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Autonomous Power Management for Hybrid AC/DC Nano Grid Using ESS and DG 1

Autonomous Power Management for Hybrid AC/DC Nano Grid Using...

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1st international conference on Intelligent Controller and computing for Smart Power, ICICCSP 2022

作者： Razzaq, Syed Abdul Jayasankar, Vairavasamy Vel-Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Dept. Electrical and Electronics Engineering Chennai India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

The increasing power demand with energy management is turning out to be an important aspect for electrical power system. Integration of Renewable Energy Source (RES) has indeed minimized the carbon emission and succeeded in delivering clean green energy. Power electronic converters are the major building block of power transmission and conversion at economical cost and also in reducing the design and installation of bulky electrical devices in remote zones, rural, congested place and consumers with few kilo watts of power. Designing a Nano Grid (NG) with less than 50kW of capacity will be benefit for the electrical distribution board. The proposed design discuss the design of Photo Voltaic (PV) with MPPT, DC/DC converter, single phase bidirectional Voltage Source Converter(VSC), Battery Energy storage System (BESS) and three phase converter. The control technique for autonomous bidirectional power transfer to hybrid AC/DC NG with a simple PI controller. NG structure is simulated in Matlab/Simulink. This NG is equally adoptable for Pico Grids (PG), domestic and commercial consumers. It also features a load management strategy. The proposed work is reliable in upgrading to Micro Grid (MG) or integrating with MG with decentralized control scheme. This NG is plug and play type also suitable for internet of Things (IoT) for various electrical farms and other applications. © 2022 IEEE.

关键词： distributed power generation

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A Comparative study on Simulation Environment on Fog computing 1

A Comparative Study on Simulation Environment on Fog Computi...

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1st IEEE international conference on Emerging Trends in Industry 4.0, ETI 4.0 2021

作者： Kumar, Bijendra Jain, Vibha Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665422376

The evolution of Information and Communication technology (ICT) has a massive impact on the growth of distributed systems such as the internet of Things (IoT), cloud computing, and fog computing. All these technologies are closely coupled, where several IoT devices all together can generate high volume data that need to be processed either on cloud data centers or proximity deployed fog devices depending upon the nature of user application. Though these IoT-fog-cloud systems are very complex to design and implement, hence, to validate the flexibility and scalability of the new design, simulation tools areused. These days several types of fog simulators are available in the market depending upon the need of design, but it is very cumbersome task to select the best suitable tool. In this paper, a comparative study is present among nine available fog simulators which aim to help researchers and practitioners to select appropriate simulators based on their research work. © 2021 IEEE.

关键词： Fog

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Feature-Driven DDoS Detection in IoT with Machine Learning Approach

Feature-Driven DDoS Detection in IoT with Machine Learning A...

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Cyber Security and computing (CyberComp), international conference on

作者： Sandra Suriya AL Jayasuri Sofia Najwa Ramli Cik Feresa Mohd Foozy Mohd Azahari Mohd Yusof Aminu Abdulkadir Mahmoud Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Center of Information Security Research Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Jasin Melaka Campus Universiti Teknologi Mara Melaka Malaysia Department of Cyber Security Yusuf Maitama Sule University Kano Nigeria

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

This research focuses on strengthening internet of Things (IoT) security against distributed Denial-of-Service (DDoS) attacks by implementing a hybrid machine learning approach that integrates Naïve Bayes, K-Nearest Neighbors (KNN), and LightGBM algorithms. Recognizing the critical vulnerabilities of IoT systems to DDoS attacks and the urgent need for robust security measures, the study evaluates these models using the CICIoT2023 dataset. To address challenges like data imbalance, sophisticated feature selection techniques, including correlation matrix and information gain, are employed. The research rigorously assesses the models' performance in terms of accuracy and false positive rates, aiming to determine the most effective method for mitigating cyber threats and enhancing IoT system resilience. In Split 1, KNN achieves 99.44% accuracy with a false positive rate of 0.05%, LGBM achieves 99.89% accuracy with a false positive rate of 0.04%, while NB lags with 88.67% accuracy and a false positive rate of 0.2%. In Split 2, KNN reaches 99.8% accuracy with a false positive rate of 0.01%, LGBM achieves 99.9% accuracy with no false positives, and NB maintains 89.69% accuracy with a false positive rate of 0.2%. These findings highlight the effectiveness of the machine learning approach in mitigating cyber threats and fortifying IoT system resilience.

