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KLeFHNet: Kronecker LeNet Forward Harmonic Net for Crop Recommendation With Routing in Wireless Sensor Networks for Agriculture Kronecker LeNet前向谐波网在农业无线传感器网络中的作物推荐与路由
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-01 DOI: 10.1002/dac.70357
Y. Lavanya, Dr A. Maheswara Rao

A wireless sensor network (WSN) is deployed to monitor agricultural environments, helping to overcome issues like suboptimal crop selection that can result in lower yields and diminished quality. To enhance crop recommendation, this work proposes the Kronecker LeNet Forward Harmonic Net (KLeFHNet). The workflow begins with WSN simulation and energy prediction using a DRNN, after which clusters are formed using the Dung Namib Optimizer (DNO) and routing is handled by the Fractional DNO (FDNO). The combination of data normalization, Wave–Hedges metric-based feature selection, and oversampling for data augmentation boosts the performance of the model. The integration of the Deep Kronecker Network, LeNet, and Forward Harmonic Network within KLeFHNet boosts energy efficiency and prolongs network longevity. Experimental results show that FDNO achieves an energy consumption of 0.353 J, a network lifetime of 74.271, and a trust of 86.266, whereas KLeFHNet attains a TPR of 0.900, TNR of 0.895, and FPR of 0.105, supporting sustainable, data-driven agriculture.

部署无线传感器网络(WSN)来监测农业环境,帮助克服可能导致产量降低和质量下降的作物选择不理想等问题。为了加强作物推荐,本工作提出了Kronecker LeNet前向谐波网(KLeFHNet)。工作流程从使用DRNN的WSN模拟和能量预测开始,之后使用Dung Namib Optimizer (DNO)形成集群,路由由分数DNO (FDNO)处理。数据归一化、基于波对冲度量的特征选择和数据增强的过采样相结合,提高了模型的性能。在KLeFHNet中集成深度克罗内克网络、LeNet和正向谐波网络,提高了能源效率,延长了网络寿命。实验结果表明,FDNO的能耗为0.353 J,网络寿命为74.271,信任度为86.266,而KLeFHNet的TPR为0.900,TNR为0.895,FPR为0.105,支持可持续的数据驱动农业。
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引用次数: 0
False Data Detection in Wireless Body Area Network Using BiLSTM 基于BiLSTM的无线体域网络假数据检测
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-30 DOI: 10.1002/dac.70384
K. R. Shibu, Annie Jose

The recent technologies in IoT-based smart healthcare services are gaining attention because of their quality of service and reliability. These remote health monitoring systems offer enhanced quality of comfort to individuals and also meet emergency situation management requirements. Automatic data recording, processing, and communication with a third party like a doctor, caretaker, or hospital wirelessly enables the system to supersede conventional healthcare techniques. This paper focuses on the major challenging issues faced in the implementation of wireless body area networks (WBANs) in a dynamic environment, and the performance evaluation of the currently used systems. As the data collected by the sensor nodes are highly critical, a machine learning algorithm, integrating a rule-based screening and a bidirectional LSTM, is proposed to detect data manipulation by intruders. The performance of the network is then evaluated with the help of various parameters. The study results provide better insight into system performance and optimization of system parameters to achieve better reliability and throughput in the presence of false data.

近年来,基于物联网的智能医疗服务技术因其服务质量和可靠性而备受关注。这些远程健康监测系统提高了个人的舒适度,也满足了紧急情况管理的要求。自动数据记录、处理以及与第三方(如医生、看护或医院)的无线通信使该系统能够取代传统的医疗保健技术。本文重点讨论了在动态环境下实现无线体域网络(wban)所面临的主要挑战,以及目前使用的系统的性能评估。由于传感器节点收集的数据非常关键,因此提出了一种基于规则筛选和双向LSTM相结合的机器学习算法来检测入侵者对数据的操纵。然后在各种参数的帮助下评估网络的性能。研究结果可以更好地了解系统性能和优化系统参数,从而在存在虚假数据的情况下实现更好的可靠性和吞吐量。
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引用次数: 0
Adaptive Multilayer Contrastive Graph Neural Networks for Channel Estimation in Reconfigurable Intelligent Surface-Aided MIMO Systems 用于可重构智能表面辅助MIMO系统信道估计的自适应多层对比图神经网络
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-30 DOI: 10.1002/dac.70365
L Kebila Anns Subi, P. V. Deepa, S Felix Stephen

