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An intelligent and Dynamic Replication Scheme for Reliable Cloud Environment

An Intelligent and Dynamic Replication Scheme for Reliable C...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Damodhar M Ch. D V Subba Rao Department of Computer Science and Engineering Sri Venkateswara University College of Engineering Tirupati India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Recently, cloud computing research, especially Virtual Machine based replication schemes and their applications, were becoming prevalent. A cloud data center (DC) comprises of hosts with an enormous of virtual machines (VMs) where determining replica quantity and replication order is a tedious task to minimize cost and bandwidth. Majority of the existing dynamic replication techniques rely on budget constraints and server failure rate to guarantee customer requirements. But, there is a strong need to determine replication order of servers to retain the performance and system stability. In this paper, we suggest two different algorithms for better selection with proper and intelligent placement of replicas in the cloud region. The proposed algorithms towards dynamic replication are request based replica selection (RRS) and ant colony optimization (ACO). The foremost proposed algorithm was request based replica selection used for obtaining the best selected replicas depends upon the VM performance ranking and failure probability based on the task requirement. Conversely, the subsequent offered algorithm is ACO that is helpful to find the best location to place the designated replica depends on the least-cost distance metric, and the replicas availability in order to encompass and regulate with the entire network load and the link bottlenecks. The performance of proposed techniques using CloudSim was evaluated against various pro-active fault tolerance strategies. The simulation results gives that dynamic replica selection provides efficient task replication when compared with the existing pro-active algorithms. Additionally, ACO realizes high availability, low cost, and less bandwidth utilization.

关键词： Cloud computing Ant colony optimization Costs Heuristic algorithms Simulation Bandwidth Throughput Virtual machines Stability analysis Servers

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression

A Novel Rate of Penetration Fusion Modeling Method based on ...

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ieee international conference on Industrial Technology (ICIT)

作者： Yang Zhou Zihan Zhang Shipeng Chen Min Wu Xin Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the ieee CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications.

关键词： Drilling Support vector machines Employee welfare Chaos Geology Clustering methods Clustering algorithms Benchmark testing data models Optimization

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An intelligent SCADA System for Power Distribution Network Cable Fault Detection with Real-Time Monitoring and Autonomous Maintenance

An Intelligent SCADA System for Power Distribution Network C...

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international conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： S M Nahidul Islam Shameem Ahmad Md. Mahbub-E-Elahi Prodhan Md. Shoaib Khan Chowdhury Sakib Bin Mizan Abir Ahmed Effat Jahan Md. Rifat Hazari Chowdhury Akram Hossain Department of Electrical and Electronic Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh Department of Computer Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Supervisory Control and data acquisition (SCADA) systems are significant for boosting industrial operations, but established systems often struggle with fault management and real-time monitoring. To address these issues, this paper proposes an advanced SCADA system that integrates IoT technologies, multi-agent systems, and predictive maintenance for seamless data collection, analysis, and decision-making. Machine learning enhances fault detection and cybersecurity, while a modular hardware design allows scalability and flexibility to various industrial environments. The system is highly sensitive to detecting faults, including open circuit faults, line-to-ground faults, line-to-line faults, double line to ground faults and three-phase faults. It improves operational safety and efficiency by accurately locating faults and notifying professionals in real time. Extensive simulations and hardware tests were conducted to validate its performance in different fault scenarios. The results show that the proposed SCADA system has successfully detected different types of faults, offering a unified approach for fault management, maintenance, and real-time monitoring.

关键词： Power cables Fault detection Scalability SCADA systems Electrical fault detection Real-time systems Hardware Maintenance Circuit faults Predictive maintenance

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Optimizing Shortest Paths in Big data Using the Floyd-Warshall Algorithm

Optimizing Shortest Paths in Big Data Using the Floyd-Warsha...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Sangeetha, D.P. Sekar, Satheeshkumar Parvathy, Palaniraj Rajidurai Soundarya, R. Ganeshbabu, T.R. Muthulekshmi, M. Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Mphasis Corporation ChandlerAZ United States Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Improving the efficacy and precision of route computations in massive datasets is the goal of optimizing big data shortest routes using the Floyd- Warshall algorithm. The algorithm's ability to determine the shortest paths between all pairs of vertices in a graph allows for optimal route design, network optimization, and urban transportation solutions. Creating a solid foundation that can analyze large amounts of data and provide trustworthy outcomes for decision-making is the main objective. In addressing these challenges, concerns related to scalability, slow convergence rates, and potential errors in route calculations need resolution. The objective is to improve the efficiency and decision-making abilities of intricate systems by adjusting the Floyd- Warshall algorithm for extensive data environments. This will lead to a scalable and efficient solution that can be used in transportation, network routing, and urban planning, among other areas. In one example, the intermediate matrix displays the nodes from A to D, while in the other, the results from the road network show the initial adjacency matrix, where the distances between points A and D vary from 10 to 35 kilometers. In the final matrix, for the nodes A-D, the distance in kilometers varies from 10-35 for the shortest distance traveled;in the same data set, this range is 10-40. © 2025 ieee.

