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Self-Aware Fog Layer toward Scalable Resource Allocation and Dynamic Queuing

Self-Aware Fog Layer toward Scalable Resource Allocation and...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Kalingarani G P. Selvaraj Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India

The Internet is overwhelmed with innovative IoT -assisted devices. It is predicted that the number of online-connected devices will be more than 50 billion in 2030. Such IoT devices would need support from enabling technologies to consume less memory and lower the computation cost. The cloud-based services might further increase point-to-point latency. The unprecedentedly high volumes of real-time data generated by IoT devices may suffer from this delay issue. This work proposes a novel cognitive Fog computing-based data processing approach that manages the data influx caused by the sensor devices at the edge router. The proposed cognitive Fog based architecture has empowered edge devices, with the features such as Location awareness, low latency, portability, proximity to end users, diversity, and real-time response. A scalable resource allocation with a dynamic queuing technique was proposed. The simulation results have shown that the proposed architecture boosts the performance of the IoT Fog-based applications more than the existing approaches.

关键词： Cloud computing Computational modeling simulation Dynamic scheduling Real-time systems Internet of Things Resource management

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Internet of Things and Edge Computing for Real Time applications

Internet of Things and Edge Computing for Real Time Applicat...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), international conference

作者： Chandana Sai Thondebhavi Shanthakumar Neha Harish Eshanya Anandi Giridharan California State University Sacramento CA USA Bosch Global Software Technologies Bengaluru India TuringMinds.ai Bengaluru India Indian Institute of Science Bengaluru India

Edge computing is a networking technology focused on placing data processing closer to the data source to reduce latency and bandwidth usage. Edge computing refers to moving some operations from the cloud to more on-premises locations for example edge server, IoT device or a user's Personal Computer. The quantity of long-distance communication required between a client and server is reduced when computing is moved to the edge of the network. Edge computing is a new technique that was created to get over the drawbacks of cloud computing. It has advanced using 5g technology based on locally distributed computing, storage, and control services. The core of fifth-generation (5G) wireless networks and beyond is edge computing. The software used in simulation of the topologies is Cisco Packet Tracer (CPT) which is a multi-platform visual simulation tool created by Cisco Systems that enables users to build network topologies and replicate modern computer networks. A cloud and edge computing topology model are simulated and compared for their working for processing the data, from source to the destination. Mainly focusing on low-latency of the topology. After which real time application models are created and simulated in fields of agriculture for vermicomposting, and city planning considering water logging as a major issue.

关键词： Cloud computing Network topology 5G mobile communication Computational modeling Wireless networks Real-time systems Topology

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Video Deepfake Detection: Current Trends, methodologies, and Future Directions

Video Deepfake Detection: Current Trends, Methodologies, and...

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Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： Ansar H Mathusoothana S Kumar Dept. of Computer Science & Engineering Noorul Islam Centre for Higher Education Tamil Nadu India Department of Information Technology Noorul Islam Centre for Higher Education Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Deepfake technology has emerged as a significant challenge in the domain of multimedia processing, particularly due to its capability to create highly realistic manipulated videos that can mislead viewers and compromise security, privacy, and public trust. While deepfake technologies have legitimate applications in fields such as entertainment, education, and healthcare, their misuse for malicious purposes such as misinformation and identity fraud necessitate the development of robust detection methods. This survey provides a comprehensive analysis of the state-of-the-art approaches in deepfake face detection within video data, categorizing them into three major sections: Hybrid Methods, Ensemble Methods, and Recurrent Methods. Hybrid methods integrate multiple machine learning models to enhance the accuracy of spatial and temporal feature extraction, offering a balanced approach to detecting subtle manipulations in deepfake videos. Ensemble methods leverage the combined strengths of diverse classifiers to improve robustness and adaptability in varying video contexts. Recurrent methods utilize recurrent neural networks to capture temporal dependencies across video frames, addressing challenges in detecting complex and sequential manipulations. The review highlights that while these approaches demonstrate significant progress, challenges remain in generalizing detection models to novel and highly advanced deepfake techniques. This work concludes by identifying the limitations in existing research and suggesting future directions for improving the robustness and scalability of deepfake detection systems.

