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Improving Transfer time Prediction of ML Models via Auto-correcting Dynamical systems Modeling 10

Improving Transfer Time Prediction of ML Models via Auto-cor...

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10th international conference on Network Softwarization (NetSoft)

作者： Karanam, Venkat Sai Suman Lamba Ramamurthy, Byrav Univ Nebraska Lincoln Sch Comp Lincoln NE 68588 USA

ISBN: (纸本)9798350369588;9798350369595

Machine Learning (ML) is extensively used for predicting transfer times for general purpose Wide Area Networks (WANs) or public Internet applications, but for Research and Education Networks (RENs) two major gaps exist in literature. First, RENs i.e. networks carrying large data flows have received limited attention by the networking community. RENs behave differently compared to the general purpose Internet applications and other network types. Hence, ML models from other network types cannot be used interchangeably for large data transfers. Second, the ML models are used as blackboxes to train on measured network values and then used to predict transfer times or other runtime network parameters. In this paper, we present a dynamical systems model of the large data transfers typical of RENs in the form of a system of Ordinary Differential Equations (ODEs) inspired by the Lotka-Volterra competition model. We present a transfer time prediction component called Dynamic Transfer time Predictor (DTTP) which solves the ODEs and predicts the future transfer times. Second we formulate a loss function based on Lyapunov function called Lyapunov Drift Correction (LDC) that self-corrects the transfer time prediction errors dynamically. To design and develop our model, we studied real-world datasets consisting of over 100 million transfer records collected from platforms such as Open Science Grid (OSG), Large Hadron Collider Optical Private Network (LHCOPN), Worldwide LHC Grid (WLCG), as well as the RENs of Internet2 and ESNet. We integrate our model into well-known neural network models and regressors and present evaluation results.

关键词： Lyapunov functions

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Mapping method of embedded system based on multi-sensor data fusion for unstructured terrain 5

Mapping method of embedded system based on multi-sensor data...

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5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Wang, Yufei Wang, Zhiming Shanghai University School of Mechatronic Engineering and Automation Shanghai China

ISBN: (纸本)9798350357738

Simultaneous localization and mapping, as the basic methods for autonomous motion of mobile robots, have been widely developed and applied in conventional structured environments. But it is more difficult to collect and process data in unstructured environments. The elevation map provides an efficient, simple and powerful geometric representation of terrain for ground robots. Robots can use this information for navigation, path planning and motion control in uncertain environments. However, a single sensor provides less information about the environment. At the same time, for embedded systems with limited computational power, their real-time performance will be affected. In order to solve the above problems, this paper proposes an improved elevation mapping method based on multi-sensor data fusion. More informative elevation maps are obtained using sensor data from multiple sources, while processing is accelerated by GPU. We demonstrate the optimization performance of the proposed method with simulated data sets and real terrain experiments, and compare the reconstructed elevation map with the real ground, proving that the proposed method can be used for autonomous navigation of mobile robots. © 2023 ieee.

关键词： embedded systems

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Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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4th international conference on Soft computing for Security applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-time traffic sensor data. timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 ieee.

关键词： Random forests

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Enhancing the Efficiency of Healthcare Facilities Management with Digital Twins

Enhancing the Efficiency of Healthcare Facilities Management...

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international conference on Smart applications, Communications and Networking (SmartNets)

作者： Al-Jaroodi, Jameela Mohamed, Nader Robert Morris Univ Dept Engn Pittsburgh PA 15108 USA Penn Western Univ Dept Comp & Engn Technol California PA USA

ISBN: (纸本)9798350385328;9798350385335

Using digital twins for the healthcare industry has picked up pace with the various advances in technologies and capabilities that support creating digital twins and with the increased awareness of digital twins benefits for the industry. At a more specialized level, healthcare facilities management is an important and very critical component contributing to the effective and efficient planning and use of the facilities. Some work has been going on exploring how digital twins can be applied in some areas of healthcare facilities management such as maintenance and building management. However, there is very little exploring the use of digital twins in other areas of healthcare facilities management such as capacity planning, disaster recovery planning and facilities and resource scheduling. These areas can benefit tremendously from digital twins. In this paper we discuss the different aspects of healthcare facilities management where digital twins can be of great benefit. In addition, we show how using the digital twins and their accurate representations of the real healthcare systems can be integrated with other advanced tools like simulation, analytics and visualization to create more value-added capabilities to help enhance healthcare facilities management processes and outcomes. In particular, we believe that using the digital twin models and real-time operational data can be used as inputs for various simulation processes to help in the decision-making processes and to make better and more informed decisions regarding the various activities in healthcare facilities management.

