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Health Monitoring of Structural and Biological Systems XVI 2022

作者： Prasad, Sneha Kumar, David Kalra, Sumit Chiang, Chih-Hung Khandelwal, Arpit Interdisciplinary Research Platform Internet of Things and Applications Indian Institute of Technology Jodhpur Jodhpur342030 India Department of Aeronautical Engineering Center for NDT Chaoyang University of Technology Taichung413310 Taiwan Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Jodhpur342030 India Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur342030 India

ISBN: (数字)9781510649729

ISBN: (纸本)9781510649712

An automatic lightweight feature detection algorithm is developed to perform real-time structural health monitoring (SHM) of large structures. The algorithm works on the specified region of interest (ROI) and applies canny edge detection with k-means clustering for identifying the displaced pixel location in an image sequence. The location of detected edges (white pixels) in the selected ROI is first validated and then given as input to the k-means clustering algorithm for centroid calculation. The pixel movement tracing method is validated by image simulation, indoor digital micrometer experiment and then an outdoor field experiment on wind turbine. The image simulation experiment was performed to generate sample data and ground truth values. In this experiment, the algorithm was able to detect the defined pixel translations. With this validation, other two experiments were conducted. The indoor experiment was implemented for experimental verification where it successfully identifies the moving bar's 20mm displacement. Likewise, it also accurately measures the natural frequency of the tower of a utility-scale wind turbine. Hence, the algorithm was built on parallel processing with multi-ROI selection to optimize the space and time complexity for real-time vibration analysis. The present study proclaims that the developed algorithm can be used to perform real-time SHM of large-scale structures. © 2022 SPIE

关键词： Pixels

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Anomize: Better Open Vocabulary Video Anomaly Detection

arXiv

引用

arXiv 2025年

作者： Li, Fei Liu, Wenxuan Chen, Jingjing Zhang, Ruixu Wang, Yuran Zhong, Xian Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China

Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. To address these challenges, we explore supplementary information from multiple sources to mitigate detection ambiguity by leveraging multiple levels of visual data alongside matching textual information. Furthermore, we propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. The resulting Anomize framework effectively tackles these issues, achieving superior performance on UCF-CRIME and XD-VIOLENCE datasets, demonstrating its effectiveness in OVVAD. Copyright © 2025, The Authors. All rights reserved.

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Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in internet of Vehicles

arXiv

引用

arXiv 2024年

作者： Zhang, Cui Zhang, Wenjun Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. The School of Internet of Things Engineering Wuxi Institute of Technology Wuxi214122 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China The Department of Electronic Engineering Shanghai JiaoTong University Shanghai200240 China Hong Kong

The internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Information leakage

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Design and Development of an Emulation Model for VPN and VPN Bonding

Design and Development of an Emulation Model for VPN and VPN...

引用

IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Kaustubh Sharma Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Virtual Private Networks (VPNs) have become indispensable for organizations seeking secure remote network access, with a significant rise in their adoption. While the COVID-19 pandemic initially fueled the surge in remote work and VPN usage, the trend has continued post-pandemic as organizations increasingly opt for hybrid work models. VPNs allow users to establish secure connections to their organization’s network from any location, ensuring the confidentiality of transmitted data. Moreover, VPN bonding, which combines multiple VPN connections into a unified interface, improves performance and reliability, particularly in areas with limited internet connectivity. As the usage of VPN and VPN bonding technologies continues to expand, a growing demand arises for research in this field, and consequently, the need for robust emulators and simulators. However, existing network emulators or simulators currently lack comprehensive support for VPN-related technologies. To address these limitations, this paper aims to develop intuitive and user-friendly Application Programming Interfaces (APIs) for emulating VPN and VPN bonding in Network Stack Tester (NeST), a powerful Python package designed to facilitate network emulation for experienced researchers and individuals new to the field. This work utilizes OpenVPN, a secure tunneling daemon, to integrate the support of VPN emulation in NeST. By augmenting the capabilities of NeST, this work intends to provide researchers with a valuable toolset for exploring and testing VPN and VPN bonding technologies across diverse network environments.

关键词： Emulation Organizations Tunneling Remote working Virtual private networks Reliability Bonding

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Enabling Support for TLS and TLS Fingerprinting in Network Stack Tester

Enabling Support for TLS and TLS Fingerprinting in Network S...

