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检索条件"任意字段=17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025"

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YogaFi: WiFi Sensing Based Yoga Pose Recognition Using Machine Learning Model

YogaFi: WiFi Sensing Based Yoga Pose Recognition Using Machi...

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international conference on Sensing Technology, ICST

作者： Gayathri Gorrepati Sruthi. P Siba K Udgata WiSeCom Lab School of Comp. and Info. Sc University of Hyderabad Hyderabad India

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

Wi-Fi sensing is an emerging technology that leverages wireless signals to detect and interpret various environmental changes and human activities. By analyzing the variations in Channel State Information (CSI), Wi-Fi sensing can provide fine-grained insights into movement, presence, and even gestures. Yoga is recognized worldwide for maintaining physical and mental health. this paper explores the innovative application of Wi-Fi sensing technology for recognizing different yoga poses. Leveraging the variations in Wi-Fi signals caused by human movement, we develop a low-cost, non-invasive, privacy-preserved system that recognizes different yoga postures. We conducted experiments in the lab environment to collect the CSI values corresponding to seven different yoga poses. Two low-cost ESP32 modules, one as a transmitter and the other as a receiver, are placed at a distance of 10 feet in line-of-sight condition, and the yoga exercise is done in between. We propose two machine-learning approaches for yoga pose classification. One is to identify the pose, and the other is to identify the direction and pose. the accuracy of determining the pose is 82.8% and the accuracy of identifying both direction and pose is 91%. this method offers a non-intrusive, privacy-preserved, cost-effective alternative to traditional vision-based systems. this provides new opportunities for remote fitness monitoring and personalized training.

关键词： Wireless communication Training Wireless sensor networks Accuracy Transmitters Receivers Sensors Wireless fidelity Channel state information Monitoring

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Secure and User Efficient EAP-based Authentication Protocol for IEEE 802.11 Wireless LANs

Secure and User Efficient EAP-based Authentication Protocol ...

引用

IEEE Internatonal conference on Mobile Adhoc and Sensor systems (MASS)

作者： Awaneesh Kumar Yadav Manoj Misra Madhusanka Liyanage Gaurav Varshney Indian Institute of Technology Roorkee Roorkee Uttarakhand India School of Computer Science University College Dublin Ireland and CWC University of Oulu Finland Indian Institute of Technology Jammu Jammu India

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

Wireless Local Area networks (WLANs) have experienced significant growth in the last two decades due to the extensive use of wireless devices. Security (especially authentication) is a staple concern as the wireless medium is accessible to everybody. Extensible Authentication Protocol (EAP) is the widely used authentication framework in WLANs to secure communication. the authentication mechanism designed on EAP is called EAP method. there are numerous EAP based and nonEAP based authentication protocols for WLANs, but there is no protocol that fulfills all the security requirements, as mentioned in RFC-4017 and other additional requirements like perfect forward secrecy, Denial-of-service (DoS) attack protection, and lightweight computation. Hence, it is fair to infer that there is an impelling need to design a protocol that can meet all the security requirements. In this paper, we propose a secure and user efficient EAP-based authentication protocol for IEEE 802.11 WLANs. the proposed protocol has been formally validated by BAN logic and the AVISPA tool [18]. the simulation results depict that the proposed protocol achieves all security requirements, as mentioned in RFC-4017 along with perfect forward secrecy, Denial-of-service (DoS) attack protection, and lightweight computation. the proposed protocol outperforms the existing protocols in terms of computation cost by reducing the computation cost by ≈ 99.9956%, 99.991%, 27.27%, 22.705% in comparison to EAP-TLS, EAP-TTLS, EAP-Ehash, EAP-SELUA, respectively. Keywords-AP, AS, AVISPA, BAN, EAP, WLANs.

