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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

Multimedia Tools and Applications 2025年第17期84卷 17951-17977页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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Enhancing aquaculture water quality forecasting using novel adaptive multi-channel spatial-temporal graph convolutional network

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International Journal of Agricultural and Biological engineering 2025年第1期18卷 279-291页

作者： Tianqi Xiang Xiangyun Guo Junjie Chi Juan Gao Luwei Zhang College of Computer Science Beijing Information Science&Technology UniversityBeijing 102206China College of Management Science and Engineering Beijing Information Science&Technology UniversityBeijing 102206China College of Engineering China Agricultural UniversityBeijing 100083China

In recent years,aquaculture has developed rapidly,especially in coastal and open ocean *** practice,water quality prediction is of critical ***,traditional water quality prediction models face limitations in handling complex spatiotemporal *** address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality *** aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the *** results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,*** indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.

关键词： water quality prediction aquaculture spatial-temporal graph convolutional network multi-channel adaptive graph construction

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Privacy-Preserving Knowledge Distillation in Latency-Critical Federated Task Offloading for Consumer IoT Networks

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IEEE Transactions on Consumer electronics 2024年第1期71卷 239-251页

作者： Chen, Yishan Zhang, Jianwei Wang, Zhiqiang Dai, Wenshuo Gao, Honghao Deng, Shuiguang Jiangxi University of Science and Technology College of Information Engineering China Shanghai University School of Computer Engineering and Science China Gachon University College of Future Industry Korea Republic of Zhejiang University College of Computer Science and Technology China

Along with the explosive demand for massive data computation, federated local-edge-cloud computing enables many IoT task offloading processes and has recently gained widespread attention in consumer-centric healthcare. However, existing approaches for latency-critical task offloading do not pay sufficient attention to patient privacy data and thus are far from being ready for large-scale practical deployment. In this paper, a novel local-edge-cloud based federated learning framework with one-way knowledge distillation for latency-critical and security-aware task offloading is proposed, while a task offloading scheme based on deep reinforcement learning is presented. Then, based on the consideration of privacy guarantees, computing resources and offloading decisions, an optimization problem is formulated to minimize the overall latency of all tasks for local medical devices. Additionally, a pseudo-task mechanism is adopted in the federated framework for the uploaded weights to aggregate the models on the edges and the cloud. Besides, a one-way knowledge distillation mechanism is proposed to transfer knowledge from a teacher model to student models, which compresses the network parameters and reduces the model latency. Finally, experiments based on Chest X-Ray Images of Pneumonia are conducted for corroborating the theoretical results. © 1975-2011 IEEE.

关键词： Computation offloading

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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An object detection approach with residual feature fusion and second-order term attention mechanism

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CAAI Transactions on Intelligence technology 2024年第2期9卷 411-424页

作者： Cuijin Li Zhong Qu Shengye Wang College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina College of Electronic Information Chongqing Institute of EngineeringChongqingChina

Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging *** the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects,the authors propose a network model with a second-order term attention mechanism and occlusion ***,the backbone network is built on *** a method is designed for the feature extraction network based on an item-wise attention mechanism,which uses the filtered weighted feature vector to replace the original residual fusion and adds a second-order term to reduce the information loss in the process of fusion and accelerate the convergence of the ***,an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense *** experimental results demonstrate that the authors’method achieved state-of-the-art performance without reducing the detection *** mAP@.5 of the method is 85.8%on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8%on the KITTI dataset.

关键词： artificial intelligence computer vision image processing machine learning neural network object recognition

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A Dual-Layer Attention Based CAPTCHA Recognition Approach with Guided Visual Attention

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computer Modeling in engineering & sciences 2025年第3期142卷 2841-2867页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Enhancing website security is crucial to combat malicious activities,and CAPTCHA(Completely Automated Public Turing tests to tell computers and Humans Apart)has become a key method to distinguish humans from *** text-based CAPTCHAs are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable *** this regard,we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual *** have specifically adapted the well-established image captioning task to address this *** approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance CAPTCHA *** extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and Captcha 0.3—shows the adaptability and efficacy of our *** approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for *** results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets,showcasing its robustness,reliability,and ability to handle varied challenges in CAPTCHA recognition.

关键词： Text-based CAPTCHA image recognition guided visual attention web security computer vision

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Adversarial attacks and defenses for digital communication signals identification

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Digital Communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150000China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators

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A novel thermally-stable single-phase phosphor Ca_(2)InTaO_(6):Dy^(3+)for NUV-excited white LEDs

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Journal of Rare Earths 2024年第10期42卷 1855-1864页

作者： Jian Hu Ruirui Cui Jun Zhang Peng Linghu Youzhen Shi Xiang Guo Key Laboratory of Micro-Nano-Electronics and Software Technology of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025China College of Computer and Information Engineering Guizhou University of CommerceGuiyang550014China

The work focuses on the utilization of the conventional solid-state sintering procedure to synthesize white phosphors Ca_(2)InTaO_(6):xDy^(3+)(0.02≤x≤0.12).Utilizing X-ray diffraction,the phase structure of samples was examined,and the crystal structure was refined using the Rietveld method.A scanning electron microscope was used to analyze the microstructure of ***-principles calculations confirm that the indirect bandgap of Ca_(2)InTaO_(6)is 3.786 eV,The luminous properties and energy transfer mechanism of Ca_(2)InTaO_(6):xDy^(3+)were studied using photoluminescence ***^(4)F_(9/2)→^(6)H_(13/2)transition of Dy^(3+)ions is responsible for the greatest emission peak,which was measured at 575 *** to research,the lifespan falls as the concentration of Dy^(3+)doping amount rises because of frequent interaction and ene rgy transfer between Dy^(3+)*** correlated color temperature of the WLEDs packaged with Ca_(2)InTaO_(6):0.08Dy^(3+)is 4677 K and CIE 1931 chromaticity coordinates are(0.3578,0.3831).Meantime,the phosphor also shows outstanding te mperature stability property,which maintains 83.8%of its initial emission intensity at 450 K(activation energy of 0.1467 eV).The W-LEDs retain their performance for 100 min when powered at 3.4 V voltage and 600 mA current,demonstrating the packed W-LEDs'sustaine d operation at high temperatures.

关键词： Ca_(2)InTaO_(6):Dy^(3+)phosphors Thermal stability Photoluminescence W-LEDs Thermal imaging Rare earths

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