关键词： Surveys Deepfakes Adaptation models Accuracy Computational modeling Streaming media Feature extraction Robustness Ensemble learning Face detection

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Metamodeling of Numerical simulations for Optimization of Hairpin Bending Processes 11

Metamodeling of Numerical Simulations for Optimization of Ha...

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11th international Electric Drives Production conference (E/DPC)

作者： Wirth, Felix Hausmann, Ludwig Eppler, Andreas Fleischer, Juergen Karlsruhe Inst Technol KIT Wbk Inst Prod Sci Karlsruhe Germany

ISBN: (纸本)9781665418096;9781665418089

As a result of the progressive electrification of mobility solutions, manufacturing technologies for distributed stator windings need to be improved in order to meet the automotive demand for productivity and quality. In this context, the hairpin technology already provides advantages regarding the possible level of automation and productivity in comparison to conventional winding technologies, but still shows technological weaknesses concerning process reliability. Against this background, approaches for optimized process control based on physical process models have a high potential to minimize waste in production resulting from variations in wire properties or an inappropriate machine parametrization. However, the accuracy of analytical modeling techniques is limited due to complex interactions of machine kinematics, tooling and material properties in rectangular wire bending processes. Hence, the calculation effort of more precise finite element simulations needs to be reduced in order to enable model-based process control and economic tool design. Therefore, conventional and AI-based methodologies for metamodeling of numerical process simulations are introduced and compared in this paper. Furthermore, opportunities of optimized process control of hairpin shaping processes based on efficient but also accurate metamodels are discussed.

关键词： hairpin technology hairpin shaping process control numerical simulation artificial intelligence metamodeling

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Role of Artificial Intelligence for Noninvasive Height Measurement applications: A Survey

Role of Artificial Intelligence for Noninvasive Height Measu...

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IEEE Punecon

作者： Inaganti Ruchitha Sunita Dhavale Department of Computer Science and Engineering Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

Height measurement is crucial across various domains including healthcare, ergonomics, security, and forensic analysis. Traditional methods, such as stadiometers, often require cooperation from the subject and can be intrusive, posing significant challenges especially with children, the elderly, or individuals with disabilities. This survey paper addresses the growing need for artificial intelligence (AI) based noninvasive, automated height measurement techniques that can effectively estimate height without physical interaction. In this paper, we examined different state of the art methodologies for height measurement application area, including AI, computer vision (CV), deep learning (DL), depth imaging, and 3D laser scanning. While 3D scanning and depth imaging methodologies provide precise measurements, but they require specialized equipments which leads to additional costs for implementation. Recently, DL and advanced CV models have shown great accuracy in height estimation. Promising solutions for uncooperative subjects can be found by utilizing facial recognition and anatomical traits, and incorporating biometric parameters which can improve prediction accuracy. Despite these advancements, challenges remain with methods requiring multiple images or additional equipment, underscoring the need for more practical and user-friendly solutions. This survey highlights the necessity for tailored approaches to address specific measurement challenges and proposes guidelines for future studies to improve efficiency of noninvasive height estimation technologies.

关键词： Surveys Deep learning Accuracy Three-dimensional displays Face recognition Computational modeling Estimation Height measurement Security Artificial intelligence

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UniSim-design simulation and analysis of a sulphuric acid manufacturing plant with double absorption process 10

UniSim-design simulation and analysis of a sulphuric acid ma...