Channel state information (CSI) is necessary for wireless systems supported by reconfigurable intelligent surfaces (RIS) to regulate wireless channels and maximize bandwidth as well as energy effectiveness. In this paper, an Adaptive Multilayer Contrastive Graph Neural Networks for Channel Estimation in Reconfigurable Intelligent Surface-Aided MIMO Schemes (RIS-MIMO-CE-MCGNN) is proposed. Using reconfigurable intelligent surfaces, multiuser millimeter wave big MIMO systems with lower overhead may estimate frequency flat and frequency-selective cascaded channels. Then uses a double-structured sparsity property of the angular cascaded channel matrices and the common sparsity property among the different subcarriers since distinct angle cascaded channels that are visible to various users have completely shared nonzero rows together with user-specific column supports. Support Detection using Adaptive Multilayer Contrastive Graph Neural Networks (AMCGNN). Then, the Channel estimation is performed using the Wader Hunt Optimization Algorithm (WHOA) technique. Finally, the system seeks to minimize the quantity of pilot overhead needed for precise channel estimate. The proposed technique attains 6.18%, 5.58% and 7.29% higher SNR and 7.88%, 5.88%, and 7.19% higher PSNR comparing with the existing methods: Channel estimation for reconfigurable intelligent surface-dependent full-duplex MIMO with hardware impairments (CE-RIS-MIMO-HI), RIS-supported Multiuser MIMO Systems Exploiting Extreme Learning Machine (RIS-MU-MIMO-EELM), and RIS-assisted mmWave-MIMO channel estimation utilizing DL and compressive sensing (RIS-MIMO-CE-CS), respectively.

信道状态信息(CSI)对于可重构智能表面(RIS)支持的无线系统来说是必要的,以调节无线信道并最大化带宽和能源效率。提出了一种用于可重构智能表面辅助MIMO (RIS-MIMO-CE-MCGNN)信道估计的自适应多层对比图神经网络。使用可重构的智能表面,多用户毫米波大MIMO系统可以估计频率平坦和频率选择性级联信道。然后利用角度级联信道矩阵的双结构稀疏性和不同子载波之间的共同稀疏性,因为不同的角度级联信道对各种用户可见,它们具有完全共享的非零行以及用户特定的列支持。自适应多层对比图神经网络(AMCGNN)支持检测。然后,使用Wader Hunt优化算法(wow)技术进行信道估计。最后,该系统寻求最小化精确信道估计所需的导频开销。与现有方法相比,该技术的信噪比分别提高了6.18%、5.58%和7.29%,PSNR分别提高了7.88%、5.88%和7.19%。现有方法分别是:可重构智能表面相关全双工MIMO硬件损伤信道估计(CE-RIS-MIMO-HI),支持ris的利用极限学习机器的多用户MIMO系统(RIS-MU-MIMO-EELM),以及利用DL和压缩感知的ris辅助毫米波MIMO信道估计(RIS-MIMO-CE-CS)。
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引用次数: 0
Node Localization in 3D WSN Using Optimized Deep Learning Mechanism 基于优化深度学习机制的三维WSN节点定位
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-30 DOI: 10.1002/dac.70377
Akash Raghuvanshi, Dr. Awadhesh Kumar, Nilesh Chandra