关键词： Wireless communication Wireless LAN Protocols Authentication Denial-of-service attack Security Servers

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HF RFID-based Book Localization via Mobile Scanning

HF RFID-based Book Localization via Mobile Scanning

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IEEE communications Society conference on Sensor and Ad Hoc communications and networks (SECON)

作者： Lin Xu Jia Liu Xia Wang Hualin Gong Yanyan Wang Lijun Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728166308

ISBN: (纸本)9781728166315

Shelf scanning is one of the most important processes for inventory management in a library, which is able to help library users find the misplaced books and pinpoint where they are. Traditional solutions suffer from intensive manual labor, long scanning delay, or high-cost infrastructure renewal, which is a great barrier to commercial adoption. In this paper, we propose an HF RFID system that can perform the shelf scanning automatically by combining robotics and RFID technology, where the mobile robot is used for replacing the library staff and the RFID is for locating the tagged books on the bookshelves. the biggest challenge is that the commodity HF RFID device cannot capture RF phase, which is a fine-grained metric with high resolution for locating. To address this problem, we break down the task of localization into two stages, tier-level localization and order estimation, and use fuzzy logic to model and estimate the tagged book's position. We implement the system and deploy it into four libraries for practical use. Long-term extensive experimental results demonstrate that our system provides a finegrained book localization with an accuracy of a few centimeters.

关键词： Radiofrequency identification Libraries Antennas Measurement Radio frequency Robot sensing systems

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SDN-Based 6G Video Content Delivery Technique via Multi-Path Routing, Multi-Server Architecture and Multi-Connectivity

SDN-Based 6G Video Content Delivery Technique via Multi-Path...

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international conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Mykhailo Medvetskyi Mykola Beshley Ilona Scherm Oksana Urikova Halyna Beshley Teresa Dierkes Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine Saxon Study Academy BA Dresden University of Coop. Education Dresden Germany Department of Finance Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

With the rise of 6G networks, ensuring optimal video content delivery under conditions of high mobility and data volume becomes increasingly complex. this paper presents a novel SDN-based video content delivery technique tailored for 6G, which combines Multi-Path Routing, Multi-Server Architecture, and Multi-Connectivity to meet growing user demands for high-quality video streaming. Our approach integrates these three technologies to optimize data flow and ensure seamless, uninterrupted video transmission. Multi-path routing dynamically distributes data across multiple paths, minimizing latency and improving throughput. the multi-server architecture enhances system reliability and fault tolerance by distributing video content across several servers, reducing load on individual nodes. With multi-threaded downloading, the video is transmitted simultaneously through multiple streams from distributed servers, accelerating data transfer and enhancing system efficiency. Multi-connectivity, in turn, allows seamless transitions between base stations, ensuring stable connections, especially in high-mobility environments. We implement and test this combined solution in an SDN environment using 6G network parameters, focusing on its ability to adapt to network conditions and maintain high Quality of Experience (QoE) for the end-user. Our results demonstrate that this integrated approach efficiently optimizes network resources while delivering high-quality video, including 8K/16K streams, making it well-suited for future 6G networks.

关键词： 6G mobile communication Computer architecture Streaming media Routing Real-time systems Quality of experience Servers Streams Monitoring Video recording

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Towards Environment-independent Behavior-based User Authentication Using WiFi

Towards Environment-independent Behavior-based User Authenti...

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IEEE Internatonal conference on Mobile Adhoc and Sensor systems (MASS)

作者： Cong Shi Jian Liu Nick Borodinov Bruno Leao Yingying Chen Rutgers University New Brunswick NJ USA University of Tennessee Knoxville TN USA Siemens Princeton NJ USA

