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作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

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Multi-Level Chaotic Maps for 3D Textured Model Encryption 2nd

Multi-Level Chaotic Maps for 3D Textured Model Encryption

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Jin, Xin Zhu, Shuyun Wu, Le Zhao, Geng Li, Xiaodong Zhou, Quan Lu, Huimin Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (纸本)9783030177621

With the rapid progress of virtual reality and augmented reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often considers the point clouds, the meshes, and the textures individually. In this work, a multi-level chaotic maps model for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons, and textures. For vertices which make main contribution for recognizing, we use high-level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold’s cat map and 1D logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method and use the same high-level chaotic map for point cloud, polygons, and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, and correlation attack. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Textures

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2D Federated Learning for Personalized Human Activity Recognition in Cyber-Physical-Social Systems

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第6期9卷 3934-3944页

作者： Zhou, Xiaokang Liang, Wei Ma, Jianhua Yan, Zheng Wang, Kevin I-Kai Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Hunan Univ Technol & Business Sch Frontier Crossover Studies Changsha 410205 Peoples R China Changsha Social Lab Artificial Intelligence Changsha 410205 Peoples R China Hosei Univ Fac Comp & Amp Informat Sci Tokyo 1028160 Japan Xidian Univ State Key Lab Integrated Serv Networks Xian 710126 Peoples R China Xidian Univ Sch Cyber Engn Xian 710126 Peoples R China Aalto Univ Dept Commun & Networking Espoo 02150 Finland Univ Auckland Dept Elect Comp & Software Engn Auckland New Zealand

The proliferation of the Internet of Things (IoT), wearable computing, and social media technologies bring forward the realization of the so-called Cyber-Physical-Social Systems (CPSS), which is capable of offering intelligent services in many different aspects of our day-to-day life. While CPSS offer a wide variety of data from wearable devices, challenges such as data silos and secure data sharing still remain. In this study, a 2-Dimensional Federated Learning (2DFL) framework, including the vertical and horizontal federated learning phases, is designed to cope with the insufficient training data and insecure data sharing issues in CPSS during a secure distributed learning process. Considering a specific application of Human Activity Recognition (HAR) across a variety of different devices from multiple individual users, the vertical federated learning scheme is developed to integrate shareable features from heterogeneous data across different devices into a full feature space, while the horizontal federated learning scheme is developed to effectively aggregate the encrypted local models among multiple individual users to achieve a high-quality global HAR model. A computationally efficient somewhat homomorphic encryption (SWHE) scheme is then improved and applied to support the parameter aggregation without giving access to it, which enables heterogeneous data sharing with privacy protection across different personal devices and multiple users in building a more precise personalized HAR model. Experiments are conducted based on two public datasets. Comparing with three conventional machine learning methods, evaluation results demonstrate the usefulness and effectiveness of our proposed model in achieving faster and smoother convergence, with better precision, recall, and F1 scores for HAR applications in CPSS.

关键词： Collaborative work data models Computational modeling Distributed databases Internet of Things Activity recognition Wearable computers Distance learning Homomorphic encryption Federated learning Learning systems Human activity recognition Cyber-physical systems Social factors Federated learning convolutional neural networks human activity recognition privacy protection cyber-physical-social systems

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Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第4期10卷 1742-1751页

作者： Zhou, Xiaokang Ye, Xiaozhou Wang, Kevin I-Kai Liang, Wei Nair, Nirmal Kumar C. Shimizu, Shohei Yan, Zheng Jin, Qun Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Univ Auckland Dept Elect Comp & Software Engn Auckland 1142 New Zealand Hunan Univ Technol & Business Changsha Social Lab Artificial Intelligence Changsha 410205 Peoples R China Cent South Univ Business Sch Changsha 410017 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710026 Peoples R China Xidian Univ Sch Cyber Engn Xian 710026 Peoples R China Aalto Univ Dept Commun & Networking Espoo 02150 Finland Waseda Univ Fac Human Sci Tokorozawa 3591192 Japan

The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals' physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user's health status. Distributed machine learning paradigms, such as federated learning, offer a potential solution for privacy-preserving knowledge sharing without sending raw personal data. However, federated learning is vulnerable to harmful participants that can degrade the overall model quality by sharing low-quality data. Therefore, it is critical to select suitable participants to ensure the accuracy and efficiency of federated learning. In this article, a unique clustering-based approach is proposed to use social context data for participant selection. Different edge participant groups will be established, and group-specific federated learning will be performed. The models of various edge groups will be further aggregated to strengthen the robustness of the global model. The experimental results demonstrated that through participant selection, clustering-based hierarchical federated learning can achieve better results with less participants in two different IoMT applications for ECG and human motion monitoring. This shows the efficacy of the proposed method in improving federated learning performance and efficiency in various IoMT applications.

