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Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

computers, Materials & Continua 2023年第1期74卷 453-467页

作者： Mohamed Amine Ouamri Reem Alkanhel Cedric Gueguen Manal Abdullah Alohali Sherif S.M.Ghoneim University of Rennes 1/ADOPNET team at IRISA laboratory Rennes35000France Facultéde Technologie Laboratoire d’Informatique Médicale(LIMED)Universitéde BejaiaBejaia06000Algérie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electrical engineering department College of EngineeringTaif University P.O.Box 11099Taif21944Saudi Arabia

With the rapid development of emerging 5G and beyond(B5G),Unmanned Aerial Vehicles(UAVs)are increasingly important to improve the performance of dense cellular *** a conventional metric,coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous *** recent years,stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network *** this paper,an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been *** assumption was considered that all users are distributed according to Poisson Cluster Process(PCP)around base stations,in particular,Thomas Cluster Process(TCP).Using thismodel,the impact of beam alignment errors on the coverage probabilitywas ***,the ProbabilityDensity Function(PDF)of directional antenna gain between the user and its serving base station was ***,association probability with each tier was achieved.A tractable expression was derived for coverage probability in both Line-of-Sight(LoS)andNon-Line-of-Sight(NLoS)condition *** results demonstrated that at low UAVs altitude,beam alignment errors significantly degrade coverage ***,for a small cluster size,alignment errors do not necessarily affect the coverage performance.

关键词： Unmanned aerial vehicles coverage analysis stochastic geometry millimeter wave imperfect beam alignment

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Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection against Multipath Routing

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： Network intrusion

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Seastar: A Cache-Efficient and Load-Balanced Key-Value Store on Disaggregated Memory

Seastar: A Cache-Efficient and Load-Balanced Key-Value Store...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

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Smart Advertising Based on Customer Preferences and Manage the Supermarket 4

Smart Advertising Based on Customer Preferences and Manage t...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Eishan Dinuka, W.H.A. Wickramasinghe, A.Y.S.W. Weerasinghe, W.S.H. Karunaratne, K.P.G. Liyanapathirana, Chethana Rupasinghe, Lakmal Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

As a developing country, Sri Lanka needs to go along with cutting-edge technologies. In the beginning phase of this digital advertising, multiple advertisements were displayed on the users' feeds, including advertisements despite their preferences. This was a terrible user experience for the users. However, smart advertising based on customer preferences can manage the flow of advertisements on the feed as per the users' preferences. This same technique can be used in handling advertisements while shopping at supermarkets. These advertisements can be directed based on demographic characteristics like face and gender and previous customer transactions. Additionally, providing the nearest supermarket they can reach based on their current location. Queue management is the next most crucial facility that needs to be provided to a supermarket. However, the manual system of queue management is not effective. But with a modernized queue management system, overcrowded supermarkets can be managed effectively. This proposed system also considers providing a chatbot service to manage customer inquiries in a reliable strategy. In this system, we mainly used the Keras model called VGGFace for face detection, the Conventional Neural Network and Keras-based model for gender detection, the TensorFlow model called Single Shot MultiBox Detection MobileNet for queue and crowd detection, the Apriori algorithm base model for predicting the buying pattern, a Keras-based model for Artificial Intelligence chatbot and finally, google map Application Programming Interface for the nearest supermarket finding are models and technology. This system was developed to manage a supermarket properly. © 2022 IEEE.

关键词： Sales

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MMR-HAD: Multi-scale Mamba Reconstruction Network for Hyperspectral Anomaly Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Fu, Xiyou Zhang, Ting Cheng, Juan Jia, Sen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing Xi'an710119 China

Hyperspectral anomaly detection (HAD) aims to identify anomalous objects from hyperspectral images (HSIs) whose spectral features significantly deviate from their surroundings. Existing HAD methods still reconstruct some anomalies during the background reconstruction process, which can seriously affect the detection accuracy. Consequently, inspired by Mamba's ability to effectively model long sequences, we propose a Multi-scale Mamba Reconstruction Network for Hyperspectral Anomaly Detection (MMR-HAD) by enhancing the representation of the background and inhibit anomalies from being reconstructed. MMR-HAD first removes most of the anomalous pixels in hyperspectral images using the Random Mask (RM) strategy, which reduces the interference of anomalous pixels on the background reconstruction and makes the background features more prominent. To further filter out the small amount of residual anomalous pixels, we propose the Multi-scale Dilated Attention Background Enhancement (MDABE) mechanism, which enhances the background representation. Finally, we apply the Multi-scale Dynamic Feature Fusion (MDFF) strategy to reconstruct the background image, further extracting and strengthening the background information, thus obtaining a pure background image. MMR-HAD, which focuses on generating a pure background, has been experimentally validated on seven real hyperspectral datasets. The results demonstrate that it excels in background enhancement and anomaly suppression, significantly improving detection accuracy. Introducing Mamba offers a promising solution for HAD, with substantial potential for practical application. © 1980-2012 IEEE.

