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International Conference on Computer and Communications (ICCC)

作者： Kun Guo Dongbin Wang Hui Zhi Yibo Gao Tao Zhang Tiejun Lv Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing University of Posts and Telecommunications Beijing China TravelSky Technology Limited Beijing China Cyberspace Security Research Center Peng Cheng Laboratory Beijing University of Posts and Telecommunications Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9781665409513

Existing routing algorithms in Flying Ad-Hoc Net- works (FANETs) lack the optimization consideration of destruction resistance cost caused by routing recovery when some nodes fail suddenly. In this paper, we propose a hybrid routing algorithm based on destruction resistance cost and reliability. Based on the real-time network topology and network state, the algorithm can provide a multi-parameters-based routing path with low destruction resistance cost for communication transmission. It can ensure that the main routing path can be quickly recovered after interruption and reduce the process of discovering new routing paths. We have constructed a variety of simulation scenarios to analyze the proposed algorithm and the existing algorithms. The simulation results show that the pro- posed algorithm has good performance in reducing destruction resistance cost.

关键词： Resistance Analytical models Costs Computer network reliability Simulation Routing Real-time systems

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Remote Healthcare Monitoring System for Aging Population Based on IoT and Big Data Analysis

Remote Healthcare Monitoring System for Aging Population Bas...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qiang Liu Songlin Sun Liang Gong Zhongle Wu Mohamed Cheriet Michel Kadoch National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Academy of Broadcasting Planning NRTA China ETS University of Quebec Montreal Canada

ISBN: (数字)9781728157849

ISBN: (纸本)9781728157856

As the population ages, this will put tremendous pressure on pensions and the medical system. Health insurance for the elderly poses a major challenge to the allocation of limited medical resources. The Internet of Things (IoT) is widely regarded as a potential solution to relieve pressure on healthcare systems. With the rapid development of wireless communication technologies such as backscatter communication, Wi-Fi and 5G, in vivo and in vitro medical devices have been greatly developed. In this article, we propose a real-time big data healthcare analysis system based on the combination of shallow neural networks and deep neural networks. The system consists of two parts: the detection of fog layer abnormality based on shallow neural network and the diagnosis of cloud disease based on deep neural network. The system can not only make full use of the real-time nature of the fog layer but also give full play to its powerful computing power based on reducing the computing pressure of the cloud layer.

关键词： Cloud computing Neural networks Sociology Real-time systems Internet of Things Statistics Medical diagnostic imaging

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SemHARQ: Semantic-Aware HARQ for Multi-task Semantic Communications

arXiv

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arXiv 2024年

作者： Hu, Jiangjing Wang, Fengyu Xu, Wenjun Gao, Hui Zhang, Ping The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Network and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Peng Cheng Laboratory Shenzhen518066 China The Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

Intelligent task-oriented semantic communications (SemComs) have witnessed great progress with the development of deep learning (DL). In this paper, we propose a semantic-aware hybrid automatic repeat request (SemHARQ) framework for the robust and efficient transmissions of semantic features. First, to improve the robustness and effectiveness of semantic coding, a multi-task semantic encoder is proposed. Meanwhile, a feature importance ranking (FIR) method is investigated to ensure the important features delivery under limited channel resources. Then, to accurately detect the possible transmission errors, a novel feature distortion evaluation (FDE) network is designed to identify the distortion level of each feature, based on which an efficient HARQ method is proposed. Specifically, the corrupted features are retransmitted, where the remaining channel resources are used for incremental transmissions. The system performance is evaluated under different channel conditions in multi-task scenarios in Internet of Vehicles. Extensive experiments show that the proposed framework outperforms state-of-the-art works by more than 20% in rank-1 accuracy for vehicle re-identification, and 10% in vehicle color classification accuracy in the low signal-to-noise ratio regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Rate Control for VVC Intra Coding with Simplified Cubic Rate-Distortion Model

Rate Control for VVC Intra Coding with Simplified Cubic Rate...

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IEEE Workshop on Multimedia Signal Processing

作者： Yizhao Wang Jiaqi Zhang Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

In this paper, we propose a simplified cubic polynomial R-D model with corresponding rate control methods for Versatile Video Coding (VVC) intra frame coding. First, we explore the rate-distortion (R-D) characteristics of VVC intra coding. By comparing several potential R-D modeling approaches, a new intra coding R-D model has been proposed based on the simplified cubic polynomial function. Subsequently, we derive the corresponding $R-\lambda$ model and introduce a complexity measurement to improve the performance of intra frame rate control. Furthermore, we propose a Coding Tree Unit (CTU)-level rate control method based on the newly proposed R-D model and further develop a pre-compression-based approach on this basis. Experimental results show that the proposed method can achieve 1.87% and 0.55% bit rate reduction for All-Intra (AI) and Random-Access (RA) configurations over the original rate control in VVC Test Model (VTM), while the computational complexity increment is negligible. Meanwhile, the enhanced bit rate accuracy from rate control has been observed in the proposed methods.