Many mobile and sensor nodes comprised wireless sensor networks (WSN). Yet, it is quite challenging to locate these sensor and mobile nodes. Because of the time-varying movements, analysis of the current positions of sensor nodes in WSN is quite challenging. Because of locating all known sources in unknown nodes, the typical localization approaches are used to find the position of these nodes, producing a lot of inaccuracy when forecasting the distance between the source and unknown nodes. Also, it is very expensive to use Global Positioning System (GPS) technology for node detection. Although numerous localization procedures for WSNs in a three-dimensional topology have been proposed, it is still important to create and refine new localization algorithms to further increase the accuracy of the node positioning method. In this research work, an advanced heuristic algorithm and a deep learning technique are developed for localizing the unknown nodes in a three-dimensional wireless sensor network (3D-WSN). Initially, the distance between the unknown node as well as the anchor node is evaluated using efficient hybrid deep learning techniques named bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). Hybrid position of mine blast and chameleon swarm (HP-MBCS) is developed for tuning the parameters in deep learning techniques. An objective function of minimizing the average localization error (ALE) on node localization is obtained by optimally selecting the position of unknown nodes with the support of computed distance from the developed Bi-LSTM-GRU technique. The experimental simulation is carried out between the proposed and traditional models to show that the proposed model is efficient in minimizing localization error. The resultant outcome shows that the MEP value of the proposed HP-MBCS-Bi-LSTM-GRU model is 24.191, which is better than the other existing algorithms like EHO, EOO, MBO, and CSO, respectively. Thus, it was confirmed that the proposed Bi-LSTM-GRU not only improves the precision and effectiveness of node localization but also enhances the overall energy efficiency.

许多移动和传感器节点组成了无线传感器网络(WSN)。然而,定位这些传感器和移动节点是相当具有挑战性的。由于传感器节点的时变运动,分析传感器网络中传感器节点的当前位置具有很大的挑战性。由于将所有已知源定位在未知节点上,传统的定位方法用于寻找这些节点的位置,在预测源与未知节点之间的距离时会产生很大的不准确性。此外,使用全球定位系统(GPS)技术进行节点检测是非常昂贵的。虽然在三维拓扑中已经提出了许多wsn的定位方法,但为了进一步提高节点定位方法的精度,仍然需要创建和完善新的定位算法。在本研究中,提出了一种先进的启发式算法和深度学习技术来定位三维无线传感器网络(3D-WSN)中的未知节点。最初,未知节点和锚节点之间的距离使用高效的混合深度学习技术进行评估,称为双向长短期记忆(Bi-LSTM)和门控循环单元(GRU)。提出了一种矿井爆破与变色龙群混合位置(HP-MBCS)的深度学习参数整定方法。利用所开发的Bi-LSTM-GRU技术,在计算距离的支持下,通过最优选择未知节点的位置,得到节点定位平均定位误差(ALE)最小的目标函数。通过与传统模型的对比实验,验证了该模型在最小化定位误差方面的有效性。结果表明,HP-MBCS-Bi-LSTM-GRU模型的MEP值为24.191,分别优于现有的EHO、EOO、MBO和CSO算法。验证了所提出的Bi-LSTM-GRU不仅提高了节点定位的精度和有效性,而且提高了整体能源效率。
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引用次数: 0
A Novel EKF-PSO Approach for Enhanced Object Tracking and Routing in Wireless Sensor Networks 一种增强无线传感器网络中目标跟踪和路由的EKF-PSO方法
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-29 DOI: 10.1002/dac.70369
T. Vairam, S. Sailesh Kumar, A. S. Vishal

WSNs are essential in industrial and environmental areas, where they can be used to monitor things and track objects in real time. In WSNs that achieve accurate target localization, it is a primary problem in object tracking, primarily in dynamic environments. Additionally, tracking of objects is complicated due to the motion of objects being nonlinear in real-life conditions, which may negatively affect the performance of the tracking process. To address it, the current research will introduce a new hybrid tracking algorithm, which is a combination of Extended Kalman Filters (EKF) and Particle Swarm Optimization (PSO) to measure target state and adjust covariance parameters in WSNs. In this case, the adaptive parameter tuning will be carried out by PSO, whereas EKF modulates the real-time alterations dynamically, which will increase the monitoring efficiency under unpredictable circumstances. This combination enhances precision in tracking whereby filter parameters are constantly diminished with respect to the changing motion of the target. EKF has been applied to predict the state of a target in real-life situations because of its success in estimating the state of a free nonlinear system with Gaussian noise. A static filter configuration can, however, be inadequate in dynamic environments. PSO guarantees reliable tracking of objects regardless of the unpredictable situations by addressing this limitation by modulating EKF parameters in real time. In order to evaluate the performance of the proposed EKF-PSO approach, the study is applied in MATLAB using two network topologies: Random Topology (RT) and Hybrid Topology (HT). The various parameters that are employed to quantify the workability of the suggested approach include power usage, delay, and tracking error. The results of simulations for 100 repeated runs indicate that root mean square error decreases by 28% and energy savings increase by 15% compared with conventional EKF. The algorithm is dynamic in conditions of nonlinear motion and motion noise. From the results, it is shown that HT-EKF-PSO significantly outperforms RT-EKF-PSO with all metrics.