关键词： Urban transportation

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On-Device Neural Architecture Search

On-Device Neural Architecture Search

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ieee international conference on Consumer Electronics (ICCE)

作者： Andrea Mattia Garavagno Edoardo Ragusa Paolo Gastaldo Antonio Frisoli Claudio Loconsole Department of Electrical Electronic Telecommunication Engineering and Naval Architecture DITEN University of Genoa Genoa Italy Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy Institute of Mechanical Intelligence Scuola Superiore Sant'Anna Pisa Italy Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors. This new adaptation capability can be particularly useful in the case of human-machine interfaces for which the neural network analyzing the biometrical data can be re-designed each time the user changes, after a guided data collection procedure, fighting the typical data variations between individuals on a new level. To implement the proposed approach a new NAS has been designed and then validated on the Italian Sign Language dataset (ISL), a collection of surface electromyography (sEMG) signals of the signs of the Italian alphabet, using several embedded systems. Moreover, further validation on the Case Western Reserve University dataset (CWRU), a benchmark for intelligent fault diagnosis, is presented to suggest another possible application of the proposed approach. When run on a Raspberry Pi 4, the proposed NAS performs beyond the state of the art proposing a tiny neural architecture having 0.63 times less RAM occupancy and 5.96 percentage points of more accuracy in the case of the ISL dataset; and 0.44 times less RAM occupancy and 0.2 percentage points of more accuracy in the case of the CWRU dataset.

关键词： Performance evaluation Fault diagnosis Biometrics Sign language Costs Human-machine systems Random access memory Computer architecture Real-time systems Neural architecture search

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advanced Fault Sleuth System Architectures for Prognostic Anomaly Detection and Autonomous Irregularity Mitigation in intelligent Power Networks

Advanced Fault Sleuth System Architectures for Prognostic An...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Biresh Kumar Arpit Anurag Sanyam Anand Ritesh Kumar Kamaldeep Gupta Binod Kumar Sharma Amity Inst. of Information Technology Amity University Ranchi Jharkhand India Cambridge Institute Of Technology Tatisilwai Ranchi Jharkhand India St. Xavier’s College Ranchi Jharkhand India Dept. of Computer Sci. & Engg. BIT Mesra Ranchi Jharkhand India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Revenue losses, inconsistent service, delayed problem detection, and electricity theft are just a few of the major issues that power distribution networks deal with. The suggested Fault Sleuth System makes use of Internet of Things (IoT) technologies to facilitate remote system control, automatic anomaly detection, and real-time electrical parameter monitoring. The technology guarantees accurate fault localization and proactive theft prevention by combining sophisticated data analytics and feature extraction techniques. The technology greatly lowers maintenance costs and security threats while improving operating efficiency through thorough data collecting, preprocessing, and intelligent analysis. In addition to improving problem detection and theft mitigation, this strategy also raises customer happiness, power quality, and service reliability. The study's conclusions show how the Fault Sleuth System can revolutionize the way that important power distribution issues are handled. This technology opens the door for a more robust, safe, and effective energy infrastructure that can satisfy needs in the future by leveraging real-time analytics and state-of-the-art IoT technologies.

关键词： Accuracy Prevention and mitigation Fault detection Power distribution Real-time systems Smart grids Internet of Things Security Predictive analytics Anomaly detection

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Adaptive Power Control with Improved Genetic Algorithm to Enhance QoS Performance in Vehicular Communication systems

Adaptive Power Control with Improved Genetic Algorithm to En...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： A. Aranganathan Dalia Al-Tarazi S. Bharath Durga Devi Jayant Giri S. Ravi Department of ECE Sathyabama Institute of Science and Technology Chennai India Department of Architecture Engineering Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of ECE Rajalakshmi Institute of Technology Chennai India Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad hoc Networks (VANETs) play a crucial role in intelligent transportation systems since they enable vehicles to communicate instantaneously. Elevated mobility and dynamic settings create difficulties for dependable data transmission, resulting in longer delays and packet loss. Method used: An advanced Genetic Algorithm-based Vehicular Ad hoc Routing (AIGAV) model that leverages meta-heuristic optimization to enhance routing decisions in VANETs. It utilizes GPS for live navigation and modifies routes as required. This optimization reduces congestion and distributes network load more evenly for improved packet forwarding. Results: The AIGAV model exceeds the performance in crucial efficacy aspects. It enhanced data success rates (95.25%), remarkable throughput (521.41 Kbps), and minimal routing overhead (254.18 packets), and decreased data loss rates. Conclusion: The AIGAV model improves the efficiency of VANET routing through dependable and adaptable communication in mobile settings. It employs optimization to enhance safer, more intelligent transportation systems for upcoming uses.

