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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Cloud-Connected Wearable Devices for Lone Worker Protection With Wireless Sensor Networks 4

Cloud-Connected Wearable Devices for Lone Worker Protection ...

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4th international conference on Sustainable Expert systems, ICSES 2024

作者： Mohankumar, N. Manivannan, K.K. Babu, A. Ramesh Sampoornam, M. Maria Kovarasan, Rajesh Kambattan Srinivasan, C. Nagpur Campus Maharashtra Pune India Kcg College of Technology Department of Mechatronics Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Various sectors continue to prioritize lone worker safety, which requires creative ways to provide quick support and monitoring. To safeguard lone workers, this paper introduces a new method that makes use of cloud-connected wearable devices that are coupled with Wireless Sensor Networks (WSNs). The system design relies on sensors embedded in wearable devices that can monitor a range of environmental elements as well as the vital signs of workers. A central cloud platform receives data in real-time via wireless communication from these devices and processes and analyzes it. The system may detect possible dangers or crises using smart techniques and immediately notify those who are supposed to react or the proper authorities. Cloud computing also makes it easy to store, analyze, and monitor large amounts of data over time, which is great for doing proactive risk assessments and improving safety standards. It demonstrates solution's capacity to improve lone worker safety, reduce reaction times, and mitigate hazards via simulations and actual deployment scenarios, efficacy and dependability. This growing body of knowledge on how to protect lone workers in various workplaces by using the power of IoT and cloud computing. © 2024 ieee.

关键词： Cloud platforms

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An integrated approach of Genetic Algorithm and Machine Learning for generation of Worst-Case Data for real-time systems 26

An integrated approach of Genetic Algorithm and Machine Lear...

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26th ieee/ACM international Symposium on Distributed Simulation and real time applications (DS-RT)

作者： Kumar, Vikash Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (数字)9781665497992

ISBN: (纸本)9781665497992

Determining Worst-Case Execution time (WCET) is essential for temporal verification of real-time and embedded systems. These systems are designed to meet the stringent timing constraints imposed by the regulations. If a system gets delayed due to non-compliance with the deadline, it will lead to disastrous events. Worst-Case Data which gives maximum execution time, plays a vital role in the estimation of WCET. An evolutionary algorithm such as the Genetic Algorithm has been employed to generate the Worst-Case Data. The complexity of an evolutionary algorithm requires the use of several computational resources. This paper presents a novel method to replace the hardware and simulator used in the evolution process with machine learning models. This method reduces the overall time required to generate Worst-Case Data. Different machine learning models are trained to integrate with genetic algorithms. Our machine learning models are created using the Pygad Framework. The feasibility of the proposed approach is validated using benchmarks from different domains. The results show the speedup in the generation of Worst-Case Data.

关键词： embedded System Genetic Algorithm Machine Learning real-time System

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CLEAR: An Efficient Traffic Sign Recognition Technique for Cyber-Physical Transportation systems 15

CLEAR: An Efficient Traffic Sign Recognition Technique for C...

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ieee/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, applications and Technologies (BDCAT)

作者： Rai, Meghna Khosla, Bhawna Dhawan, Yash Kharotia, Himanshi Kumar, Neeraj Bandi, Ajay Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India Northwest Missouri State Univ Maryville MO USA

ISBN: (纸本)9781665460873

In this modern era, the popularity of autonomous systems has increased manifold because they are replacing people in various jobs and are expected to become the backbone of modern society. Specifically, self-driving vehicles and robots are gaining popularity in a wide range of applications. However, traffic sign detection, a critical component of intelligent transportation systems, remains quite challenging since it needs to be done quickly, with high precision, and with high dependability. A fast, real-time, robust automatic traffic sign detection and recognition can support and relieve the driver and significantly increase driving safety and comfort. Out of many algorithms and frameworks available for traffic sign identification, i.e., object detection, one of the most popular ones is You Only Look Once (YOLO) since it provides accurate results with minimal background errors in most real-time processing tasks and has excellent learning capabilities. Motivated by this, this research article provides a novel framework, CLEAR, for traffic sign identification in adverse climate conditions using YOLOv5. We compare different models' speed, accuracy, and other metrics on a Traffic Sign Dataset obtained from the Open Image Dataset V6. Experimental results demonstrated that the proposed CLEAR model achieved the best performance with a Mean Average Precision of 0.73392 and Recall of 0.74194 compared to the existing schemes.

关键词： YOLO Traffic Signs Autonomous Vehicles Object Detection Image Classification Computer Vision Deep Learning real-time Detection

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Impact of AI-based predictive analytics on demand forecasting in ERP systems: A Systematic Literature Review

Impact of AI-based predictive analytics on demand forecastin...