关键词： Healthcare systems Digital Twins Healthcare Facilities Management Visualization systems Simulation systems Modeling

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Utilizing Blockchain Technology for Financial Services in Parallel, Distributed, and Grid computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

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2024 international conference on computing and Intelligent reality Technologies, ICCIRT 2024

作者： Chokkamreddy, Prakash Palagati, Anusha Rao, Akurathi Lakshmi Pathi Maheswari, P. Mahadevan, Vanitha Balan, Santhosh Kumar School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798331510299

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. Exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector. © 2024 ieee.

关键词： Smart contract

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Multi-Objective Optimization for an Online Re-Planning of Autonomous Vehicles 53

Multi-Objective Optimization for an Online Re-Planning of Au...

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53rd Annual ieee/IFIP international conference on Dependable systems and Networks (DSN)

作者： Le Gall, Kilian Lemarchand, Laurent Dezan, Catherine Lab STICC UBO Brest France

ISBN: (纸本)9798350325430

Autonomous vehicles are well-known for automated tasks that are difficult or dangerous to be performed by human. However, the environment in which those Autonomous Vehicle (AV) are evolving is generally hard to predict. Thus, the challenge is to achieve a predefined mission while adapting AVs to their shifting environment in real time as efficiently as possible. The mission often includes path planning problems, where self-adaptation to terrain modifications is required while maintaining contradictory objectives, such as safety, risk assessment, travelling time or distance or consumed energy. We choose to focus on supervision missions (covering area with a lidar, with pictures, searching, etc) with two objectives: travelled distance (that could later be modeled into time or energy consumption) and covered area. We propose a multi-objective optimization (MOO) framework for a self adaptation of autonomous vehicles, with an offline/online approach, in order to solve covering/monitoring missions. The offline process will predict a initial path for the AV and the online process will be useful for the dynamic path re-planning when obstacles are detected. Our results demonstrate the benefits of reusing the offline pre-computed solutions for the online phase and for dynamic path re-planning.

关键词： drone/AV path planning embedded decision making obstacle avoidance

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Testing of real time embedded Onboard Software : a Launch Vehicle Case Study 14

Testing of Real time Embedded Onboard Software : a Launch Ve...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Krishnan, Ranjani Manju, C.R. Jayalekshmy, L. Avionics Vikram Sarabhai Space Centre Trivandrum India

ISBN: (纸本)9798350335095

The avionics system in safety critical aerospace systems plays an extremely important role in ensuring proper performance as well as safety of the mission. The Onboard Computer (OBC) that functions as the master in the avionics bus in launch vehicles is based on an indigenous processor. The development and testing of the real time Executive (RTE) software for the OBC, satisfying all requirements is a daunting task. This work describes the testing techniques employed for the RTE software in the reusable launch vehicle. Various dynamic testing techniques like unit testing, integration testing and system testing were employed to validate the OBC software. Validations were also carried out for the entire system utilizing different test beds, where all the subsystems are connected. In addition, the software was subjected to code inspection and rigorous reviews which constitutes static testing. The results of testing the onboard software are also elaborated. Subsequent to detailed analysis of all test results and incorporation of necessary corrections, the software was cleared for final flight use and its performance was normal in flight. © 2023 ieee.