无线传感器网络在工业和环境领域是必不可少的,它们可以用来实时监控和跟踪物体。在实现精确目标定位的无线传感器网络中,目标跟踪是一个主要问题,尤其是在动态环境中。此外,在现实生活中,由于物体的运动是非线性的,物体的跟踪是复杂的,这可能会对跟踪过程的性能产生负面影响。为了解决这一问题,本研究将引入一种新的混合跟踪算法,该算法将扩展卡尔曼滤波(EKF)和粒子群优化(PSO)相结合,用于WSNs中目标状态的测量和协方差参数的调整。在这种情况下,粒子群算法对参数进行自适应调整,而EKF算法对实时变化进行动态调节,提高了不可预测情况下的监测效率。这种组合提高了跟踪的精度,从而使滤波器参数随着目标运动的变化而不断减小。由于EKF在估计具有高斯噪声的自由非线性系统的状态方面取得了成功,因此已被应用于实际情况中目标状态的预测。但是,静态过滤器配置可能不适用于动态环境。PSO通过实时调制EKF参数来解决这一限制,从而保证了在不可预测的情况下对目标的可靠跟踪。为了评估所提出的EKF-PSO方法的性能,该研究在MATLAB中使用了两种网络拓扑:随机拓扑(RT)和混合拓扑(HT)。用于量化所建议方法的可行性的各种参数包括功率使用、延迟和跟踪误差。100次重复运行的仿真结果表明,与传统EKF相比,该方法的均方根误差降低了28%,节能效果提高了15%。该算法在非线性运动和运动噪声条件下是动态的。从结果来看,HT-EKF-PSO在所有指标上都明显优于RT-EKF-PSO。
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引用次数: 0
Gain Enhancement Mechanisms in Circularly Polarized FSS-Embedded Meander Monopole Antenna 圆极化fss内嵌弯曲单极天线的增益增强机制
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-24 DOI: 10.1002/dac.70375
Biplab Bag, Kalyan Mondal, Snehasish Saha, Susmita Bala, Partha Pratim Sarkar

This paper describes the design and implementation of a dual-layer frequency selective surface (FSS) based dual-band high-gain circularly polarized (CP) meander-shaped monopole antenna for the applications of Wi-Fi and C-band. To achieve the final configuration, the design process involves several steps: designing the meander-shaped antenna, modeling the FSS, circuit analysis, and practical realization. The antenna prototype (electrical dimension: 0.333 λ₀ × 0.266 λ₀, λ₀ at 2 GHz and physical dimension of 50 × 40 mm2) comprises a meander-shaped strip and a 3 × 3 matrix dual layer periodic FSS embedded under the antenna structure without disturbing the impedance bandwidth. The low-cost FR4 dielectric substrate is used to design both the antenna and FSS layer. Initially, only the antenna part was designed, which yielded −10 dB impedance bandwidths (IBWs) and 3-dB axial ratio bandwidths (ARBWs) of 1160 and 600 MHz in the lower band, and 560 and 700 MHz in the upper band, respectively. Without an FSS structure, the peak gains of the antenna are 3.6 dBi (lower band) and 3.8 dBi (upper band). The proposed FSS-based antenna is fabricated and measured in the microwave test bench. The measured results show 3-dB ARBWs of 350 MHz (2.35–2.7 GHz) and 600 MHz (3.9–4.5 GHz) with LHCP waves. The measured peak gains are 8 dBi in the lower band and 8.5 dBi in the upper band. With small tolerance, the measured results agree with simulations.