引用

IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Nitin Dabi Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Transport Layer Security (TLS) is the standard protocol widely used for securing client-server communications over the internet. It ensures the confidentiality and integrity of data transmitted between systems. With the rising deployment of TLS 1.3, which is the latest version of the protocol, stronger encryption and improved security features are introduced. The adoption of TLS 1.3 brings about the need for techniques such as TLS fingerprinting. TLS fingerprinting involves analyzing the unique characteristics of TLS handshakes to identify and classify different implementations or configurations of TLS. However, researchers currently lack a dedicated platform to test TLS and TLS fingerprinting techniques in a controlled environment. To address this gap, we aim to enhance NeST (Network Stack Tester) by adding support for TLS and TLS fingerprinting. NeST is a Python tool for network emulation that internally uses Linux network namespaces and Linux utilities to create virtual testbeds, perform experiments using various network protocols and collect statistics. By integrating TLS and TLS fingerprinting into NeST, we provide an easy-to-use tool that enables researchers to experiment with TLS, analyze TLS traffic, and evaluate the effectiveness of TLS fingerprinting techniques.

关键词： Protocols Linux Emulation Fingerprint recognition Communications technology internet Encryption

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Metaverse for Education: Developments, Challenges, and Future Direction

引用

computer Applications in engineering Education 2025年第3期33卷

作者： Chamola, Vinay Peelam, Mritunjay Shall Mittal, Uday Hassija, Vikas Singh, Anushka Pareek, Ritawari Mangal, Poorvi Sangwan, Devika de Albuquerque, Victor Hugo C. Mahmud, Mufti Brown, David J. Department of Electrical and Electronics Engineering Internet of Things Research Group APPCAIR BITS-Pilani Rajasthan Pilani India Department of Electrical and Electronics Engineering BITS-Pilani Rajasthan Pilani India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Humanities and Social Sciences BITS-Pilani Rajasthan Pilani India Fortaleza Brazil Department of Computer Science Computing and Informatics Research Centre Medical Technologies Innovation Facility at Nottingham Trent University Clifton Lane Nottingham United Kingdom Department of Computer Science Computing and Informatics Research Centre at Nottingham Trent University Clifton Lane Nottingham United Kingdom

The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement. © 2025 Wiley Periodicals LLC.

关键词： Contrastive Learning

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Traffic Steering in Large-scale Public Cloud 22

Traffic Steering in Large-scale Public Cloud

引用

Proceedings of the 8th International Conference on Communication and Information Processing

作者： Zhangfeng Hu Siqing Sun Ping Yin Yanjun Li Qiuzheng Ren Baozhu Li Xiong Li Department of Cloud Platform Research and Development Inpur Cloud China Internet of Things & Smart City Innovation Platform Zhuhai Fudan Innovation Institute China School of Computer Science and Engineering University of Electronic Science and Technology China

ISBN: (纸本)9781450397100

More and more complex services composed of a series of sequentially arranged middleboxes which are mainly used to meet the requirements of advanced services such as security services, auditing services, monitoring services, personalized enterprise services, and so forth, are increasingly deployed in cloud data centers of public cloud. SFC (Service Function Chaining) is a technique that facilitates the enforcement of complex services and differentiated traffic forwarding policies, dynamically steering the traffic through an ordered list of service functions. Flow table-based traffic steering scheme is commonly adopted in SDN-enabled scenarios, which consumes too many flow entries and is unsuitable for large-scale public clouds in steering traffic between VNFs (Virtual Network Function) inside of VPC (Virtual Private Cloud). Legacy PBR (Policy-based Routing) based schemes which are widely used in traditional physical networks cannot fulfill the requirements of fully distributed routing architectures of large-scale public clouds. In this paper, we present a PBR and unsymmetrical NAT (Network Address Translation) converged scheme to structure SFC in a fully distributed routing architecture. The scheme uses distributed PBR rules to steer traffic between an ordered list of VNFs located on different nodes while performing NAT on different nodes for ingress/egress traffic of a specific flow to avoid asymmetry of packet headers which may lead to failures of communication. The proposed scheme brings no overhead in data transmission, eliminates extra configurations on each middle box of the chain, and is scalable to support the scenarios of large-scale public cloud.