关键词： Fault tolerance Accuracy Correlation Machine learning Nearest neighbor methods Feature extraction Denial-of-service attack internet of Things Security Computer crime

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Determination of Suitable Resource Discovery Tool and Methodology for High-Volume internet of Things (IoT) 1

Determination of Suitable Resource Discovery Tool and Method...

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1st international Recent Trends in Engineering, Advanced computing and technology conference, RETREAT 2020

作者： Bakar, Mohd Tamizan Abu Jamal, Azrul Amri Faculty of Computer Media and Technology University College Tati Jalan Panchor Teluk Kalong Kemaman Terengganu24000 Malaysia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Besut Terengganu22200 Malaysia

This paper discusses several issues supporting a knowledge-based methodology for discovery of high-volume IoT resources in the simulator NS-3 environment. We found the concept was developed in previous researches, especially based on widely accepted concepts of Q-Learning discovery model. The model is validated using samples from emulations of tested data in the NS-3 simulator. Proper simulation in NS-3 based on the different modules such as checkpoint and restore was used to model and analyse the data. The main feasibility checkpointing concept of simulations in the NS-3 processes were using distributed Multi-Threaded Checkpointing (DMTCP) to run on a single machine and Message Passing Interface (MPI) under distributed machine to speed up the NS-3 model initialization and execution. As the chosen model to be implemented in this analysis, the Q-learning algorithm proposal offers a possible solution for addressing evolving IoT environments and configurations. Q-learning is one of the successful techniques available for the exploration of IoT nodes, but context-based problems have already been established and simplified as issues of dedicated server management, IoT object data acquisition issues, and unique application requirements. The findings empirically support the validation of the Q-Learning model improvement for high-volume IoT resource discovery cases. The study will contribute to the new model development by providing new insights on the conceptualization and validation of knowledge-based methodology based on widely accepted techniques and approaches. © Published under licence by IOP Publishing Ltd.

关键词： internet of things

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Buck Boost Converter Control in an IoT-Based Wind System Using Supervised Learning Technique

Buck Boost Converter Control in an IoT-Based Wind System Usi...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (IstEMS), international conference on

作者： N. Anbuselvan S. Hemalatha Suresh Lakshminarayanan E. Sivanantham N. Mohankumar Thamizhamuthu R Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Engineering & Electronics Oman South Al Batina University of Technology and Applied Sciences - Almusanna Oman Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V Raju Institute of Technology Telangana India Department of Computing Technologies Kattankulathur Campus-Chengalpattu SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

In recent years, distributed energy resources (DERs) have gained significance as a way of introducing a substantial quantity of renewable energy sources (RESs) into power grids. Wind turbines are accessible in windy areas, giving wind energy systems (WES) a realistic alternative for clients. This work attempts to increase the effectiveness and adaptability of WES by presenting a novel way of managing DC-DC buck-boost converters. The suggested method combines an internet of Things (IoT) system for monitoring with supervised learning algorithms to make energy conversion more stable, efficient, and able to adapt in real time to changing wind conditions. In this research, creating a reliable supervised learning control method is the primary goal. The second goal is to optimize the most important parameters of the converter. Using the new supervised learning algorithm, the study changes variables like duty cycle, switching frequency, and inductance to get the best results based on past performance data and how the system is working now. It investigates the influence of these parameters on overall system efficiency and stability. The incorporation of an IoT monitoring system into the WES is an essential component of the study. Various sensors monitor variables such as wind speed, converter input and output voltages, and currents in real-time and are shown as results.

