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SSRN 2023年

作者： Banafshehvaragh, Samira Tahajomi Zarei, Mani Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan

One of the most significant challenges of vehicular ad-hoc networks (VANETs) is to establish reliable connections with the network infrastructure despite the participation concern of attacking vehicles in routing. Vehicles need a proper defense mechanism against all types of attacks even if they are accompanied by a reliable path planning strategy. This paper proposes RSR-IDS, a reliable score-based routing protocol using an intrusion detection system in VANETs. RSR-IDS detects abnormal data to calculate the untrust score (US) by deploying an intrusion detection system (IDS) in the data center. An IDS-based scoring mechanism is deployed in the data center. The classifier of the IDS is trained using three machine learning-based algorithms. These algorithms include the decision tree, random forest, and extra trees. Then, RSR-IDS selects a route with the lowest total USs and hop count compared to others for communications between the source and the destination. RSR-IDS is evaluated based on the accuracy, F1-score, false negative rate (FNR), packet delivery ratio (PDR), packet loss ratio (PLR), and end-to-end (E2E) delay criteria using OMNeT++ simulator. The most significant improvement of RSR-IDS includes 14.1% in accuracy, 11.4% in F1-score, and 5.4% in PDR regarding various vehicle densities. © 2023, The Authors. All rights reserved.

关键词： Vehicular ad hoc networks

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SHE: A Generic Framework for Data Stream Mining over Sliding Windows 22

SHE: A Generic Framework for Data Stream Mining over Sliding...

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Proceedings of the 51st International Conference on parallel Processing

作者： Yuhan Wu Zhuochen Fan Qilong Shi Yixin Zhang Tong Yang Cheng Chen Zheng Zhong Junnan Li Ariel Shtul Yaofeng Tu School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China and PCL Research Center of Networks and Communications Peng Cheng Laboratory China School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China National University of Defense Technology China Redis Labs Israel ZTE Corporation China

ISBN: (纸本)9781450397339

1Data stream mining over a sliding window is a fundamental problem in many applications, such as financial data trackers, intrusion detection and QoS. To meet the demand for high throughput of high speed data streams, sliding window algorithms turn to hardware platforms including FPGA/ASIC and programmable switches. These hardware platforms have three constraints for algorithms running on, which are 1) small memory usage 2) single stage memory access and 3) limited concurrent memory access. Algorithms perfectly fit in with these constraints will enable a highest utilization of these hardware platforms. However, no existing sliding window algorithm is specifically designed for hardware platforms. In this paper, we propose the Sliding Hardware Estimator (SHE), which is a generic framework that extends existing fixed window algorithms to sliding windows on hardware platforms. The key idea of SHE is that, during insertions we approximately delete out-dated information with little time and space overhead, while during queries we design sophisticated techniques to minimize error. We have fully implemented our SHE on FPGA, achieving a throughput of 544 Mips. We apply SHE to four typical data stream mining tasks. Experimental results show that, when compared with the state-of-the-art which cannot be implemented in hardware, SHE reduces the error by up to 100 times in membership queries. All related source codes are released at Github.

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Breaking IoT Data Silos: Trustworthy Data Trading with Consortium Blockchain and Zero-Knowledge Proof

Breaking IoT Data Silos: Trustworthy Data Trading with Conso...

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International Conference on computer Supported Cooperative Work in Design

作者： Wanshan Liu Yubing Han Guijuan Wang Yao Huang Anming Dong Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The Internet of Things (IoT) connects numerous de-vices and sensors, generating data with significant informational and economic value. However, data silos hinder effective data utilization and trading, leading to the dispersion of data across various devices and systems. Additionally, traditional third-party trading models face challenges related to data security and trust. To address these issues, this paper proposes a secure data trading framework based on a consortium blockchain and designs a corresponding solution. Specifically, it introduces the integration of zero-knowledge proofs into the smart contract scheme for authenticity and integrity verification of transaction data. From the perspective of IoT device users, this paper aims to enable secure data trading through a decentralized platform, using off-chain storage methods to reduce the blockchain's data burden while ensuring security and privacy. Off-chain storage encrypts and securely stores sensitive data, recording only necessary information on the blockchain, effectively protecting user privacy. To validate the practicality of the proposed solution, experiments were conducted using Hyperledger Fabric, demonstrating its feasibility in facilitating secure storage and trustworthy trading of IoT data. Finally, this study analyzes the experimental results and offers valuable insights for future research.

