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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Optimizing Renewable Energy Integration in Smart Grids through IoT-Driven Management Systems 2

Optimizing Renewable Energy Integration in Smart Grids throu...

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2nd International conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Dankan Gowda, v. Surya, S.G. Kumar, N.M.G. Prasad, K.D.v. Satya Prasad, v.K. Kaur, Mandeep BMS Institute of Technology and Management Department of Electronics and Communication Engineering Bangalore India SCMS School of Engineering and Technology Karukutty Department of Computer science and Engineering Kerala Ernakulam India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhar Pradesh Tirupathi India Symbiosis Institute of Business Management Department of Research Hyderabad India Pune India Symbiosis Institute of Business Management Hyderabad India Punjabi University Department of Electronics and Communication Engineering Punjab Patiala India

ISBN: (纸本)9798350371314

The incorporation of renewable energy sources into smart grid systems does basically signify a significant move of achieving sustainable energy consumption and management. On the contrary, due to the unpredictability of renewables and the energy grids' increased complexity, there remain certain issues to be tackled. This study explores enhancing of the capacity of energy to be derived from renewable sources through IoT linked management systems. Through the utilization of modern IoT technologies which contribute towards maximizing the effectiveness of smart grid operations the study aims to accomplish this thereby enhancing the efficiency, reliability, sustainability of smart grids, which in turn facilitate the balancing of energy supply and demand and optimal energy distribution followed by the ready integration of renewable energy sources. This paper focuses on a holistic solution that includes the utilization of IoT sensors and devices to perform real-time energy monitoring, devising smart algorithms for an energy management system, and harnessing the power of cloud computing to analyze data. This part involves implementation explaining the practical steps done to work out the framework: system's architecture design, IoT devices selection, data management activities and the control mechanism's integration. Simulation results represent the ability of distributed IoT systems to advance smart grid practices by showing a decrease in energy transportation losses, AC grid stability and the integration of RES potential. Problems that come up during the course of project, like scalability and security, are described, and solutions to those problems proposed. © 2024 IEEE.

关键词： Energy efficiency

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Implementation and Analysis of Feistel and SPN Structured Ciphers -CLEFIA and PRESENT

Implementation and Analysis of Feistel and SPN Structured Ci...

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2023 IEEE International conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Ramya, K.v. Manjunatha Reddy, H.S. Raghunatha Reddy, M. Bharathi, C. Global Academy of Technology Visvesveraya Technological University Dept. of Ece Belagavi590018 India Nitte Meenakshi Institute of Technology Visvesveraya Technological University Dept. of Ece Belagavi590018 India

ISBN: (纸本)9798350300826

The investigation of interconnected devices, sensors, and various network-connected facilitates communication and information exchange. These devices are susceptible to security threats related to privacy and data exchange, especially considering the billions of inter-connected devices. Lightweight block ciphers offer a viable solution for power-constrained devices, including Radio-frequency identification (RFID) tags, ubiquitous computing, wireless sensor networks, aggregation networks, and the Internet of Things (IoT). This study implements two lightweight block ciphers, namely CLEFIA and PRESENT, both of which have FIESTEL and SPN structures respectively. Feistel structure has simple encryption and decryption algorithms and SPN is simple and easy to analyze. These ciphers are implemented using the Xilinx and Cadence tool. Implemented ciphers performance is evaluated in terms of power, area, and delay. Our findings demonstrate that PRESENT surpasses CLEFIA in terms of memory usage and security, while CLEFIA provides superior throughput compared to PRESENT. © 2023 IEEE.

关键词： Electronic data interchange

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Digika - An Application to Empower Disabilities

Digika - An Application to Empower Disabilities

引用

2024 International conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Sheela, T. Gnana Padmesh, C.K. Lokesh, v. Sri Sairam Engineering College Department Of Information Technology Chennai India

ISBN: (纸本)9798350352689

People who are physically/mentally impaired, visionary/hearing impaired, paralytic people, handicapped people, etc., are referred to the category as Disabled people. Even persons with mental retardation fall under this category. They cannot lead the life of a normal human being as they lack any or more inevitable features in them. In our day-to-day life, we come across many disabled people and students who are unable to lead normal life like us. Focusing, students with disabilities find difficulty with their schooling and studies. So, an application in their hands will aid them to live independently and take care of themselves in many aspects. To carry forward the way of living of disabled people, we proposed an application named 'Digika', exclusively for disabled persons. This app is the hub of many features like the production of graphical tactile images, identifying the particulars of the image with character detection, conversion mechanisms, etc., and is not restricted to one of the disabilities with the features and algorithms of Machine learning, deep learning, Convoluted Neural Networks, and Computer vision. Finally implementing the concept of the Internet of Things with the help of appropriate sensors for educating disabled students and controlling those via this application. The application developed can be effortlessly installed on a diverse range of devices, promoting its portability to wearable platforms like Wear OS and Android Wear. Given its open-source nature, this application is poised to significantly enhance the overall quality of life for individuals with disabilities. © 2024 IEEE.