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10th international conference on simulation and modeling methodologies, technologies and applications, SIMULTECH 2020

作者： Mounaam, Amine Harmen, Yasser Chhiti, Younes Souissi, Ahmed Salouhi, Mohamed Khouakhi, Mohamed El All Laboratory Ecole Mohammadia d'Ingénieurs Mohammed V University Rabat Morocco Innovation Lab for Operations Mohammed VI Polytechnic University Ben Guérir Morocco Ibn Tofail University Kenitra Morocco National School of Applied Sciences Chouaib Doukkali University El Jadida Morocco

ISBN: (纸本)9789897584442

In the sulphuric acid manufacturing industries, plant modelling and simulation is a challenging task to minimize emissions, maximize production performance and revenue. In this context, this study presents the steady behaviour of a double absorption process of an industrial sulphuric acid plant. The closed-loop process is modelled and simulated using UniSim Design R451 simulator and validated with plant data. The model includes principally: conversion reactor, plug flow reactors, absorbers, heat exchangers, pumps and compressors. The parameters of the converter kinetic were fitted to the real plant data, while the other parameters were estimated using conventional correlations. The results show a good agreement for the complete plant, with an accuracy that exceeds 97 %. Besides the optimization aspects, UniSim Design plant model is also useful for operator training, simulation of diverse scenarios and development of processes digital twin. © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

关键词： Personnel training

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Zero-Order Optimization in Security Constrained Power System Scheduling

Zero-Order Optimization in Security Constrained Power System...

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Electrical, Computer and Energy technologies (ICECET), international conference on

作者： Qi An Jianxiao Wang Shengyu Wu Weikai Ren Ruanming Huang Gengyin Li State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China State Grid Energy Research Institute CO. LTD. Beijing China Institute of Energy Peking University Beijing China Planning Center State Grid Shanghai Electric Power Company Shanghai China

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

The security and resilience of power systems are gradually challenged. As a typical index measuring the security and resilience of power systems, the frequency stability issue is becoming prominent. Due to the increase in the complexity of power systems, the frequency stability boundary is gradually becoming fuzzy, leading to traditional analytical-modeling algorithms hardly applicable for frequency stability constrained dispatch. Existing data-driven methods for characterizing frequency stability boundary typically requires massive simulation calculations, leading to unnecessary computing power consumption. To this end, zeroth-order (ZO) optimization, which aims to solve optimization problems embedding non-analytic functions, is adopted for solving the frequency stability constrained dispatch in this paper. In contrast to traditional data-driven methods, we directly embed frequency stability simulation check into power system dispatch. The gradient of frequency stability simulation check is estimated through ZO optimization, thereby realizing the interaction optimization between power system dispatch and real-time simulation. In this way, the solution for the frequency stability constrained dispatch is modified towards the optimal point along the gradient descent direction. Cases studies based on IEEE-9 bus system verifies that, compared with traditional data-driven methods, the solution approach we propose could reduce the system cost while significantly improving the calculation efficiency.

关键词： Power measurement Power demand Processor scheduling Stability criteria Power system stability Real-time systems Frequency estimation Indexes Optimization Resilience

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simulation of ultrasonic sensor at lower ultrasonic range in data transmission

Simulation of ultrasonic sensor at lower ultrasonic range in...

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2021 international conference on Industrial Engineering, applications and Manufacturing, ICIEAM 2021

作者： Saidov, Behruz Telezhkin, Vladimir Higher School of Electronics and Computer Science South Ural State University National Research University Chelyabinsk Russia

ISBN: (纸本)9781728145877

At present, the modeling of an ultrasonic transducer at the lower ultrasound range (20 kHz) during data transmission is being defaced. Ultrasonic sensors are a key part of ultrasonic technology. The use of ultrasonic vibrations is one of the simplest and most effective ways to process an ultrasonic signal. The advantage of this method is that ultrasound is not audible to the human ear, so it does not have a negative mental effect on a person and by increasing the radiation power, a larger suppression area can be achieved. More recently, information-processing technologies in corporate communication systems and information transfer using ultrasonic vibrations are increasingly being used. This is because more advanced applications are emerging in the field of wireless (short and long range) communication and data transmission, and at the same time, new challenges arise that require these corporate systems to have high frequency, wide bandwidth and compact size. The use of simplified physical models does not allow obtaining the expected results in the process of structural analysis of the latter. In this regard, the need arose for the integration of models that regulate and take into account a wide variety of material properties and allow you to solve with the help of powerful software in computer modeling is the ANSYS software package. © 2021 IEEE.

