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6G in medical robotics: development of network allocation strategies for a telerobotic examination system

international JOURNAL OF computer ASSISTED RADIOLOGY AND SURGERY 2025年第1期20卷 167-178页

作者： Kolb, Sven Madden, Andrew Kroeger, Nicolai Mehmeti, Fidan Jurosch, Franziska Bernhard, Lukas Kellerer, Wolfgang Wilhelm, Dirk Tech Univ Munich TUM Sch Med & Hlth Klinikum Rechts Isar Res Grp MITI Munich Germany Tech Univ Munich Chair Commun Networks TUM Sch Computat Informat & Technol Munich Germany Tech Univ Munich TUM Sch Med & Hlth Dept Surg Klinikum Rechts Isar Munich Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany

PurposeHealthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network *** developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs).ResultsThe results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical *** work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

关键词： 6G Medical robotics Telemedicine Fog computing

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distributed Self-fault Outlier Detection in Wireless Sensor Network Based on Machine Learning 6th

Distributed Self-fault Outlier Detection in Wireless Sensor ...

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6th international Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Padhi, Ruchika Mudulli, Debendra Gouda, Bhabani Sankar C. V. Raman Global University Odisha Bhubaneswar India School of Computer Science NIST Institute of Science and Technology Odisha Brahmapur India

ISBN: (纸本)9789819780891

Due to a wide range of essential applications, resource limitations, and intermittent irregular responses from cluster sensor nodes, different cluster sensor node faults and failures of distributed wireless sensor networks have occurred in recent years. Due to limited calculation capabilities, limited storage space, resource constraints, and a poor wireless communication environment, relatively few studies have been done on distributed fault diagnosis and fault tolerance in wireless sensor networks, making these networks vulnerable to impending security issues and abnormal detection. This chapter presents a thorough investigation into data quality, with a focus on the features of sensor node information with respect to time and space similarity, as well as recent advances in the study of outlier analysis. In this study, we employ classification to identify outliers by predicting the values of faulty sensor nodes using information gathered from their fault-free neighbors. The proposed method, DSFOD, uses the KNN algorithm to diagnose the proximity of a distance and achieves promising results of greater than 0.99. To accomplish the goal of data outlier detection in WSN, the data are analyzed and detected using the DSFOD method. The simulation’s findings demonstrate the network’s efficiency and superior performance at that specific time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor nodes

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international Conference on Applied Innovations in IT, ICAIIT 2024

International Conference on Applied Innovations in IT, ICAII...

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international Conference on Applied Innovations in IT, ICAIIT 2024

ISBN: (纸本)9783031892950

The proceedings contain 37 papers. The special focus in this conference is on Applied Innovations in IT. The topics include: Use of Heterogeneous Special Purpose Telecommunication networks for Provision of Convergent Services;methodology for Choosing the Best Network Topology for the Multiple Objects Network;turing Machine Development for High Protected Remote Control of the IoT Mobile Platform;distributed Multi-agent systems Based on the Mixture of Experts Architecture in the Context of 6G Wireless Technologies;technological Principles for Building a Network Architecture of Service Data Processing Centers;Comparison of Performance and Power Consumption in Sigfox, NB-IoT, and LTE-M;approach to Effective Query Execution;possible Features of Designing Telemedicine networks and Telemedicine Stations;applying Adaptive Learning Techniques for Studying of Installation Process an Operating System on a Personal computer;determination the Conversion Power of the Most Popular Switching Converters for computer systems;achieving Cyber-Physical Consistency for Immersive Robot Controlling;quantifying the Economic Impact of Investment Activities: Methods and Applications;analysis of Modern Solutions for the Identification of Anonymous Users;ontology-Driven Approach to the Structuring of Information Resources Describing a Subject Domain;a Comprehensive Integration of Practical Strategies in DevOps;methods of Spline Functions in Information Technologies;adaptive Clustering for Distribution Parameter Estimation in Technical Diagnostics;semi-Autonomous Object Retrieval with Robot Arm and Depth Camera for Quadruped Robots;power-to-X Strategies: A Key Driver for Decarbonization and Renewable Energy Integration in Economies;Estimation of the Repeater Span Length of OTH Transmission System with QAM Modulation;automated 3D Sign Language Animation Using Machine Learning Algorithms.