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2024 international Research conference on Smart computing and systems Engineering, SCSE 2024

作者： Fathima, Fazaal Inparaj, Rishani Thuvarakan, Dushyanthan Wickramarachchi, Ruwan Fernando, Ishenka University of Kelaniya Department of Industrial Management Kelaniya Sri Lanka

ISBN: (纸本)9798350375688

Artificial intelligence (AI) has revolutionized demand forecasting within Enterprise Resource Planning (ERP) systems, offering a powerful tool to enhance accuracy and efficiency in predicting future demand patterns. This literature review explores the impact of AI-based predictive analytics on demand forecasting in ERP systems by synthesizing and analyzing existing research. This paper provides a comprehensive examination of the transformative effects of AI-driven demand forecasting across diverse industries, including fashion retail, biopharmaceuticals, energy management, and transportation. We highlight the unique benefits and applications of AI-driven demand forecasting, such as anticipating customer needs, optimizing inventory levels, and making data-driven decisions, ultimately leading to a competitive edge in the marketplace. Our study emphasizes the importance of AI integration into ERP systems for businesses seeking to enhance decision-making and achieve organizational success in today's dynamic and competitive business landscape. By providing valuable insights and showcasing significant improvements in forecasting accuracy, real-time insights, supply chain efficiency, and risk management facilitated by AI-based predictive analytics, this research contributes to advancing knowledge in the field and offers practical guidance for businesses and researchers alike. © 2024 ieee.

关键词： Predictive analytics

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Design and Implementation of a Mobile Infant Care Monitoring System Based on Virtual Simulation Technology of Computer Service Programs

Design and Implementation of a Mobile Infant Care Monitoring...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Jun, Guobin School of Children's Welfare Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the changes in modern families and the needs of infants, traditional parenting methods are no longer able to meet the needs of parents. In order to provide a more advanced and convenient parenting method, this study designed and implemented a mobile parenting monitoring system based on virtual simulation technology of computer service programs. This system utilized virtual simulation technology, combined with mobile devices and cloud computing technology, to provide real-time remote monitoring and intelligent childcare services, providing greater convenience for parents and comprehensive care for infants. The real-time monitoring range of the mobile childcare monitoring system designed in this article was expanded to 100 meters compared to the traditional one, with the number of cameras increasing from 1 to 4, and the storage capacity also expanding to 50GB. The feasibility of the system also had advantages in terms of technical proficiency, economic cost, time cost, market competitiveness, and operational difficulty. The average score in terms of system stability was 85.71, which was also much higher than traditional childcare monitoring systems. The mobile childcare monitoring system designed in this article was significantly superior to traditional childcare monitoring systems and had broad application prospects. © 2025 SPIE.

关键词： Mobile applications

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Improving Fog computing Infrastructure with Qos-Aware Resource Provisioning 4

Improving Fog Computing Infrastructure with Qos-Aware Resour...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhabliya, Dharmesh Kuppuraj, T. Ojha, Varun Primmia, D.R. Sharma, Divya Mathur, Sanjiv Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India

ISBN: (纸本)9798331518981

In today's internet-enabled world, it's possible for everything to link to anything else. Because of this, a new way of working known as the Global Internet of Things has emerged, and it has begun to produce massive amounts of data. While it may be difficult to glean insights from streaming data, doing so has the potential to unleash technological marvels that will benefit society at large. It is critical to have a system in place to store, process, and forecast the data. Another active field of study, infrastructure development has seen a shift from centralised server systems to distributed cloud systems and is continuing to do so. There is a lot of room for investigation into the cloud's shortcomings in processing streaming data and making decisions in near real-time. Now that we have a network, the next step is to find a way to make it more useful and to distribute the load more evenly across the nodes via efficient routing. So that the network doesn't fail suddenly from too much traffic, the allocation is based on the present node load and the total network load. A balanced load on each node in the network is another result of optimised route selection. We built and tested a simple RL algorithm for an insignificant network that leverages the Q-Table. To get around the Q-Table's high level of complexity, an RL technique based on neural networks was suggested, which performs better on big networks. Over all time periods, the Proposed Deep RL technique outperforms Open Shortest Path First (OSPF) and Deep Neural Networks (DNN), achieving an accuracy of almost 92% in minimising average latency. In real-time applications, when minimising latency is crucial, its improved efficiency is evident. © 2024 ieee.