关键词： Ultrasonic sensors

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Exploration of Congestion Control Techniques on Dragonfly-class HPC Networks Through simulation 12

Exploration of Congestion Control Techniques on Dragonfly-cl...

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12th international Workshop on Performance modeling, Benchmarking and simulation of High Performance Computer Systems held as part of the 33rd ACM/IEEE international conference for High Performance Computing, Networking, Storage and Analysis

作者： McGlohon, Neil Hemmert, K. Scott Brown, Kevin A. Levenhagen, Michael Chunduri, Sudheer Ross, Robert B. Carothers, Christopher D. Rensselaer Polytech Inst Troy NY 12180 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9781665411189

Ensuring optimal conununication latency in High Perfonnance Computing (HPC) networks is of critical importance to the efficient operation of facilitated applications. Different application operations and types of tasks, such as 10 operations, can create a variety of traffic patterns across the system interconnect. Some conununication patterns, however, can be problematic for overall system performance. One traffic pattern of particular concern is the many-to-one or incast. When packets sent from many different endpoints target a singular, or small number of destinations, it can overwhelm the receiving endpoints' ability to process the traffic, resulting in a cascading effect of induced congestion. This can have broadreaching, detrimental effects to other applications as their data streams encounter induced congestion. The concept of congestion control has been explored in various HPC system technologies and is an important feature in stateof-the-art networks such as Infiniband and the Cray Slingshot interconnect. Because access to physical, full-scale interconnects of bleeding-edge design can be challenging and the exact mechanisms of operation not publicly known, we look to simulation to explore techniques for congestion control with a fine level of flexibility not available on real-world systems. We present and explore a mechanism for congestion control which seeks to detect network congestion, identify its cause, and abate it by throttling injection of identified aggressor endpoints. Our work proposes, discusses and evaluates two similar implementations of this mechanism for congestion control in two different network simulators and their capabilities at mitigating the effects of congestion on application communication performance and general system packet latencies.

关键词： Technological innovation Codes Computational modeling System performance High performance computing conferences Traffic control

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Adaptive Optimization and Resource Allocation (AORA) Model for IoT-Edge Computing Using Hybrid Newton-Raphson and Dolphin Echolocation Algorithm (HNR-DEA) Technique

Adaptive Optimization and Resource Allocation (AORA) Model f...

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Advanced Computing technologies (ICoACT), international conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan A. Shiny K. Dharani Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The swift expansion of Internet of Things (IoT) applications demands the implementation of real-time data processing and low-latency communication, thereby posing challenges to conventional cloud computing paradigms. This study confronts these issues by amalgamating edge computing with hybrid optimization methodologies, and introduces the AORA (Adaptive Optimization and Resource Allocation) framework. The AORA framework capitalizes on intelligent task scheduling, dynamic resource allocation, and energy-efficient approaches to augment the performance of edge *** research methodology encompasses the collection and preprocessing of data derived from IoT devices, feature extraction, prediction of resource demand, and the optimization of task offloading. It employs the Hybrid Newton Raphson and Dolphin Echolocation Algorithm (HNR-DEA) methodology, which synergizes Newton Raphson optimization technique for faster convergence rate around local optima with bio-inspired Dolphin Echolocation Algorithm (DEA) for comprehensive global *** Experimental outcomes reveal considerable advancements, including a 45% reduction in average latency, a 38% decrease in overall energy consumption, and a 23% enhancement in resource utilization. Furthermore, the model attained a 97% task completion rate alongside consistently elevated network throughput. simulations were executed in CloudSim with 100 IoT devices generating dynamic workloads and edge servers equipped with multi-core processors. The experimental framework utilized Google Cluster Data to replicate workload variations, thereby ensuring scalability and robustness. These results underscore the AORA model's potential as a resilient and scalable solution for real-time IoT applications.

关键词： Adaptation models Computational modeling Scalability Prediction algorithms Real-time systems Internet of Things Resource management Newton method Optimization Dolphins

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