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arXiv 2022年

作者： Zambirinis, Sofoclis Hartle, Harrison Papadopoulos, Fragkiskos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus Network Science Institute Northeastern University BostonMA02115 United States

We consider and analyze a dynamic model of random hyperbolic graphs with link persistence. In the model, both connections and disconnections can be propagated from the current to the next snapshot with probability ω ∈ [0, 1). Otherwise, with probability 1−ω, connections are reestablished according to the random hyperbolic graphs model. We show that while the persistence probability ω affects the averages of the contact and intercontact distributions, it does not affect the tails of these distributions, which decay as power laws with exponents that do not depend on ω. We also consider examples of real temporal networks, and we show that the considered model can adequately reproduce several of their dynamical properties. Our results advance our understanding of the realistic modeling of temporal networks and of the effects of link persistence on temporal network properties. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Micromagnetics Simulation of Highly Efficient Oscillation of Magnetization in a Ferromagnetic Local Areas via Spin Waves Induced by Spin-Orbit Torque

Micromagnetics Simulation of Highly Efficient Oscillation of...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Tetsunori Koda Sho Muroga Yasushi Endo Department of Electronic Mechanical Engineering National Institute of Technology Oshima College Suo-Oshima Japan Department of Mathematical Science and Electrical-Electronic-Computer Engineering Graduate School of Engineering Science Akita University Akita Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai Japan Center for Spintronics Research Network Tohoku University Sendai Japan Center for Science and Innovation in Spintronics (CSIS) Organization for Advanced Studies Tohoku University Sendai Japan

We study numerical simulation of highly efficient oscillation of magnetization in a ferromagnetic local region using the spin-orbit torque. It is found that in the presence of multiple SOT regions, the precession motion can be excited at a lower current density through the interaction. This interaction is caused by spin waves, and as a result, phase control between the precession motions is possible through the gap between the areas. Our new finding indicates that the interaction between the wave source and the spin wave is very complex in devices that utilize the interference effects of spin waves.

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Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAVs networks: A Meta-Heuristic Approach

arXiv

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arXiv 2022年

作者： Nguyen, Loc X. Tun, Yan Kyaw Dang, Tri Nguyen Park, Yu Min Han, Zhu Hong, Choong Seon The Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden The Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

In recent years, unmanned aerial vehicles (UAVs) assisted mobile edge computing systems have been exploited by researchers as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. However, it remains challenging for the standalone MEC-enabled UAVs in order to meet the computation requirement of numerous mobile users due to the limited computation capacity of their onboard servers and battery lives. Therefore, we propose a collaborative scheme among UAVs so that UAVs can share the workload with idle UAVs. Moreover, current task offloading strategies frequently overlook task topology, which may result in poor performance or even system failure. To address the problem, we consider offloading tasks consisting of a set of subtasks, and each sub-task has dependencies on other sub-tasks, which is practical in the real world. Sub-tasks with dependencies need to wait for the resulting signal from preceding sub-tasks before being executed. This mechanism has serious effects on the offloading strategy. Then, we formulate an optimization problem to minimize the average latency experienced by users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem appears to be NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two sub-problems: the offloading decision problem and the communication resource allocation problem. Then a meta-heuristic method is proposed to find the sub-optimal solution of the task offloading problem, while the communication resource allocation problem is solved by using convex optimization. Finally, we perform substantial simulation experiments, and the result shows that the proposed offloading technique effectively minimizes the average latency of users, compared with other benchmark schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

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A Multi-Modal System Featuring Wireless Flexible Sensor Patches and a Depth-Sensing Imager for Home-Based Monitoring of Rehabilitation Exercises

Integrated Circuits and Systems

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Integrated Circuits and Systems 2024年第4期1卷 227-238页

作者： RUNTIAN YANG YUHAN HOU SAVANNA BLADE YINFEI LI VANSH TYAGI GLORIA-EDITH BOUDREAULT-MORALES JOSÉZARIFFA XILIN LIU The Edward S.Rogers Sr.Department of Electrical and Computer Engineering(ECE) University of TorontoTorontoON M5S 1A1Canada Institute of Biomedical Engineering University of TorontoTorontoON M581A1Canada KITE Research Institute Toronto Rehabilitation Institute-University Health Network(UHN)TorontoON M581A1Canada

Monitoring rehabilitation progress at home over the long term following a spinal cord injury(SCI)is crucial for maximizing therapeutic outcomes and enhancing the quality of life of affected *** monitoring requires collecting a range of physiological data,including surface electromyography(sEMG)and exercise motion ***,assessments typically take place in clinical settings,which can be both costly and inconvenient for *** is a lack of accessible,user-friendly systems that allow individuals with SCI to independently gather this data at ***,video recordings may be necessary to verify that patients are positioning the sensors correctly and performing the exercises *** bridge this gap,we have developed a self-contained,multi-modal sensor system that captures sEMG and motion data,along with depth-sensing video to track patient exercises while ensuring privacy by minimizing identifiable *** system includes a configurable number of wireless,multisensor wearable patches that are easy to attach and comfortable for extended use,along with a time-of-flight depth-sensing *** multi-modal data is streamed and synchronized in real-time on a Raspberry Pi,establishing an innovative platform to support SCI rehabilitation and adaptable for various clinical monitoring applications.