关键词： Adaptation models Metaheuristics Vehicular ad hoc networks Routing Throughput Reliability Integrated circuit modeling Genetic algorithms Load modeling Global Positioning System

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An IoT Cloud Based Health Monitoring System for Remote Area Using Node MCU: E Health Checker

An IoT Cloud Based Health Monitoring System for Remote Area ...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： B. Venkataramanaiah Sravan Kumar Chittimalla V Raveendra Reddy A. Vani Malokhat Saidmuratova Surayyo Khasanova P Vinayagam Department of ECE Vel Tech Rangarajan Dr.sagunthala R&D Institute of science and technology Chennai India Enliven Technologies Inc Texas United States Department of EEE Ramireddy Subbaramireddy Engineering College SPSR Nellore India Department of ECE Chaitanya Bharathi Institute of Technology Hyderabad India Department of Pedagogy and Psychology Urgench State University Urgench Uzbekistan Department of Psychology and Medicine Mamun University Khiva Uzbekistan Department of ECE Saveetha Engineering College Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research work introduces a real-time health monitoring system using IOT sensors and cloud integration for human health monitoring. The system leverages the Node MCU micro controller for its Wi-Fi capabilities., interfacing with sensors like DHT11 (for temperature and humidity)., HBR (for heart rate)., and SpO2 (for oxygen saturation). These sensors continuously gather health-related data., which is transmitted to the Thing Speak cloud platform for storage and analysis. The hardware setup involves connecting the Node MCU to the sensors for accurate data collection., while the firmware is programmed to handle real-time data acquisition and cloud communication. ThingSpeak is configured to receive and store the data, organizing it into custom channels for further analysis. Basic data processing algorithms are applied to filter out noise and detect trends in the collected health metrics. The system enables real-time visualization of health data and employs machine learning models to offer personalized healthcare insights. This framework facilitates proactive healthcare interventions., enhancing patient care and supporting chronic condition management. With continuous monitoring and cloud-based analysis, it provides a foundation for personalized treatment plans, improving the overall healthcare experience and people lives in rural areas will benefit.

关键词： Temperature sensors Cloud computing Machine learning algorithms Clouds Medical services Market research Real-time systems Sensor systems Sensors Monitoring

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A Stackelberg Game-Based Real-Time Defense Strategy Against False data Injection Attacks 8th

A Stackelberg Game-Based Real-Time Defense Strategy Against ...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wu, Lei Du, Dajun Xiao, Yang Zhu, Minggao Rakić, Aleksandar Shanghai Key Laboratory of Power Station Automation Technology School of Mechanical Engineering and Automation Shanghai University Shanghai200072 China School of Electrical Engineering University of Belgrade Belgrade11000 Serbia

ISBN: (纸本)9789819602933

When the transmitted control signals in the forward channel are maliciously tampered by false data injection attacks (FDIAs), their authenticity is destroyed, leading to networked control systems (NCSs) performance losses or even instability. This paper proposes a Stackelberg game-based real-time defense strategy against FDIAs. First, if the transmitted control signals are tampered and the cost function of the attacker is not leaked, the defender makes the best response only based on its own cost function during each control cycle. Second, considering these sequential asymmetric information interactions between the attacker and defender, Stackelberg game is employed to establish the optimization model including cost functions and constraints. Third, Stackelberg equilibrium of the established optimization model is solved by extremum-based optimal strategy algorithm to obtain real-time defense strategy, while asymptotic stability of the system is proved. Finally, the feasibility and effectiveness of the proposed defense strategy are confirmed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cost functions

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Optimizing Battery Performance in Electric Vehicles Through Predictive Modeling

Optimizing Battery Performance in Electric Vehicles Through ...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Kumar, C. Siva Sagar, P. Lakshmi Venkata Bhanu Pratap, B. Geethamadhuri, Gurramkonda Kumar, Banda Sai Ram Dinesh, Bommineni Mukesh, Pallaputhimmannagari Sree Vidyanikethan Engineering College Dept of Csse Andhra Pradesh Tirupati India Dept of Data Science Karakambadi A.P Tirupati India BORiTS Private Limited Telangana Hyderabad India Ug School Dept of Csbs Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798331527495

The fast-paced electrification of the automobile sector has intensified an immediate need for reliable battery management systems capable of sustaining long-term performance. One of the most important indicators when it comes to battery health is State of Health (SOH), which indicates how much capacity is left and how long a certain battery will endure. Reliable estimation of SOH is critical to enable the optimal performance of vehicles, reduce service costs, avoid unanticipated failures. In this project, we propose a machine learning approach to predict the SOH of EV batteries based on the two widely used algorithms, XGBoost and LGBM. In order to cover and analyze as many process variables available as possible to build a database for the training and evaluation of the models, we measured multiple battery parameters, such as voltage, current, temperature and historical charge/discharge cycles. The train test split this dataset in such a way that the greater part of the dataset is used for the machine learning operations. Individual models, such as XGBoost and LGBM, are used to be train due to its strength in handling large data and complex data with a high precision. This study attempts to find out the better of the two algorithms for real-time SOH prediction in practice by analyzing the accuracy and performance metrics of both algorithms. Results of this analysis can help manufacturers and operators make data-informed decisions regarding battery usage, maintenance schedules and replacement, which will contribute to improved vehicle reliability and extended battery life. © 2025 ieee.

关键词： Decision making

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