关键词： Federated learning data models Computational modeling Biomedical monitoring Distributed databases Context modeling Monitoring Clustering federated learning Internet of Medical Things (IoMT) participant selection

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Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 4876-4891页

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400040 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Hubei Peoples R China Chongqing Univ Posts & Telecommun Elect Informat & Networking Res Inst Chongqing 400065 Peoples R China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent difference reward based actor-critic with V2I bandwidth allocation (MDRAC-VBA) solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of MDRAC-VBA.

关键词： Vehicular cyber-physical system edge computing cooperative sensing heterogeneous information fusion multi-agent deep reinforcement learning

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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第1期39卷 154-169页

作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Aalto Univ Sch Elect Engn Dept Commun & Networking Espoo 02150 Finland Oulu Univ Dept Informat Technol & Elect Engn Oulu 90570 Finland Sejong Univ Dept Comp & Informat Secur Seoul 05006 South Korea Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

关键词： Device-to-device communication data models Collaboration Servers Delays Computational modeling Training Edge caching device to device attention-weighted federated learning deep reinforcement learning

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Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing with a Shared Wireless Backhaul

arXiv

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arXiv 2024年

作者： Chen, Mingxin Zhao, Ming-Min Liu, An Li, Min Shi, Qingjiang College of Information Science and Electronic Engineering Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China School of Software Engineering Tongji University Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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CAN: CONTEXTUAL AGGREGATING NETWORK FOR SEMANTIC SEGMENTATION 44

CAN: CONTEXTUAL AGGREGATING NETWORK FOR SEMANTIC SEGMENTATIO...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Cong, Dechun Zhou, Quan Cheng, Jie Wu, Xiaofu Zhang, Suofei Ou, Weihua Lu, Huimin Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Networking Nanjing Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Jiangsu Peoples R China Guizhou Normal Univ Sch Big Data & Comp Sci Guiyang Guizhou Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu Fukuoka Japan

ISBN: (纸本)9781479981311

Fully convolutional neural networks (FCNs) have shown great success in dense estimation tasks. One key pillar of such progress is mining multi-scale context cues from features in different convolutional layers. This paper introduces contextual aggregating netvvork(CAN), a generic convolutional feature ensembling framework for semantic segmentation. Our framework first captures multi-scale contextual clues by concatenating multi-level feature representation, which carries both coarse semantics and fine details. Then it adaptively integrates stacked features to perform dense pixel estimation. The proposed CAN is trainable end-to-end, and allows us to fully investigate multi-scale context information embedded in images. The experiments show the promising results of our method on PASCAL VOC 2012 and Cityscapes dataset.

关键词： Semantic segmentation Convolutional features Fully convolutional networks Multi-scale context

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A Physics-based and data-driven Approach for Localized Statistical Channel Modeling

arXiv

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arXiv 2023年

作者： Zhang, Shutao Ning, Xinzhi Zheng, Xi Shi, Qingjiang Chang, Tsung-Hui Luo, Zhi-Quan School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel’s angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths’ powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method. © 2023, CC BY.

关键词： Statistical Physics

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Learning Beams Adaptive to the Environment: An RSRP-based Codebook Design

Learning Beams Adaptive to the Environment: An RSRP-based Co...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Xinzhi Ning Shutao Zhang Ye Xue Xi Zheng Qingjiang Shi Tsung-Hui Chang School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

The design of an effective codebook is crucial for the fifth generation (5G) systems, particularly in the context of massive multiple-input-multiple-output (MIMO) and millimeter wave communication. However, existing codebook design methods that rely on the instantaneous channel impulse response (CIR) face challenges in adapting to dynamic channels that undergo small-scale fading changes. To address this issue, this paper proposes a novel two-stage codebook design framework based solely on the reference signal receiving power (RSRP), which provides large-scale statistical information about environment attenuation. The proposed approach involves two stages: first, the angular power spectrum (APS) is estimated through sparse Bayesian learning (SBL), which uses the statistical relationship between APS and RSRP. Second, an iterative clustering and optimization algorithm is used to determine the optimal codebook based on the estimated APS. Experimental results demonstrate that the proposed codebook design framework with a smaller number of beams can adapt to localized environment well and achieves better performance than the classical DFT codebook.

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