关键词： Bluetooth low energy embedded system multimodal sensor system rehabilitation monitoring SEMG spinal cord injury wireless wearable device

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Uplink transmission design for crowded correlated cell-free massive MIMO-OFDM systems

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Science China(Information Sciences) 2021年第8期64卷 127-142页

作者： Junyuan GAO Yongpeng WU Yongjian WANG Wenjun ZHANG Fan WEI The Department of Electronic Engineering Shanghai Jiao Tong University The National Computer Network Emergency Response Technical Team/Coordination Center of China

In cell-free massive multiple-input multiple-output(MIMO) orthogonal frequency division multiplexing(OFDM) systems, user equipments(UEs) are served by many distributed access points(APs),where channels are correlated owing to finite angle-delay spread in realistic outdoor wireless propagation environments. Meanwhile, the number of UEs is growing rapidly for future fully networked society. In this paper, we focus on the uplink transmission design in crowded correlated cell-free massive MIMO-OFDM systems with a limited number of orthogonal pilots. For the pilot transmission phase, we identify active UEs based on non-orthogonal pilot phase shift hopping patterns and non-orthogonal adjustable phase shift pilots(APSP). We derive a closed-form expression of mean square error of channel estimation(MSE-CE) and obtain an optimal condition for minimizing MSE-CE. According to this condition, the APSP set allocation scheme is proposed. Furthermore, for the data transmission, the max-min power control algorithm is devised to maximize the minimum spectral efficiency(SE) lower bound among active UEs. Simulation results indicate significant performance gains in terms of MSE-CE for the proposed APSP set allocation scheme. The proposed power control scheme can further improve the minimum SE among active UEs. Hence, they are crucial for crowded correlated cell-free massive MIMO-OFDM systems.

关键词： APSP set allocation cell-free massive MIMO-OFDM correlated channels crowded scenarios power control

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences

Resilient Routing Table Computation Based on Connectivity Pr...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： János Tapolcai Péter Babarczi Pin-Han Ho Lajos Rónyai Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Deptartment of Electrical and Computer Engineering University of Waterloo Canada Eötvös Loránd Research Network and Department of Algebra Institute for Computer Science and Control Institute of Mathematics Budapest University of Technology and Economics Budapest Hungary

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences.

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RelativeRFF: Multi-Antenna Device Identification in Multipath Propagation Scenarios

RelativeRFF: Multi-Antenna Device Identification in Multipat...

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IEEE International Conference on Communications (ICC)

作者： Hongyi Luo Guyue Li Yuexiu Xing Junqing Zhang Aiqun Hu Xianbin Wang School of Cyber Science and Engineering Southeast University Nanjing China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical Engineering and Electronics University of Liverpool Liverpool U.K. Purple Mountain Laboratories for Network and Communication Security Nanjing China Department of Electrical and Computer Engineering University of Western Ontario CA

Radio frequency fingerprinting (RFF) is a promising solution for realizing secure and efficient device authentication. The multipath channel overshadows and disrupts the RFF extraction, which causes difficulties in training new models in the presence of fading. Existing approaches attempt to deal with this challenge by traversing channels through simulated channel models. However, this solution requires a large amount of data for training and it is difficult to guarantee that the training covers all possible channels. To mitigate the multipath channel effect on RFF with less training data, we propose a new method in a multi-antenna system, named Relative-RFF (R-RFF), which utilizes channel state information (CSI) feedback to counteract the multipath channel. The RFF imperfection relation between the different antenna chains of the device is proved to be retained after the counteraction of the multipath channel. Numerical results demonstrate that the proposed R-RFF can achieve an identification accuracy of 95.9% for 30 UEs in Tapped Delay Line channel with a signal-to-noise ratio of 20 dB.

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

SpreadFGL: Edge-Client Collaborative Federated Graph Learnin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Luying Zhong Yueyang Pi Zheyi Chen Zhengxin Yu Wang Miao Xing Chen Geyong Min College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster UK School of Engineering Computing and Mathematics University of Plymouth UK Department of Computer Science University of Exeter UK

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

关键词： Training Accuracy Collaboration Benchmark testing Imputation Generators Topology

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IEdroid:Detecting Malicious Android network Behavior Using Incremental Ensemble of Ensembles 27

IEdroid:Detecting Malicious Android Network Behavior Using I...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Liu, Cong Yan, Anli Chen, Zhenxiang Zhang, Haibo Yan, Qiben Peng, Lizhi Zhao, Chuan University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing China School of Information Science and Engineering University of Jinan China Hainan University China University of Otago Department of Computer Science Dunedin9016 New Zealand Michigan State University Department of Computer Science and Engineering United States

ISBN: (纸本)9781665408783

Malware detection has attracted widespread attention due to the growing malware sophistication. Machine learning based methods have been proposed to find traces of malware by analyzing network traffic. However, network traffic exhibits a series of growing and changing states, which makes it challenging to design a detection model that can detect malicious traffic over a long period without the need for costly retraining. In this paper, we present, IEdroid, an Android malicious network behavior detection method that leverages incremental ensembles for model update. Specifically, we train multiple classifiers to form an interim ensemble in distributed cluster environment, and update the interim ensemble by removing and adding classifiers. The generated model is composed of multiple interim ensembles that can adapt to the network traffic. We evaluated the performance of IEdroid using a dataset consisting of 98,565 benign and 41,267 malicious flows. Results show that IEdroid can effectively detect malicious traffic compared with state-of-the-art detection models. The experiment trained IEdroid on datasets incrementally for 10 times without a significant loss on accuracy, precision, recall, and F-Measure, compared with re-training from scratch with full data. © 2021 IEEE.

关键词： Big data

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