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

With the increasing prevalence of smart mobile and Internet of things (IoT) environments, user authentication has become a critical component for not only preventing unauthorized access to security-sensitive systems but also providing customized services for individual users. Unlike traditional approaches relying on tedious passwords or specialized biometric/wearable sensors, this paper presents a device-free user authentication via daily human behavioral patterns captured by existing WiFi infrastructures. Specifically, our system exploits readily available channel state information (CSI) in WiFi signals to capture unique behavioral biometrics residing in the user's daily activities, without requiring any dedicated sensors or wearable device attachment. To build such a system, one major challenge is that wireless signals always carry substantial information that is specific to the user's location and surrounding environment, rendering the trained model less effective when being applied to the data collected in a new location or environment. this issue could lead to significant authentication errors and may quickly ruin the whole system in practice. To disentangle the behavioral biometrics for practical environment-independent user authentication, we propose an end-to-end deep-learning based approach with domain adaptation techniques to remove the environment-and location-specific information contained in the collected WiFi measurements. Extensive experiments in a residential apartment and an office with various scales of user location variations and environmental changes demonstrate the effectiveness and generalizability of the proposed authentication system.

关键词： Wireless communication Wireless sensor networks Biometrics (access control) Authentication Internet of things Biosensors Wireless fidelity

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Chemical Substance Classification Using Long Short-Term Memory Recurrent Neural Network

Chemical Substance Classification Using Long Short-Term Memo...

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2017 17th IEEE international conference on communication Technology (ICCT 2017)

作者： Jinlei Zhang Junxiu Liu Yuling Luo Qiang Fu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

this paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural networks(LSTM-RNN). the chemical substance data was collected using a mass spectrometer which is a time-series data. the classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. the experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.

关键词： recurrent neural networks chemical substances long short-term memory feed forward neural networks

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Architecture for Secure Data Distribution Using Cloud-Based Digital Twins

Architecture for Secure Data Distribution Using Cloud-Based ...

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IEEE international conference on Utility and Cloud Computing (UCC)

作者： Turki Alhazmi Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Digital Twin networks (DTN) is an emerging technology that aims to connect digital twins for data sharing purposes. It has the potential to solve data sharing issues between different entities without physical constraints, such as physical separation. this is especially useful in smart city context because they span a large geographical area where systems that are far away need to cooperate to achieve a certain objectives. One of the key issues in DTN is data security and ensuring confidentiality, integrity, availability and privacy of information at scale. this article proposes a Secure Data Sharing architecture for cloud-based Digital Twins (SDTN). It uses WebSockets protocol to provide a secure and scalable environment for DTs to share information. SDTN distinguishes between two types of clients, a mobile client and a stationary one. It is a service oriented architecture where each WebSockets server is wrapped and provided as a service for data distribution called a Data Sharing Service (DSS). A DSS provides access to data through a publish/subscribe interface where the topics are decided by connected clients which include other DSSs and DTs. Data discovery is done through a Discovery Service (DS) and authentication for all entities (DSS, DS, and DTs) is handled by an Authentication Service (AS). Furthermore, these service are managed by a special service called the Service Manager Service (SMS). We showcase how SDTN can be implemented and scaled in a cloud environment. Furthermore, we demonstrate the applicability of our architecture in the context of digital twins of smart cities. We demonstrate how scalability, authentication, discovery and data distribution are achieved in this context. Also, we sketch its implementation in a typical cloud environment.

关键词： Cloud computing Protocols Smart cities Scalability Authentication Computer architecture Spread spectrum communication Digital twins Service-oriented architecture Servers

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Slim-DP: A Multi-Agent System for communication-Efficient Distributed Deep Learning 18

Slim-DP: A Multi-Agent System for Communication-Efficient Di...

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Proceedings of the 17th international conference on Autonomous Agents and MultiAgent systems

作者： Shizhao Sun Wei Chen Jiang Bian Xiaoguang Liu Tie-Yan Liu Nankai University Tianjin China Microsoft Research Beijing China