关键词： Avionics

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Fall detection paradigm for embedded devices based on YOLOv8

Fall detection paradigm for embedded devices based on YOLOv8

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2023 ieee international conference on Imaging systems and Techniques, IST 2023

作者： Moutsis, Stavros N. Tsintotas, Konstantinos A. Kansizoglou, Ioannis An, Shan Aloimonos, Yiannis Gasteratos, Antonios Democritus University of Thrace Department of Production and Management Engineering XanthiGR-671 32 Greece JD Health International Inc Beijing100176 China University of Maryland Department of Computer Science College ParkMD20742 United States

ISBN: (纸本)9798350330830

Human-action recognition attempts to determine which deed occurs by people. Owing to the wide spectrum of human activities, action recognition spans a wide range. Among all, falls the conduct gathering special interest, especially for the elderly, as 37.3 million accidents happen annually, with 1.83% being fatal. Over the years, several sensors, data types, and classification techniques have been explored to cope with this problem. Nowadays, ambient assisting living environments employ cameras and companion robots to monitor human activity. The need for low computing power platforms in such environments is essential. To this end, we propose here a lightweight fall detection pipeline for processing sequences-of-images in real-time, which is deployed on an embedded device, namely the Raspberry Pi 4. The proposed system is built upon YOLOv8, having been trained to locate people on the well-established COCO dataset, and a binary classification support vector machine trained on E-FDPS. Through an extensive experimental protocol on LE2I, we show that high detection accuracy and fast timing capabilities are achieved. Lastly, aiming to facilitate the community, our framework is open-sourced at https://***/smoutsis/real-time-fall-detection. © 2023 ieee.

关键词： Fall detection

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Quantum-Safe Authentication Protocol Using Post-Quantum Key Encapsulation Mechanism for Transportation systems 4

Quantum-Safe Authentication Protocol Using Post-Quantum Key ...

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4th ieee international conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Parameswarath, Rohini Poolat Sikdar, Biplab College of Design and Engineering National University of Singapore Department of Ece Singapore

ISBN: (纸本)9798350383997

The integration of Internet of Things (IoT) technology into transportation systems holds immense potential to transform the way we move people and goods, offering benefits such as increased efficiency, reduced emissions, improved safety, and enhanced user experience. With the ubiquitous and high-speed connectivity, sixth-generation (6G) technology is envisaged to make unprecedented revolutions in several fields and applications including IoT applications and transportation systems. The convergence of 6G networks with these applications will provide seamless connectivity and help to integrate functionalities such as sensing, communication, and computing, resulting in superior service quality. However, the IoT network is highly vulnerable to cyber-attacks. With the recent developments in quantum computers and quantum algorithms, it is important to make them secure from quantum attacks as well. In this paper, we propose a quantum-safe authentication protocol using a Post-Quantum Key Encapsulation Mechanism (PQKEM) for IoT applications in transportation systems. The protocol is built using CRYSTALS-Kyber, a lattice-based PQKEM, selected by the National Institute of Standards and Technology (NIST) in round 3 of the post-quantum standardization process. We provide a formal security proof of the proposed authentication protocol using the real-Or-Random (RoR) model. We also present a performance analysis of the proposed authentication protocol. © 2024 ieee.

关键词： Authentication Protocol

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LEM: a Tool for Large-Scale Workflow Control in Edge-Based Industry 5.0 applications 19

LEM: a Tool for Large-Scale Workflow Control in Edge-Based I...

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19th Annual international conference on Distributed computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Reis, Rui Santos, Pedro M. Sousa, Mario J. Martins, Nuno Sousa, Joana Almeida, Luis Universidade Do Porto Faculdade de Engenharia Portugal Portugal Cister Research Center in Real-Time and Embedded Computing Systems Portugal

ISBN: (纸本)9798350346497

Edge computing is poised to greatly boost Industry 5.0 by providing a computing substrate that can run highly specialized applications with minimal latency, reducing the requirements on computational resources at the end-user and allowing the sharing of resources between multiple end-users. The intended services and workflows need to be designed, configured and deployed at target edge devices, and during operation their status needs to be monitored. There are several industrial frameworks that provide such functionalities, but often there is little support to execute them simultaneously over many target devices. In this paper we present the LEM tool, a tool that enables the management (monitor, configure, deploy) of workflows in a large number of edge devices. The tool connects to existing industrial frameworks to provide these functionalities, abstracting the low level configuration aspects of each framework and enabling simultaneous configuration of multiple devices, a feature that many frameworks do not support. The tool offers a web-based GUI that allows the infrastructure manager to conveniently check the status of all devices and flows running in each device, deploy new flows on selected devices, and start and stop flows. Experimental measurements show residual response time overhead as the number of devices increases. © 2023 ieee.

关键词： edge Industry5.0 Node-RED workflow

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