本文介绍了一种用于Wi-Fi和c波段的基于双层选频表面(FSS)的双频高增益圆极化(CP)曲线形单极天线的设计与实现。为了实现最终的配置,设计过程包括设计曲线形天线、FSS建模、电路分析和实际实现几个步骤。天线原型(电气尺寸:0.333 λ 0 × 0.266 λ 0, λ 0为2 GHz,物理尺寸为50 × 40 mm2)包括一条曲线形带和嵌入在天线结构下的3 × 3矩阵双层周期FSS,且不干扰阻抗带宽。采用低成本的FR4介质衬底设计天线和FSS层。最初只设计了天线部分,下频段为1160 MHz,上频段为560 MHz,下频段为600 MHz,下频段为−10 dB阻抗带宽(ibw)和3db轴比带宽(arbw)。在没有FSS结构的情况下,天线的峰值增益分别为3.6 dBi(下频段)和3.8 dBi(上频段)。该天线在微波试验台进行了制作和测试。测量结果显示,在LHCP波下,3db ARBWs的频率分别为350 MHz (2.35-2.7 GHz)和600 MHz (3.9-4.5 GHz)。测得的峰值增益在下频段为8dbi,在上频段为8.5 dBi。在误差较小的情况下,测量结果与模拟结果吻合。
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引用次数: 0
An Improved DV-Hop Localization Algorithm in Anisotropic-Based Wireless Sensor Network Using Domination Method and Q-Learning-Based Crayfish Optimization 基于控制法和基于q -学习的小龙虾优化的各向异性无线传感器网络DV-Hop定位算法
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-24 DOI: 10.1002/dac.70370
S Afizudeen, R Pavithra

Sensor localization is a crucial factor in ensuring reliable operation in wireless sensor network (WSN). The existing research on localization focuses more under isotropic conditions, assuming homogeneous environments with uniformly distributed nodes. In contrast, real-world deployments frequently exhibit Anisotropic WSN (AWSN) characteristics, including irregular topologies, obstacles, and non-uniform connectivity, which significantly challenge the localization process. Among many localization methods, the DV-Hop algorithm is widely used approach for its cost-effectiveness and easy implementations. However, the DV-Hop algorithm performs poorly when there is an obstacle encountered; the average hop distance might deteriorate, and it is hard to locate the coordinates of the location of unknown nodes. In order to achieve better localization accuracy with cost-effective, this study proposes Domination-based Anchor Placement for AWSNs (DAPA) to obtain minimum anchor requirement and optimal anchor placement. Further, a Q-learning-based Crayfish Optimization Algorithm (QCOA) is proposed to enhance the DV-Hop algorithm localization accuracy. The proposed QCOA was compared using 10 benchmark functions with some similar existing optimization algorithms. The proposed DAPA approach was compared with random anchor deployment method using three variants of DV-Hop algorithms. DAPA outperform the random anchor deployment in each three comparisons. Further, the proposed DAPA-based QCOA algorithm simulated in C-, S-, and O-shaped topologies based on localization error from varying the AWSN metrics. The proposed algorithm achieves high localization accuracy among the existing algorithms.

传感器定位是保证无线传感器网络可靠运行的关键因素。现有的定位研究多集中在各向同性条件下,假设节点均匀分布的均匀环境。相比之下,实际部署经常表现出各向异性WSN (AWSN)特征,包括不规则拓扑、障碍和非均匀连接,这对定位过程构成了重大挑战。在众多的定位方法中,DV-Hop算法以其性价比高、易于实现等优点被广泛采用。然而,当遇到障碍物时,DV-Hop算法的性能较差;平均跳距可能会变差,并且难以确定未知节点的位置坐标。为了在经济高效的前提下获得更好的定位精度,本研究提出了基于dominbased Anchor Placement的AWSNs (DAPA)定位方法,以获得最小的锚点需求和最优的锚点放置。在此基础上,提出了基于q学习的小龙虾优化算法(QCOA)来提高DV-Hop算法的定位精度。用10个基准函数与现有的一些类似优化算法进行了比较。采用DV-Hop算法的三种变体,将DAPA方法与随机锚点部署方法进行了比较。在每三个比较中,DAPA都优于随机锚部署。在此基础上,本文提出的基于dapa的QCOA算法分别在C型、S型和o型拓扑结构中进行了仿真。该算法在现有算法中具有较高的定位精度。
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引用次数: 0
Design and Study of Twin Band Quad Terminal Reflector Loaded Wearable Radiator With Reduced SAR and High Gain 低SAR高增益双波段四端反射器负载可穿戴辐射器的设计与研究
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-23 DOI: 10.1002/dac.70368
Dheeraj Nagar, Prashant Ranjan, Atanu Chowdhury