To afford the huge computational cost, large-scale deep neural networks (DNN) are usually trained on the distributed system, especially the widely-used parameter server architecture, consisting of a parameter server as well as multiple local workers with powerful GPU cards. During the training, local workers frequently pull the global model and push their computed gradients from/to the parameter server. Due to the limited bandwidth, such frequent communication will cause severe bottleneck for the training acceleration. As recent attempts to address this problem, quantization methods have been proposed to compress the gradients for efficient communication. However, such methods overlook the effects of compression on the model performance such that they either suffer from a low compression ratio or an accuracy drop. In this paper, to better address this problem, we investigate the distributed deep learning as a multi-agent system (MAS) problem. Specifically, 1) local workers and the parameter server are separate agents in the system; 2) the objective of these agents is to maximize the efficacy of the learned model through their cooperative interactions; 3) the strategy of the agents describes how they take actions, i.e. communicate their computed gradients or the global model; 4) rational agents always select the best-response strategy with the optimal utility. Inspired by this, we design a MAS approach for distributed training of DNN. In our method, the agents first estimate the utility (i.e., the benefit to help improve the model) of each action (i.e., transferring a subset of the gradients or the global model), and then take the best-response strategy based on their estimated utilities mixed with ε-random exploration. We call our new method Slim-DP as it, being different from the standard data-parallelism, only communicates a subset of the gradient or the global model. Our experimental results demonstrate that our proposed Slim-DP can reduce more communication cost

关键词： distributed training application of multi-agent system deep learning best response strategy

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Learn to Optimize: Adaptive VNF Provisioning in Mobile Edge Clouds

Learn to Optimize: Adaptive VNF Provisioning in Mobile Edge ...

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IEEE communications Society conference on Sensor and Ad Hoc communications and networks (SECON)

作者： Qiufen Xia Wenhao Ren Zichuan Xu Pan Zhou Wenzheng Xu Guowei Wu International School of Information Science and Engineering Dalian University of Technology Dalian China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9781728166308

ISBN: (纸本)9781728166315

Machine learning (ML) has been penetrating into our daily life by facilitating many daily applications, e.g., self-driving, cloud gaming, product fault detection and drones. Meanwhile, there is an emerging trend that adopts ML methods into network optimization problems, such as flow classification, traffic engineering, routing, and etc. Conventional ML methods need careful training for a specific application of a given network structure, and the trained model normally cannot be applied to other applications and network structures. In this paper, we aim to design adaptive ML methods for network optimization problems, with the trained models having the ability of being deployed to any similar problems. In particular, we consider the virtualized network function (VNF) provisioning problem as our target optimization problem. We first propose a deep Q-learning-based optimization framework for VNF provisioning in a mobile edge network with network capacity constraints, by devising an adaptive graph feature embedding method. We then propose a series of deep Q-learning based learning algorithms for the problems of service chaining and the throughput maximization, based on the proposed learning-based optimization framework. We also propose a novel design of master-slave dual neural network that enables the decisions on both cloudlet selections and routing path finding. To stabilize and accelerate the convergence of the proposed methods, we devise a novel environment generation and termination strategy and a new structure for the replay buffer. We also evaluate the performance of the proposed framework and algorithms by extensive simulations. Results show that the proposed algorithms outperform existing methods by around 12%, and the trained model in a network can be directly adapted to other network structures and settings.

关键词： Delays Optimization Cloud computing Machine learning throughput Adaptive systems Machine learning algorithms

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Improving Learning Algorithm Performance for Spiking Neural networks

Improving Learning Algorithm Performance for Spiking Neural ...

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2017 17th IEEE international conference on communication Technology (ICCT 2017)

作者： Qiang Fu Yuling Luo Junxiu Liu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

this paper proposes three methods to improve the learning algorithm for spiking neural networks（SNNs）. the aim is to improve learning performance in SNNs where neurons are allowed to fire multiple times. the performance is analyzed based on the convergence rate, the concussion condition in the training period and the error between actual output and desired output. the exclusive-or（XOR） and Wisconsin breast cancer（WBC） classification tasks are employed to validate the proposed optimized methods. Experimental results demonstrate that compared to original learning algorithm, all three methods have less iterations, higher accuracy, and more stable in the training period.

关键词： spiking neural network optimization method learning performance

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