关键词： Wind energy Wind speed Switching frequency Supervised learning Real-time systems stability analysis Wind turbines

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Detection and Classification of Anomalies in internet of Vehicles using Convolutional Neural Networks

Detection and Classification of Anomalies in Internet of Veh...

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international conference on Computational Science and technology (ICCst)

作者： Palash Hade Kamlesh Waghmare PG CS GCOEA Amravati Maharashtra India Faculty of CS GCOEA Amravati Maharashtra India

ISBN: (纸本)9781665476560

internet of Vehicles in brief is the distributed networks that makes the use of data produced due to connected cars and vehicular ad-hoc networks. With the progress in Science and technology, The internet of Vehicles has got an exponential boost, leading to tremendous progress in the field. The smart vehicles are equipped with various sensors which are data collection points, from which various useful data is collected for the processing and operating of the intelligent vehicles and play a vital role in implementation and communication of the vehicles. The smart vehicles has been a boon to humans which include fewer accidents, faster travel times, less theft due to tracking. But with these advantages, the vehicles have to cope up with various malicious intrusions and issues that compromise with the security and integrity of the vehicle. This is one of the major issues in the IOV which makes the data more vulnerable to be exposed. The research work aims to make the system more secure and safe by the detection and classification of the attacks that make these smart vehicles unsafe. The approach used in this work is based on the CNN based architecture VGG19, in which the data obtained from OBD port of vehicle and converted to visual format is feed. The types of the attacks included in this research work for the classification are Denial of Service Attack (DoS), Spoofing(RPM Spoof, Gear Spoof), Fuzzy Attack. The purpose of the research is to design a solution that makes the system more secure, preserves the privacy and increase the trustability of the user.

关键词： Visualization Privacy Scientific computing Intelligent vehicles Gears Convolutional neural networks Security

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Fair Fit—A Load Balance Aware VM Placement Algorithm in Cloud Data Centers 1st

Fair Fit—A Load Balance Aware VM Placement Algorithm in Clo...

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1st international conference on Advanced Communication and Computational technology, ICACCT 2019

作者： Gohil, Bhavesh N. Gamit, Sachin Patel, Dhiren R. S.V. National Institute of Technology SuratGujarat395007 India VJTI Mumbai India

ISBN: (纸本)9789811553400

Cloud computing is a kind of large-scale distributed computing. It has recently emerged as a new technology for providing services over the internet. It has gained a lot of attention in individual, industry, and academia. Cloud computing dynamically allocates virtual resources as per the demands of users. The rapid increase of data computation and storage in cloud results in uneven distribution of workload on its heterogeneous resources, which may violate SLAs and degrades system performance. Distributing balanced workload over the available hosts is a key challenge in cloud computing environment. VM placement is the process by which it selects the most suitable physical machine (PM) in cloud data centers to deploy newly created virtual machine (VM) during runtime. In this paper, we study VM placement problem with the goal of balanced resource utilization in cloud data centers. Several algorithms have been proposed to find a solution to this problem. Most of the existing algorithms balance the cloud resources based on its utilization which results in unbalanced and inefficient cloud resource utilization. We propose an algorithm, which minimizes the imbalance of resource usage across multiple dimensions, reduces resource leak and wastage, and maximizes the resource utilization. Our algorithm finds a suitable host for incoming VM from categorized host list based on remaining resources using cosine similarity measure. Simulation results show major improvements over the existing algorithms like Binary Search Tree-based Worst Fit and Best Fit, Round Robin, and default Worst Fit. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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distributed Manufacturing in Knitting Industry

Distributed Manufacturing in Knitting Industry

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Lijun Li Pingfan Sun Jixiang Lu School of Automation Southeast University Nanjing China Institute of Knitting Ningbo Cixing Co. Ltd. Ningbo China College of Science and Technology Ningbo University Ningbo China