This research presents a wearable radiator with dual-band and quad-terminal that is compact and made on polyimide substrate. Dual metallic rings integrated with the feed line create the band notch in between 2.66 and 3.28 GHz and convert the wideband into dual band feature. By taking use of spatial and polarization diversity, the separation level among various terminals is more than 25 dB. A single-negative (SNG) metamaterial-based metasurface (MS) reflector-cum-absorber surface located just below the radiator reduces the specific absorption rate (SAR) for the 1- and 10-g tissue models by more than 90%, while also increasing the radiator gain to 5.65 and 4.55 dBi. ANSYS HFSS 2023 R2 was used for full-wave simulations, and a Keysight E5071C VNA in both flat and bending configurations was used for experimental validation. Using a three-layer human tissue phantom (skin, fat, and muscle) with 1- and 10-g averages, SAR assessment was conducted in accordance with IEEE/IEC 62209-1 and ICNIRP recommendations. Its performance in two frequency bands, 2.3–2.65 and 3.3–3.65 GHz, is confirmed by measurement findings. All these features make the radiator applicable for WBAN application.

本研究提出了一种紧凑的聚酰亚胺基板上的双频四端可穿戴散热器。与馈线集成的双金属环创建了2.66和3.28 GHz之间的频带缺口,并将宽带转换为双频功能。利用空间分集和极化分集,各终端间的分离电平大于25 dB。基于单负(SNG)超材料的超表面(MS)反射和吸收表面位于散热器正下方,可将1 g和10 g组织模型的比吸收率(SAR)降低90%以上,同时还可将散热器增益增加到5.65和4.55 dBi。采用ANSYS HFSS 2023 R2进行全波仿真,采用Keysight E5071C平面和弯曲两种构型的VNA进行实验验证。使用三层人体组织模型(皮肤、脂肪和肌肉),平均为1和10克,根据IEEE/IEC 62209-1和ICNIRP建议进行SAR评估。在2.3-2.65 GHz和3.3-3.65 GHz两个频段上的性能得到了测试结果的证实。这些特点使该散热器适用于无线宽带网络的应用。
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引用次数: 0
Optimization of Power and Latency of Medium Access Control Protocol of Wireless Sensor Network 无线传感器网络介质访问控制协议的功率和时延优化
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-22 DOI: 10.1002/dac.70353
Kuldeep Goswami, Lalit Kumar Awasthi, Harsh Kumar Verma

In wireless sensor networks (WSNs) used for continuous surveillance, the problem of monitoring critical data transmitted infrequently is an extreme challenge of energy usage and latency requirements. Current medium access control (MAC) protocols often have high energy consumption, primarily owing to idle listening, collision, and excessive data transmission, and as a result, are not suitable for such uses. This study proposes a novel protocol to optimize energy consumption and transmission delays in WSNs used to monitor infrequent critical data. This protocol is named OWuR-MAC, that is, “Optimized Wake-up Radio based Medium Access Control.” OWuR-MAC implements an event-driven wake-up strategy utilizing wake-up receivers so that devices can stay in the low-power sleep mode until data transmission is necessary. Sensor nodes use wake-up receivers, which allow them to remain at low-energy sleep times until there is relevant data transmission that can wake them up. However, OWuR-MAC dynamically modifies the wake-up receiver sensitivity and transmission timing based on the characteristics of networked activity and environmental conditions. The protocol was implemented and compared with Fully Asynchronous Wake-up Radio MAC (FAWR-MAC) and Opportunistic Wake-up Radio MAC (OPWUM) protocols of a similar category. The results indicate that OWuR-MAC achieves lower rates of energy consumption, lower latency, and higher packet delivery ratios than the other two protocols.