关键词： Data privacy Limiting Scalability Smart contracts Reliability engineering Blockchains Sensors Recording Internet of Things Secure storage

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Synthesized Acoustic Vortex-Frequency Comb via Rotational Doppler Effect

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Physical Review Letters 2025年第13期134卷 137001-137001页

作者： Yurou Jia Xuan Zhang Suying Zhang Guoqiang Xu Taoyu Chen Hongtao Zhou Yechao Bai Ying Cheng Dajian Wu Department of Physics MOE Key Laboratory of Modern Acoustics Collaborative Innovation Center of Advanced Microstructures Jiangsu Physical Science Research Center Nanjing University Nanjing 210093 China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Mechanics School of Mechanical Engineering Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Jiangsu Key Lab on Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210023 China

The synergistic control of light’s frequency and orbital angular momentum (OAM) via integrated nonlinear ring microresonators is crucial for creating spatiotemporal optical waveforms and advancing optical metrology. A direct analog of this capability in acoustics is challenging due to the nonlinearity-based mechanisms’ dependence on external driving and limited frequency conversion efficiency. Here, we present an acoustic synthesized comb structure derived from spatiotemporal metasurfaces, concurrently controlling frequency and OAM characteristics with high conversion efficiency and removing the constraints of critical driving frequency and power level in nonlinear mechanisms. By optimizing comb structures, energy distributions among OAM modes are adjusted to different frequency lines. We finally propose spatiotemporal metalens devices based on vortex-frequency combs, enabling synchronously multiple-order spatial differential operations for parallel data processing. This Letter is groundbreaking in integrating spectral- and spatial-domain acoustic waves, promising multifunctional imaging and advantages in spatiotemporal beams.

关键词： Vortex flow

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An Adaptive Handover Scheme to support UE with various movement speeds in 5G network

An Adaptive Handover Scheme to support UE with various movem...

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2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022

作者： Yoon, Mahnsuk Park, Jeaseok Park, Taeuk Seo, Jihun Yun, Jang-Kyu Cho, Keuchul Future Mobile Communications Research Center Gumi-si Korea Republic of Korea Transport Institute Center for Connected and Automated Driving Research Department of the Fourth Industrial Revolution and Transport Sejong-si Korea Republic of Gyeongbuk Institute of It Convergence Industry Technology Vehicle Research Team Gyeongsan-si Korea Republic of Kyungpook National University School of Computer Secience and Engineering Software Education Center Daegu-si Korea Republic of

ISBN: (数字)9781665464345

ISBN: (纸本)9781665464345

The 5G network is infrastructure that can be supported by extending the mobile speed of UE supported from 350 km/h in the existing 4G network environment to 500 km/h. The 5G network has to support the fast movement speed of UE. 3GPP-based handover has a problem in that it cannot guarantee QoS for the mobility of some UEs as it provides UE based handover in a single environment that does not consider the network service environment with various movement speeds of UE. In this paper, an adaptive handover scheme is proposed to the cell located to the moving direction by determining and triggering the handover time according to the individual speed of UE to ensure the QoS according to the UE's seamless service and movement between cells. In simulation test to verify the proposed scheme compared with existing handover scheme based on 3GPP, the proposed scheme shows excellent performance in handover failure and data transmission for UE with a fast movement speed. © 2022 IEEE.

关键词： 5G mobile communication systems

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Learning optical flow from continuous spike streams 22

Learning optical flow from continuous spike streams

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Rui Zhao Ruiqin Xiong Jing Zhao Zhaofei Yu Xiaopeng Fan Tiejun Huang National Engineering Research Center of Visual Technology (NERCVT) Peking University and Institute of Digital Media School of Computer Science Peking University National Computer Network Emergency Response Technical Team National Engineering Research Center of Visual Technology (NERCVT) Peking University and Institute of Digital Media School of Computer Science Peking University and Institute for Artificial Intelligence Peking University School of Computer Science and Technology Harbin Institute of Technology

ISBN: (纸本)9781713871088

Spike camera is an emerging bio-inspired vision sensor with ultra-high temporal resolution. It records scenes by accumulating photons and outputting continuous binary spike streams. Optical flow is a key task for spike cameras and their applications. A previous attempt has been made for spike-based optical flow. However, the previous work only focuses on motion between two moments, and it uses graphics-based data for training, whose generalization is limited. In this paper, we propose a tailored network, Spike2Flow that extracts information from binary spikes with temporal-spatial representation based on the differential of spike firing time and spatial information aggregation. The network utilizes continuous motion clues through joint correlation decoding. Besides, a new dataset with real-world scenes is proposed for better generalization. Experimental results show that our approach achieves state-of-the-art performance on existing synthetic datasets and real data captured by spike cameras.