关键词： Fog computing

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When IoT Meet LLMs: applications and Challenges

When IoT Meet LLMs: Applications and Challenges

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2024 ieee international conference on Big Data, BigData 2024

作者： Kök, Ibrahim Demirci, Orhan Özdemir, Suat Ankara University Department of Ai and Data Engineering Ankara Turkey Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles of LLMs in IoT, with a focus on their reasoning capabilities. We show how LLM-IoT integration can facilitate advanced decision making and contextual understanding in a variety of IoT scenarios. Furthermore, we explore the integration of LLMs with edge, fog, and cloud computing paradigms, and show how this synergy can optimize resource utilization, enhance real-time processing, and provide scalable solutions for complex IoT applications. To the best of our knowledge, this is the first comprehensive study covering IoT-LLM integration between edge, fog, and cloud systems. Additionally, we propose a novel system model for industrial IoT applications that leverages LLM-based collective intelligence to enable predictive maintenance and condition monitoring. Finally, we highlight key challenges and open issues that provide insights for future research in the field of LLM-IoT integration. © 2024 ieee.

关键词： Predictive maintenance

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Demand Characterization of CPS with Conditionally-Enabled Sensors 27

Demand Characterization of CPS with Conditionally-Enabled Se...

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27th ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Willcock, Aaron Fisher, Nathan Chantem, Thidapat Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Virginia Tech Dept Elect & Comp Engn Arlington VA USA

ISBN: (纸本)9781665441889

Characterizing computational demand of Cyber-Physical systems (CPS) is critical for guaranteeing that multiple hard real-time tasks may be scheduled on shared resources without missing deadlines. In a CPS involving repetition such as industrial automation systems found in chemical process control or robotic manufacturing, sensors and actuators used as part of the industrial process may be conditionally enabled (and disabled) as a sequence of repeated steps is executed. In robotic manufacturing, for example, these steps may be the movement of a robotic arm through some trajectories followed by activation of end-effector sensors and actuators at the end of each completed motion. The conditional enabling of sensors and actuators produces a sequence of Monotonically Ascending Execution times (MAE) with lower WCET when the sensors are disabled and higher WCET when enabled. Since these systems may have several predefined steps to follow before repeating the entire sequence each unique step may result in several consecutive sequences of MAE. The repetition of these unique sequences of MAE result in a repeating WCET sequence. In the absence of an efficient demand characterization technique for repeating WCET sequences composed of subsequences with monotonically increasing execution time, this work proposes a new task model to describe the behavior of real-world systems which generate large repeating WCET sequences with subsequences of monotonically increasing execution times. In comparison to the most applicable current model, the Generalized Multiframe model (GMF), an empirically and theoretically faster method for characterizing the demand is provided. The demand characterization algorithm is evaluated through a case study of a robotic arm and simulation of 10,000 randomly generated tasks where, on average, the proposed approach is 231 and 179 times faster than the state-of-the-art in the case study and simulation respectively.

关键词： real-time systems control systems cyberphysical systems

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Cloud Simulator Search Framework The Vigilant Fetcher 9

Cloud Simulator Search Framework The Vigilant Fetcher

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9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Kumar, Vishal Chandigarh University Department of Computer Applications Gharuan Mohali India

ISBN: (纸本)9798350397376

Availing cloud computing services is the need of time. In contrast to being sold as a product, pooled resources, software, and data are made available to users as a network-based utility in cloud computing. real-world experimentation is expensive and challenging to conduct. As a result, a virtual cloud environment that mimics the real cloud was required. As a result, a lot of effort is put into creating cloud modeling frameworks that only include a small portion of the various components. In order to choose the best tool, it is necessary to thoroughly compare the available simulators based on a number of criteria. Large number of simulators available online. Manual simulator selection that meets all the user requirements is complex and time-consuming process. In this post, a framework will be introduced that enables researchers to quickly and easily choose the best simulator for their purposes. The best tool selection advice is then provided for researchers, cloud environment providers, and managers by offering with the simulator list, best matches with their requirements. Several scenarios are utilized effectively to evaluate the commonly used simulator's performance. The remaining issues and potential challenges for future study are then discussed. © 2023 ieee.

关键词： Cloud computing

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A Machine Learning Approached Model to Identify the Object for Visually Impaired Person 3

A Machine Learning Approached Model to Identify the Object f...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Joshi, Sunita Gupta, Neha Mitali Yadav, Gautam Manav Rachna International Institute of Research Studies Faculty of Computer Applications Faridabad India

ISBN: (纸本)9798350322842

According to the World Health Organization (WHO), 253 million individuals worldwide are visually impaired, including 36 million who are blind and 217 million who have moderate to severe vision impairment. The objective of this study is to demonstrate an improved approach through a real-time working model that is used for the prediction of objects around a visually impaired person. The proposed model is based on object detection which aids in several aspects of object prediction such as accident reduction and managing daily routines while protecting themselves from hazards or obstacles. The proposed model has an accuracy of 72%. More datasets will be embedded in the future to conduct large-scale object prediction with greater accuracy. © 2023 ieee.

关键词： Object detection

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