关键词： Image reconstruction

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SFedXL: Semi-Synchronous Federated Learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年第12期12卷 18692-18707页

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Sophia Antipolis06410 France Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a UAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by UAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different UAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed Semi-synchronous FL with Cross-Sharpness and Layer-Freezing (SFedXL), tailored for a UAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a trade-off of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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Temporal-Aware Multi-behavior Contrastive Recommendation 28th

Temporal-Aware Multi-behavior Contrastive Recommendation

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Xuan, Hongrui Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing China National Engineering Laboratory for Integrated Aero-Space-GroundOcean Big Data Application Technology Nanjing China

ISBN: (纸本)9783031306716

Modeling various types of users’ interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information). Meanwhile, the introduction of multi-behavior information is bound to generate additional noise signals, which further affects the accuracy of recommendation. Thus, to tackle the above challenges, two aspects need to be explored: (1) How to fuse interaction attributes to construct unified cross-behavior dependencies while distinguishing subtle differences between individuals’ behaviors. (2) How to mitigate the effects of noise to a certain extent while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users’ preference evolution, and alleviate the impact of multi-behavior noise. Extensive experiments and ablation tests on the three datasets indicate our TMCL outperforms various state-of-the-art methods and verify the effectiveness of our model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User profile

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Self-powered tactile sensor for real-time recognition of Morse code based on machine learning

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Nano Research 2025年第2期18卷 760-768页

作者： Shenxing Tan Yang Jiang Xujiang Chao Fei Liang Ripeng Li Tao Jiang Hai-Dong Yu Zhong Lin Wang Guangzhou Institute of Blue Energy Guangzhou 510555China Frontiers Science Center for Flexible Electronics Xi'an Institute of Flexible Electronics(IFE)and Xi'an Institute of Biomedical Materials&EngineeringNorthwestern Polytechnical UniversityXi'an 710072China Beijing Key Laboratory of Micro-Nano Energy and Sensor Center for High-Entropy Energy and SystemsBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Engineering University of Chinese Academy of SciencesBeijing 100049China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Developing lightweight,green,and flexible wearable electronics with high sensitivity and multifunctional sensing capabilities is of important significance in the field of outdoor sports,such as mountaineering,animal tracking and *** work proposes a silk fibroin fibers-based triboelectric nanogenerator(SF TENG)to harvest tiny energy from human fingertip tapping and act as a self-powered tactile *** SF-TENG adopts a green,efficient,and low-cost fabrication strategy,in which a breathable and electropositive silk fibroin fiber membrane and a silver conductive layer are prepared by electrostatic spinning and magnetron sputtering,and combined with a conductive cloth and a breathable tape to form a flexible sensor that can be attached to a human *** thin and soft portable TENG device,having a thickness of only 0.3 mm and a mass of 354 mg at the dimension of 4.5 cm×4.5 cm,can generate a maximum power density of 1.0 mW·m^(–2).Furthermore,the SF-TENG has excellent sensitivity of 1.767 mV·Pa^(–1) with good cyclic *** superior sensing characteristics provide new avenues for Morse code applications toward outdoor wearable autonomous *** proposed SF-TENG offers promising solutions in multi-scenario outdoor sport,human-machine interface interaction,and security systems.

关键词： flexible electronics triboelectric nanogenerator silk fibroin Morse code translation

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Video-Based Jump Height Estimation in Athletic Performance Testing

Video-Based Jump Height Estimation in Athletic Performance T...

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2023 IEEE International Workshop on Sport, Technology and Research, STAR 2023

作者： Lokas, Ivan Vasilj, Matea Skender, Sven Vucetic, Vlatko Mihaldinec, Hrvoje Dzapo, Hrvoje Ultrax Technologies Software Development Zagreb Croatia Human Performance Laboratory Faculty of Kinesiology Zagreb Croatia Hardware Development Ultrax Technologies Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Electronic Systems and Information Processing Zagreb Croatia

ISBN: (纸本)9798350316056

Jump height estimation is well-studied problem in sports science with a variety of methods proposed over the years. This paper introduces a portable, simple-to-setup and a cost-effective video-based jump height estimation method, and compares it with existing traditional methods that use expensive and specialized equipment. To enable more accurate comparison between different systems, a custom-designed Inertial Measurement Unit (IMU) was used as an additional sensor input. The analysis was performed on eleven female physically active handball players. The dataset consists of three most common vertical jump variations: squat jump, counter-movement jump and counter-movement jump with free arms. The analysis show that our video-based approach achieves comparable results to the traditional methods. © 2023 IEEE.

关键词： Cost effectiveness

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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