Knitwear is the second largest product category in the traditional clothing industry. The traditional clothing product supply chain is difficult to meet consumer's demand of fast fashion. AI and industrial internet technology, together with intelligent knitting equipments make C2M flexible supply chain possible through digitization. distributed cloud factory is built as regional node of production supported by digital twin and parallel intelligence. Personalized needs of end consumers can be quickly responded, and the cost is close to traditional large-scale assembly line. Parallel manufacturing applies industrial robots, internet of things, cloud computing, AR and AI technologies comprehensively to build virtual factory in Cloud that carries out decision-making and management simultaneously, with highly automated production equipments in workshop. Meanwhile, it combines with smart knitting factories to build a global distributed collaborative production management platform based on parallel manufacturing architecture system through virtual reality interaction, distributed manufacturing, managed and controlled through Cloud. Intelligent upgrading of traditional knitting industry can effectively improve labor productivity, reduce production cost and number of employees.

关键词： Productivity Cloud computing Costs Digital twin conferences Supply chains Collaboration

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Ensemble Bagging Approach for IoT Sensor Based Anomaly Detection 1st

Ensemble Bagging Approach for IoT Sensor Based Anomaly Detec...

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1st international conference on Intelligent computing in Control and Communication, ICCC 2020

作者： Reddy, Dukka Karun Kumar Behera, H.S. Pratyusha, G. M. Sai Karri, Ravikiran Burla Odisha768018 India Resapuvanipalem Visakhapatnam Andhra Pradesh530013 India Department of Computer Science Sanketika Institute of Technology and Management Pothinamallayya Palem Visakhapatnam Andhra Pradesh530041 India

ISBN: (纸本)9789811584381

The IoT is the next age of communication, as the rapidity of connecting physical objects around us to the internet is mounting swiftly. The inanimate physical devices can be empowered to create, receive, and exchange data into information networks to provide highly developed intelligent services without any human intervention. The future IoT applications are extremely promising with an enhanced level of automation, efficiency, and comfort for the users. To actualize such a world in an ever-developing mold requires enhanced user privacy, authentication, and high information security from attacks. This paper presents a characteristic examination of an anomaly detection system for successfully classifying hostile events by distinguishing them from normal activities within the distributed Smart Space Orchestration System (DS2OS) traffic pattern associated with network operations. We presented a supervised meta algorithm-based approach known as Bagging (Bootstrap Aggregation) for classifying and handling various malicious operations on the network. This paper describes most affluent machine learning classifiers k-Nearest Neighbor(k-NN), Decision Tree(DT), Random Forest(RF), and Extra Trees Classifier(ETC) to progress the efficiency of the DS2OS dataset by acquiring through a proper training phase for the static idealization of normal and anomalous network behavior. This paper estimates the overall experimentation performance and evaluation of the simulation model through accuracy, precision, recall, and F1-score. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： internet of things

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Cloud technologies in the education system 1

Cloud technologies in the education system

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1st international Scientific conference on Advances in Science, Engineering and Digital Education, ASEDU 2020

作者： Mentsiev, Adam U Magomaev, Tamirlan R Dauletukaeva, Kamila D Faculty of Information Technology Chechen State University 32 Sheripov Street Grozny 364024 Russia Department of Information Systems in Economic Institute of Digital Economics and Technological Entrepreneurship GTSU named after Acad. M. D. Millionschikov 100 Isaev Avenue Grozny 364061 Russia Department of Literature Chechen State Pedagogical University 62 Isaev Avenue Grozny 364068 Russia

distributed computing is turning into an adoptable innovation for a considerable lot of the associations with its dynamic versatility and utilization of virtualized assets as help through the internet. It will probably significantly affect the instructive condition later on. distributed computing is an incredible option for instructive organizations that are particularly under spending deficiency to adequately work their data frameworks without spending any more capital on the PCs and system gadgets. Universities exploit available cloud-based applications offered by specialist organizations and empower their clients/understudies to perform business and scholarly errands. In this paper, we will survey what the distributed computing foundation will give in the instructive field, particularly in the universities where the utilization of PCs is more severe and what should be possible to expand the advantages of typical applications for understudies and instructors. © Published under licence by IOP Publishing Ltd.

关键词： distributed computer systems

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