在用于连续监控的无线传感器网络(wsn)中,监控不频繁传输的关键数据的问题是对能量使用和延迟要求的极端挑战。当前的MAC (medium access control,介质访问控制)协议能耗高,主要是由于空闲侦听、冲突和数据传输过多等原因,不适合此类应用。该研究提出了一种新的协议来优化用于监测不频繁关键数据的wsn的能耗和传输延迟。该协议被命名为OWuR-MAC,即“基于优化唤醒无线电的媒体访问控制”。OWuR-MAC实现了一种事件驱动的唤醒策略,利用唤醒接收器,使设备可以保持在低功耗睡眠模式,直到需要传输数据。传感器节点使用唤醒接收器,这允许它们保持低能量睡眠时间,直到有相关的数据传输可以唤醒它们。然而,OWuR-MAC根据网络活动和环境条件的特点动态修改唤醒接收器的灵敏度和传输时间。实现了该协议,并与同类完全异步唤醒无线电MAC (FAWR-MAC)和机会唤醒无线电MAC (OPWUM)协议进行了比较。结果表明,与其他两种协议相比,OWuR-MAC协议具有更低的能耗、更低的时延和更高的分组分发率。
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引用次数: 0
An Energy-Efficient and Smart Traffic Management Framework With Optimization and Deep Learning for VANET 基于优化和深度学习的节能智能交通管理框架
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-22 DOI: 10.1002/dac.70358
R. Anto Pravin, R. S. Nancy Noella

Vehicular Ad Hoc Networks, in short VANETs, a category of mobile ad hoc networks, facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication for improved traffic management, safety, and autonomous driving. This work proposes a traffic flow prediction model that integrates metaheuristic optimization techniques such as Sand Cat Swarm Optimization (SCSO) and Raven Roosting Optimization (RRO) with the deep learning model Bi-directional Long Short Term Memory with Stacked Autoencoder (Bi-LSTM-SA) to improve prediction accuracy. To optimize the Bi-LSTM-SA network, a hybrid SCSO-RRO approach encodes neuron parameters like weights, biases, and activation functions. The SCSO explores the search space while RRO refines solutions by having ravens follow high-fitness sand cats. A fitness function evaluates performance, and the process iterates until convergence, after which the optimized network is validated on a separate dataset. The proposed model Combined SCSO-RRO-Bi-LSTM-SA (C-SC-RRO-Bi-LSTM-SA) is compared with existing algorithms such as Random Forest (RF), Artificial Neural Network (ANN), Bi-LSTM-SA, and evaluation parameters utilized to quantify the network's performance include accuracy, prediction, recall, F1-score, and cross-entropy loss.

车辆自组织网络(Vehicular Ad Hoc Networks,简称vanet)是移动自组织网络的一种,可促进车对车(V2V)和车对基础设施(V2I)通信,以改善交通管理、安全性和自动驾驶。本文提出了一种交通流量预测模型,该模型将沙猫群优化(SCSO)和乌鸦筑巢优化(RRO)等元启发式优化技术与深度学习模型双向长短期记忆与堆叠自编码器(Bi-LSTM-SA)相结合,以提高预测精度。为了优化Bi-LSTM-SA网络,混合SCSO-RRO方法编码神经元参数,如权重、偏差和激活函数。SCSO探索搜索空间,而RRO则通过让乌鸦跟随高适应性的沙猫来完善解决方案。适应度函数评估性能,过程迭代直到收敛,之后优化的网络在单独的数据集上进行验证。将该模型与随机森林(Random Forest, RF)、人工神经网络(Artificial Neural Network, ANN)、Bi-LSTM-SA等现有算法进行比较,并利用准确率、预测率、召回率、f1分数和交叉熵损失等评价参数量化网络的性能。
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引用次数: 0
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International Journal of Communication Systems
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