关键词： policy-based routing traffic steering SFC unsymmetrical NAT

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Energy-Latency Aware Intelligent Reflecting Surface Aided Multi-cell Mobile Edge Computing

arXiv

引用

arXiv 2023年

作者： Xu, Wenhan Yu, Jiadong Wu, Yuan Tsang, Danny H.K. The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Guangdong Guangzhou511400 China The State Key Lab of Internet of Things for Smart City China The Department of Computer and Information Science The University of Macau China

The explosive development of the internet of things (IoT) has led to increased interest in mobile edge computing (MEC), which provides computational resources at network edges to accommodate computation-intensive and latency-sensitive applications. Intelligent reflecting surfaces (IRSs) have gained attention as a solution to overcome blockage problems during the offloading uplink transmission in MEC systems. This paper explores IRS-aided multi-cell networks that enable servers to serve neighboring cells and cooperate to handle resource exhaustion. We aim to minimize the joint energy and latency cost, by jointly optimizing computation tasks, edge computing resources, user beamforming, and IRS phase shifts. The problem is decomposed into two subproblems—the MEC subproblem and the IRS communication subproblem—using the block coordinate descent (BCD) technique. The MEC subproblem is reformulated as a nonconvex quadratic constrained problem (QCP), while the IRS communication subproblem is transformed into a weight-sum-rate problem with auxiliary variables. We propose an efficient algorithm to iteratively optimize MEC resources and IRS communication until convergence. Numerical results show that our algorithm outperforms benchmarks and that multi-cell MEC systems achieve additional performance gains when supported by IRS. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing

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Wearable-based behaviour interpolation for semi-supervised human activity recognition

arXiv

引用

arXiv 2024年

作者： Duan, Haoran Wang, Shidong Ojha, Varun Wang, Shizheng Huang, Yawen Long, Yang Ranjan, Rajiv Zheng, Yefeng Department of Computer Science Durham University United Kingdom School of Engineering Newcastle University United Kingdom School of Computing Newcastle University United Kingdom Jarvis Research Center Tencent YouTu Lab China Chinese Academy of Sciences R&D Center for Internet of Things China

While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-and-error process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep learning-based HAR requires a large amount of labelled data and extracting HAR features from unlabelled data for effective deep learning training remains challenging. We, therefore, introduce a deep semi-supervised HAR approach, MixHAR, which concurrently uses labelled and unlabelled activities. Our MixHAR employs a linear interpolation mechanism to blend labelled and unlabelled activities while addressing both inter- and intra-activity variability. A unique challenge identified is the activity-intrusion problem during mixing, for which we propose a mixing calibration mechanism to mitigate it in the feature embedding space. Additionally, we rigorously explored and evaluated the five conventional/popular deep semi-supervised technologies on HAR, acting as the benchmark of deep semi-supervised HAR. Our results demonstrate that MixHAR significantly improves performance, underscoring the potential of deep semi-supervised techniques in HAR. © 2024, CC BY.

关键词： Time series analysis

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Predictable bandwidth slicing with open vSwitch

arXiv

引用

arXiv 2021年

作者： Chen, Jesse Dezfouli, Behnam Internet of Things Research Lab Department of Computer Science and Engineering Santa Clara University United States

Software switching, a.k.a virtual switching, plays a vital role in network virtualization and network function virtualization, enhances configurability, and reduces deployment and operational costs. Software switching also facilitates the development of edge and fog computing networks by allowing the use of commodity hardware for both data processing and packet switching. Despite these benefits, characterizing and ensuring deterministic performance with software switches is harder, compared to physical switching appliances. In particular, achieving deterministic performance is essential to adopt software switching in mission-critical applications, especially those deployed in edge and fog computing architectures. In this paper, we study the impact of switch configurations on bandwidth slicing and predictable packet latency. We demonstrate that latency and predictability are dependent on the implementation of the bandwidth slicing mechanism and that the packet schedulers used in OVS Kernel-Path and OVS-DPDK each focus on different aspects of switching performance. © 2021, CC BY.

关键词： Fog computing

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