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Joint resource management for MC-NOMA: A deep reinforcement learning approach

arXiv

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arXiv 2021年

作者： Wang, Shaoyang Lv, Tiejun Ni, Wei Beaulieu, Norman C. Guo, Y. Jay Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Sydney2122 Australia Beijing Key Laboratory for Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users. © 2021, CC BY.

关键词： Resource allocation

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An Event-Driven Service-Oriented Architecture for the Internet of Things

An Event-Driven Service-Oriented Architecture for the Intern...

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IEEE Asia-Pacific Conference on Services computing (APSCC)

作者： Lina Lan Fei Li Bai Wang Lei Zhang Ruisheng Shi School of Network Education Beijing University of Posts and Telecommunications Beijing China Information Systems Institute Vienna University of Technology Vienna Austria Beijing University of Posts and Telecommunications Beijing Beijing CN School of Computer Beijing University of Posts and Telecommunications Beijing China Education Ministry Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

IoT (Internet of Things) bridges the physical world and information space. IoT services are environment sensitive and event-driven. The new IoT service architecture should adapt to these features. This paper analyses IoT sensing service characteristics and proposes the future services architecture. It is focused on the middleware architecture and the interface presentation technology. In the middleware layer, the traditional SOA architecture is insufficient in the real-time response and parallel process of services execution, this paper proposes that the new sensing service system based on EDSOA (Event Driven SOA) architecture to support real-time, event-driven, and active service execution. At presentation layer, this paper presents the new IoT browser features including using augmented reality technology to input and output, and realize the superposition presentation of the physical world and abstract information. Through a use case and proof-of-concept implementation-road manhole covers monitoring system - we verify the feasibility of the proposed ideas and frameworks.

关键词： Service-oriented architecture Sensors Computer architecture Real-time systems Browsers Cameras

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An Improved CTU-level Rate Control Algorithm Based on Temporal Domain Motion Intensity

An Improved CTU-level Rate Control Algorithm Based on Tempor...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Shaokang Wang Songlin Sun Jiaqi Zou Ruotong Wu Zhilei Ling Xingtong Liu School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK Business School Beijing Normal University Beijing China

Rate control (RC) plays a vital role in Versatile Video Coding (VVC) and video transmission. However, the RC algorithm in VVC remains the problem that the process of updating model parameters only considers the impact of the current encoding block content, without considering the similarity and reference value between different Coding Tree Units (CTUs) in the temporal domain. As a result, the coding performance is not optimal. To address this problem and further optimize the RC of VVC, this paper proposes an improved CTU-level rate control model parameter updating algorithm based on temporal domain motion intensity and we integrate this algorithm into VTM 1S.0. Experiments show that the proposed algorithm achieves better coding performance than anchor with 0.74% and 1.03% BD-rate saving under the Low Delay B (LDB) and Random Access (RA) configurations, respectively, while obtaining smaller bitrate error under common test conditions.

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

arXiv

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arXiv 2024年

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado SpringsCO United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China JinYinHu Laboratory Wuhan China

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Intellicise Wireless networks From Semantic Communications: A Survey, Research Issues, and Challenges

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IEEE Communications Surveys and Tutorials 2024年 1-1页

作者： Zhang, Ping Xu, Wenjun Liu, Yiming Qin, Xiaoqi Niu, Kai Cui, Shuguang Shi, Guangming Qin, Zhijin Xu, Xiaodong Wang, Fengyu Meng, Yue Dong, Chen Dai, Jincheng Yang, Qianqian Sun, Yaping Gao, Dahua Gao, Hui Han, Shujun Song, Xiaodan State Key Laboratory of Network and Switching Technology Beijing University of Posts and Telecommunications Beijing China Shenzhen China Peng Cheng Laboratory Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Ministry of Education Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Artificial Intelligence Xidian University Xi’an China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Guangzhou Institute of Technology Xidian University Guangzhou China

Information and communication technology (ICT) has been an essential part of modern society. However, the current communication systems are not sufficient to meet the demands of emerging applications. Intellicise (intelligent and concise) wireless networks, with their inherent characteristics of intelligence-endogenous and primitive-concise, have been proposed as a promising research direction. In this paper, we focus on intellicise wireless networks from semantic communication (SemCom). We present a comprehensive framework of intellicise wireless networks, including components such as brain for intellicise wireless networks (BIWN), intellicise signal processing, intellicise information transmission, intellicise network organization, and intellicise service bearing. We also investigate the enabling technologies and driving factors of intellicise wireless networks. Subsequently, we introduce the applications of intellicise wireless networks and envision new services. Finally, we outline the challenges of implementing intellicise wireless networks from a broad perspective and discuss possible solutions. IEEE

关键词： Mobile telecommunication systems

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