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Real-Time Implementation of a Spiking Neural Network-Based Control: An Application for the Ball and Plate System

Real-Time Implementation of a Spiking Neural Network-Based C...

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American Control Conference (ACC)

作者： Diego Chavez Arana Omar A. Garcia A Ignacio Rubio Scola Eduardo S. Espinoza Luis Rodolfo Garcia Carrillo Andrew Sornborger Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Center for Research and Advanced Studies National Polytechnic Institute Mexico City Mexico Department of Industrial Products Engineering National Institute of Industrial Technology CONICET and National University of Rosario Argentina Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark sub-actuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances.

关键词： Neuromorphic engineering Simulation Prototypes Neural engineering computer architecture Spiking neural networks Control systems

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IDFusion: An Infrared and Visible Image Fusion Network for Illuminating Darkness

IDFusion: An Infrared and Visible Image Fusion Network for I...

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International Conference on computer Supported Cooperative Work in Design

作者： Guohua Lv Xiyan Wang Zhonghe Wei Jinyong Cheng Guangxiao Ma Hanju Bao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Comuter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Lianxin Intelligent Technology (Shandong) Co. Ltd Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The purpose of infrared and visible image fusion is to combine the background information of the visible images and the thermal target information of the infrared images. Current fusion methods often neglect the challenges of low-light conditions. In the night scene, the existing methods fail to capture texture information from the visible images that is hidden in the darkness, producing suboptimal fusion results that can hinder subsequent visual applications. Therefore, we propose a method for infrared and visible image fusion under night scenes, termed as IDFusion. Specifically, our method is divided into three parts, first, a dense auto-encoder is designed to obtain more useful features from the source images. Then we design a brightness enhancement network that removes visible degraded illumination maps to obtain brightness-enhanced features. Finally, a texture fusion network is designed so that the infrared features and enhanced visible features avoid texture loss during the fusion process. Experimental results show that our network can obtain better fusion results, outperforming state-of-the-art methods in terms of subjective visual effects and quantitative metrics.

关键词： Measurement Federated learning Brightness Lighting Visual effects Image fusion

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User Cooperation with Power Control for Federated Learning in CFmMIMO Networks

User Cooperation with Power Control for Federated Learning i...

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IEEE International Conference on Communications Workshops, ICC

作者： Han Bao Ke Xiong Ruichen Zhang Pingyi Fan Dusit Niyato Khaled Ben Letaief Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing China Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

In synchronous federated learning (SFL), only if all users successfully deliver their local parameters to the central server, the central server can efficiently aggregate a global model. In this case, the user with the worst quality link becomes the bottleneck limiting the training performance of SFL. To cope with this issue for SFL in cell-free massive multiple-input multiple-output (CFmMIMO) network, this paper proposes a joint user cooperation and power control (JUCPC) method. Particularly, the users with good quality links are employed to assist the ones with worse quality links to transfer their parameters to the central server. Then, the power control is employed to coordinate the interference among users to further improve the transmission performance for SFL. To find the joint optimal user cooperation pairing and the power control coefficients, we formulate an optimization problem to minimize the maximum transmission time among users. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fairness-based algorithm to select the helping users and also pair them with the ones who required to be assisted. For the second sub-problem, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCPC method can efficiently reduce the transmission time of the worst user for SFL. Moreover, compared to the traditional optimization method based on convex optimization solver (CVX), our proposed DDPG-based one requires much shorter execution time while achieves approximating result to the CVX-based algorithm.

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An intelligent Ama safety protection system based on smart IoT data and deep learning

An intelligent Ama safety protection system based on smart I...

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International Symposium on computer, Consumer and Control (IS3C)

作者： Bo-Yan Lin Wei-Che Huang Ming-Siang Wu Iching Lin Sheng-Hung Shih Ya-Ling Kao Yu-Da Lin Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Central Regional Transportation Development Research Center Taichung Taiwan Department of Marketing and Logistics Management National Penghu University of Science and Technology Penghu Taiwan

Ama is a challenging and risky profession. The income of this profession is highly dependent on the weather and the tides. Although technology can conveniently assist Ama in checking weather and tide information, no platform currently integrates both and performs analysis for Ama’s reference. Therefore, this study developed a system that uses data from the Central Weather Bureau and the Open Data Platform for Meteorological Data to build a dedicated database for integrated analysis. After obtaining the coastal hazard prediction results through algorithms and Recurrent Neural Networks, the information is displayed in an APP for the user’s reference. The APP also records the user’s current location, and if any danger occurs, it can report to the rescue unit through the APP. The rescue unit can know the location through the web page and proceed with the rescue.

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