允许在应用程序级别进行进一步分析。网络流量预测是有效的网络规划和管理的基础。深度学习(DL)已成为捕获复杂时空关系的有效方法,其中图神经网络(GNN)模型在该领域尤为流行。然而,在网络流量预测中,传统的GNN技术在长期预测方面效率较低,导致预测性能不理想。为了克服网络流量预测的困难,本文提出了一种集成深度图神经网络(DeepGNN)模型。首先,创建一个利用空间相关性的集成学习模块。此外,序列卷积神经网络(sequence convolutional neural network [CNN])用于处理非线性依赖关系,而注意力机制的整合则是针对异构特征设计的。在本研究中,集成DeepGNN在米兰和特伦蒂诺两个网络流量数据集上进行了评估。短信、电话和互联网这三种服务也被包括在第一个数据集中用于评估服务,第二个数据集中用于累积服务。考虑均方误差(MSE)、均方根误差(RMSE)和平均绝对误差(MAE),将集成DeepGNN与现有的各种模型进行比较。该技术的MAE率为4.923,低于其他技术。通过对该方法的性能分析,并与一些相关技术进行了比较,说明了该模型的优越性。
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引用次数: 0
An Energy-Efficient and Delay-Sensitive Routing for Mobile Wireless Sensor Networks Using an Optimized Deep-Learning Network 基于优化深度学习网络的移动无线传感器网络节能和延迟敏感路由
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1002/dac.70374
R. Shilpa, D. J. Chaithanya, M. N. Geetha, S. Rajini, C. Lokesh

Traditional Mobile Wireless Sensor Networks (MWSNs) often use mobile sinks to mitigate issues like energy holes. However, mobile sinks introduce new challenges, such as significant delays and buffer overflows due to fixed trajectories and variable moving speeds. These issues are particularly problematic for delay-sensitive applications, as most existing researches either focus on delay-tolerant scenarios or rely on energy-intensive greedy data collection methods. This research proposes an improved deep learning model with STGRN for delay-sensitive, energy-efficient routing and mobile sink prediction in MWSNs. STGRN-HRFO addresses mobility challenges in MWSNs through optimized routing, energy-efficient transmission, adaptive resource allocation, predictive mobility models, dynamic topology adjustments, and machine learning, enhancing stability, reducing energy consumption, and improving performance. Based on the projections from STGRN, an effective cluster-based routing system is implemented. A Hybrid Red-billed Frilled lizard magpie Optimizer (HRFO) is introduced to optimize the selection of cluster heads and improve routing efficiency. Significant gains are made using the STGRN-HRFO framework, which reduces the end-to-end path's hop count to 0.2 s, minimizes network energy usage to 3.6 J, and boosts throughput to 90%. Additionally, the energy consumed per packet is minimized to 2.2 mJ. Comparative analysis demonstrates that the STGRN-HRFO protocol effectively enhances network performance, ensuring low latency, high packet delivery ratios, and efficient energy use, particularly in real-world scenarios with complex optimization needs.

传统的移动无线传感器网络(mwsn)通常使用移动接收器来缓解能量空洞等问题。然而,移动汇带来了新的挑战,例如由于固定的轨迹和可变的移动速度而导致的显著延迟和缓冲区溢出。这些问题对于延迟敏感的应用来说尤其严重,因为大多数现有的研究要么集中在延迟容忍的场景上,要么依赖于能量密集的贪婪数据收集方法。本研究提出了一种改进的STGRN深度学习模型,用于MWSNs中延迟敏感、节能路由和移动sink预测。STGRN-HRFO通过优化路由、节能传输、自适应资源分配、预测移动模型、动态拓扑调整和机器学习等方法解决了mwsn的移动性挑战,增强了稳定性,降低了能耗,提高了性能。基于STGRN的投影,实现了一种有效的基于集群的路由系统。为了优化簇头选择,提高路由效率,提出了一种混合红嘴壁虎喜鹊优化器(HRFO)。使用STGRN-HRFO框架可以获得显著的收益,它将端到端路径的跳数减少到0.2 s,将网络能耗减少到3.6 J,并将吞吐量提高到90%。此外,每包消耗的能量被最小化到2.2兆焦耳。对比分析表明,STGRN-HRFO协议有效地提高了网络性能,确保了低延迟、高分组分发率和高效的能源利用,特别是在具有复杂优化需求的现实场景中。
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引用次数: 0
A Wideband and High Gain Cross-Slot Antenna Using Partially Reflecting Surface 部分反射面交叉槽宽带高增益天线
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-14 DOI: 10.1002/dac.70410
Venkataswamy Suryapaga, Vikas V. Khairnar
<div> <p>This paper presents a wideband, high gain Fabry-Perot cavity antenna operating at 3.5 GHz. The antenna utilizes a cross-slot as the main radiating element, along with an artificial magnetic conductor (AMC) layer and a partially reflecting surface (PRS) layer. The integration of a <span></span><math> <semantics> <mrow> <mn>9</mn> <mo>×</mo> <mn>9</mn> </mrow> <annotation>$$ 9times 9 $$</annotation> </semantics></math> AMC layer beneath the cross-slot antenna facilitates high gain and unidirectional radiation characteristics. Additionally, a <span></span><math> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> <annotation>$$ 4times 4 $$</annotation> </semantics></math> PRS layer is positioned in front of the antenna to further enhance both bandwidth and gain. The proposed antenna design achieves a <span></span><math> <semantics> <mrow> <mo>−</mo> </mrow> <annotation>$$ - $$</annotation> </semantics></math>10 dB impedance bandwidth ranging from 3.02 to 3.89 GHz (25.43%) with a peak gain of 9.56 dBi. Overall size of the antenna is <span></span><math> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>81</mn> <msub> <mrow> <mi>λ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>×</mo> <mn>0</mn> <mo>.</mo> <mn>81</mn> <msub> <mrow> <mi>λ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>×</mo> <mn>0</mn> <mo>.</mo> <mn>55</mn> <msub> <mrow> <mi>λ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> <annotation>$$ 0.81{lambda}_0times 0.81{lambda}_0times 0.55{lambda}_0 $$</annotation> </semantics></math>, where <span></span><math> <semantics> <mrow> <msub> <mrow> <mi>λ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow>
本文提出了一种工作频率为3.5 GHz的宽带高增益法布里-珀罗腔天线。该天线采用交叉槽作为主要辐射元件,以及人工磁导体(AMC)层和部分反射表面(PRS)层。交叉槽天线下方集成了9 × 9 $$ 9times 9 $$ AMC层,实现了高增益和单向辐射特性。此外,4 × 4 $$ 4times 4 $$ PRS层位于天线前面,以进一步提高带宽和增益。所提出的天线设计实现了−$$ - $$ 10 dB阻抗带宽范围为3.02 ~ 3.89 GHz (25.43%) with a peak gain of 9.56 dBi. Overall size of the antenna is 0 . 81 λ 0 × 0 . 81 λ 0 × 0 . 55 λ 0 $$ 0.81{lambda}_0times 0.81{lambda}_0times 0.55{lambda}_0 $$ , where λ 0 $$ {lambda}_0 $$ represents free space wavelength at an operating frequency of 3.5 GHz. The simulated and measured results are found to be in good agreement.
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International Journal of Communication Systems
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