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11th international symposium on From Data to Models and Back, DataMod 2023

11th International Symposium on From Data to Models and Back...

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11th international symposium on From Data to Models and Back, DataMod 2023

ISBN: (纸本)9783031872167

The proceedings contain 8 papers. The special focus in this conference is on From Data to Models and Back. The topics include: Extracting Cyber Threat Intelligence from Social Media with Case Studies in Twitter/X and Reddit;attractor and Slicing Analysis of a T Cell Differentiation Model Based on Reaction systems;preliminary Results on Shapley Value Notions and Propagation Methods for Boolean networks;towards a Flexible Approach for Understanding and Comparing Traces;modelling and Verification of an Application for Managing Sensitive Health Data;evaluating Large Language Models and Prompt Variants on the Task of Detecting Cease and Desist Violations in German Online Product Descriptions.

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XMeta-OS: A Conceptual Framework for Unified Resource Management in distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd international Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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ELITE: Efficient and Secure Machine Learning for Intelligent Perception in Smart Road Infrastructure 10th

ELITE: Efficient and Secure Machine Learning for Intellige...

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10th international symposium on Security and Privacy in Social networks and Big Data, SocialSec 2024

作者： Vembu, Swathi Kumar Limbasiya, Trupil Chattopadhyay, Anupam Nanyang Technological University 50 Nanyang Ave Singapore639798 Singapore Desay SV Automotive Singapore Pte Ltd International Business Park Singapore609935 Singapore

ISBN: (纸本)9789819637737

In the realm of Connected and Autonomous Vehicles (CAVs), securing Deep Neural networks (DNNs) poses a critical challenge, particularly in ensuring confidentiality and integrity amidst potential attacks and unauthorized access. This paper presents ELITE, a distributed approach enhancing security and computational efficiency for V2X applications. ELITE partitions DNNs used in Roadside Units (RSUs) between the RSU (edge device) equipped with a Hardware Security Module (HSM) and the cloud, with specific layers processed within a Trusted Execution Environment (TEE). By leveraging public-key encryption protocols, ELITE facilitates secure model distribution and inference. Real-time integrity verification is conducted with the Zymkey HSM at the RSU while selective processing on the AWS Nitro Enclave offers enhanced security. Experimental results show that ELITE reduces inference time by 45.73% compared to solely cloud-based processing while maintaining high accuracy with the correct encryption key. This distributed approach balances security and efficiency, mitigating risks of unauthorized access and preserving model integrity within DNN-enabled V2X roadside services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Blockchain Approach for Analyzing the Issues of IoT systems 5th

Blockchain Approach for Analyzing the Issues of IoT Systems

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5th Doctoral symposium on Computational Intelligence, DoSCI 2024

作者： Rawat, Abhishek Verma, Rajat Suryavanshi, Raghuraj Singh Department of Computer Science and Engineering Pranveer Singh Institute of Technology UttarPradesh Kanpur India

ISBN: (纸本)9789819771776

The Internet of Things combined with blockchain technology has emerged as an innovative platform to better safeguard data, save costs, and pale, achieve strength. Combining blockchain and IoT means that the distributed ledger with sophisticated cryptographic mechanisms, consensus mechanisms, and some other processes may transmit data and information in a secure and open approach among devices across long distances. This paper begins with an introduction to the IoT and blockchain technology platforms before conducting a study of how their merger can help improve the platforms. The paper will additionally emphasize critical criteria before integrating the blockchain in IoT systems. Finally, the paper offers practical examples from blockchain-supported IoT project. Overall, combining blockchain technology into IoT might transform, how perceive and communicate technical advancements, paving the path for a more secure, connected, but decentralized future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： distributed ledger

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Leveraging Deep Learning for Accurate Classification of Leptograpsus Crabs Based on Morphological Measurements 5th

Leveraging Deep Learning for Accurate Classification of Lept...

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5th international symposium on Intelligent Computing systems

作者： Malik, Usman Malik, Mustafa Malik, Asif Christ Church Univ Canterbury England Univ Kent Canterbury England Higher Coll Technol Sharjah U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

This research explores the application of deep learning techniques to classify species and sex of Leptograpsus crabs. The dataset comprised of various morphological measurements, that included the carapace width and length. A convolutional neural network (CNNs) for feature extraction and classification was used. The study achieved 97.5% accuracy for species classification and 95% accuracy for sex classification, with high ROC AUC values (1.0 for species and 0.99 for sex). To ensure model robustness overfitting prevention strategies, such as early stopping and dropout layers, were employed. Additionally, SHAP analysis identified carapace width and carapace length as the most influential features. This research demonstrates the efficacy of advanced AI methodologies in biological classification, contributing to both artificial intelligence and ecological research, with potential applications in biodiversity monitoring and conservation efforts.

关键词： Deep Learning Convolutional Neural networks (CNNs) Ecological Research Crab Species Classification Morphological Measurements

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Hashgraph consensus-based simultaneous, asynchronous and big-data supported blockchain mechanism for IT-OT enabled critical infrastructure sectors

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Journal of Ambient Intelligence and Humanized Computing 2025年 1-13页

作者： Hasan, Mohammad Kamrul Ghazal, Taher M. Issa, Ghassan Islam, Shayla Ismail, Ahmad Fadzil Kabir, S. Rayhan Bangi43600 Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman11937 Jordan School of Computing Skyline University College University City Sharjah Skyline University College Sharjah1797 United Arab Emirates Institue of Computer Science and Digitial Innovation UCSI University Malaysia Kuala Lumpur56000 Malaysia Department of Electrical and Computer Engineering International Islamic University of Malaysia Kuala Lumpur53000 Malaysia

Critical Infrastructure (CI) refers to the essential areas made up of public, private, and business sectors for the security of a country's development, such as electricity, water, health, education, etc. where information technologies (ITs) and operational technologies (OTs) enable automation services of CI. Currently, blockchain is being experimented in various CI sectors and any CI sector accumulates the data of millions of people in a country or nation from which big data is being generated. Big data connections are seen with large-scale blockchain networks because as the size of the blockchain network increases, so does the amount of connected data. Typically, the blockchain does not support asynchronous and simultaneous processes that indicate multiple block compilation does not allow at the same time for the blockchain systems. That is why high energy consumption, high bandwidth dependency, long-time computation, high mining process, and system slowness issues are seen on large-scale blockchain networks where big data is also connected. However, Hashgraph is an alternative distributed ledger technology (DLT) to blockchain and is also considered a consensus method that supports Asynchronous Byzantine Fault Tolerance (ABFT) as well as simultaneous process. Moreover, Hashgraph does not need mining and network node verification processes which saves bandwidth. In this research, we proposed a Hashgraph-enabled blockchain model for reducing the above issues and illustrated the adoption of IT-enabled OT to support CI. In addition, this manuscript discusses how the proposed model can create positive impacts in CI areas by replacing traditional blockchain systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Critical infrastructures

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Visualization of Multilayer networks 4th

Visualization of Multilayer Networks

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4th international Conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Lazzaro, Ilaria Milano, Marianna Department of Medical and Surgical Sciences University "Magna Græcia" Catanzaro88100 Italy Department of Experimental and Clinical Medicine University Magna Græcia Catanzaro88100 Italy

ISBN: (纸本)9783031812460

Network theory, in particular complex networks, has undergone considerable development finding its way into many real-world applications. However, they have several different types of relationships that cannot be represented by a mono layer network [11]. In fact, multilayer networks explicitly incorporate multiple channels, creating the right context to describe interconnected systems through different related layers, where nodes are the entities of the system and the edges represent the interactions between them. The application fields that may be modelled by multilayer networks range from human and social systems [8], to technological and transport systems, including biology and medicine [4]. Although various approaches for the visualization of multilayer networks have been suggested in recent years, this is an evolving field. In this work, we present an overview of the tools exploited for the visualization of multilayer networks. Then, we present a comparison among different tools through a case study represented by a real multilayer network. The network under investigation is a multilayer network composed of 8, 392 nodes and 128, 199 edges distributed over 2 layer that relate disease and drug. We propose methods for visualisation of the network that provide a topological analysis, attempting to derive the main measurement metrics. Through the visualisation carried out on the dataset, we will attempt to demonstrate the different layout configurations that a network can assume and the parameters that can be associated to each tool used in order to derive real scientific information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multilayer neural networks

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