关键词： Computer vision

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Home Electricity Management System 15

Home Electricity Management System

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15th International conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： vikram, S. Yuvaraj, v. Prakaash, Suriya Babu, Bindu Easwari Engineering College/ECE Chennai India

ISBN: (纸本)9798331300579

Home Electricity Management System is a smart and innovative solution designed to efficiently manage and monitor the usage of electricity within households. This system aims to provide users with the ability to actively control their energy consumption, promote energy savings, and reduce electricity costs. The system consists of several key components such as smart meters, real-time energy monitoring devices, and a user-friendly mobile application. Smart meters are installed to accurately measure electricity usage and transmit the data to the system. Real-time energy monitoring devices are integrated to provide users with real-time information about their energy consumption, allowing them to make informed decisions and adjust their usage accordingly. The user-friendly mobile application serves as a central platform where users can conveniently monitor, manage, and optimize their electricity consumption. Through the application, users can set personalized energy goals, receive real-time energy usage updates, and access informative tips and suggestions for energy conservation. Moreover, the system incorporates advanced analytics and machine learning algorithms to provide users with personalized recommendations on how to further optimize their energy usage. By using this Home Electricity Management System, households can gain greater control over their energy consumption, contribute towards a greener environment, and significantly reduce their electricity bills. © Grenze Scientific Society, 2024.

关键词： Smart meters

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Revolutioning WSN: Experimental Design of an Energy Efficient Communication Protocol Using Improved BEE Colony Clustering Model 3

Revolutioning WSN: Experimental Design of an Energy Efficien...

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International conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Priyaradhikadevi, T. vijayakumari, P. Balaji, M. Chinnammal, v. vijay, Sreeja Mailam Engn Coll Dept Comp Sci & Engn Mailam India Sairam Engn Coll Chennai India Mohan Babu Univ Dept ECE Erstwhile Sree Vidyanikethan Engn Coll Tirupati India Rajalakshmi Inst Technol Dept ECE Chennai India SA Engn Coll Dept ECE Chennai India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Wireless sensor networks (WSNs) can benefit from improved network lifetime and energy efficiency when routing algorithms are designed using clustering of sensor nodes. The main nodes in clustered WSNs, known as cluster heads, need more power since they have more work to do. As a result, finding the best cluster heads is a challenging issue. One way to reduce power consumption in WSNs is by using multi-hop routing protocols. But, because all nodes consume energy in an uneven manner, the network lifespan is still degraded. Consequently, in order to assess the efficacy of the suggested approach, we cross-validate it with the conventional model known as Artificial Bee Colony (ABC) and present an enhanced bee colony optimization model referred to as the Improved Bee Colony Clustering Model (IBCCM). The suggested IBCCM routing scheme aims to optimize energy consumption in wireless sensor networks. This is crucial because wireless network and IoT devices have limited energy reserves. Another goal is to make the network more adaptable so that it can smoothly adapt to unexpected changes, reducing disruptions and optimizing overall network performance. algorithms take into account associated goals with informative-shaped rewards to speed up learning. By lowering data delivery delay and improving the packet delivery ratio compared to existing routing protocols, we showed via the different simulations that our approach enhanced energy efficiency and allowed for quick adaptability to unanticipated changes in the network. Using priority path selection, the nearest nodes with the lowest energy consumption are located instantly. Priority two is given to nodes with the smallest link costs if the minimum energy between their neighbours is equal. In various instances, the suggested technique is tested to see how well it performs. The simulation results demonstrate that the suggested protocol outperforms other comparable protocols that have been developed recently.

关键词： WSN Energy Efficiency Communication Protocol Improved Bee Colony Clustering Model IBCCM Artificial Bee Colony ABC Wireless Networks

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A Systematic Review on Fog computing Security algorithms on Current IoT Applications and Solutions 1st

A Systematic Review on Fog Computing Security Algorithms on ...

引用

1st International conference on Deep Sciences for computing and Communications, IconDeepCom 2022

作者： Balaji, v. Selvaraj, P. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chengalpattu Dt TN Chennai603203 India

ISBN: (纸本)9783031276217

Fog computing models consist of a cloud-like platform that possesses different layers like application services, data, storage, and computation. Fog computing and cloud computing were considered similar in multiple aspects although the cloud operates as a centralized infrastructure whereas Fog computing is a distributed decentralized system. With Fog systems a large amount of data can be processed and operated through the on-premise equipment. Fog systems can be installed on heterogeneous hardware. It is indispensable to have Internet of Things (IoT) devices with rapid processing and transmitting capabilities. Several security issues about monitoring, data, malware, segregation, virtualization, and network are intensified by this broad range of functionality-driven applications. This survey focuses on the modern applications of Fog computing to identify the research gaps in terms of security. Technologies such as Micro-data centers and Cloudlets have also been analyzed in this work. Many of the applications of Fog computing overlook the aspect of security and more attention is given only to the improved functionality. This survey also discusses the consequences of the vulnerabilities and possible solutions, as well as the improved level of security measures in Fog systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fog computing

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Quantum Neural Networks and Their Potential in Traffic Prediction

Quantum Neural Networks and Their Potential in Traffic Predi...

引用

2023 Intelligent Technologies and Electronic devices in vehicle and Road Transport Complex, TIRvED 2023

作者： Moskvin, v.S. Itmo St. Petersburg Russia

ISBN: (纸本)9798350317626

This paper explores the application of Quantum Neural Networks (QNNs) in the field of traffic prediction, an integral aspect of modern transportation management. Traditional machine learning methods like neural networks, decision trees, and support vector machines have been commonly used for traffic prediction. However, the advent of quantum computing offers promising new directions. QNNs, embodying principles of quantum mechanics, have the potential to handle large, complex traffic datasets with greater efficiency and possibly provide more accurate predictions. Despite being in the early stages of research and development, the potential advantages of QNNs in terms of handling complexity, leveraging quantum superposition, entanglement, and interference make them a compelling area of study. Nevertheless, the practical implementation of QNNs for traffic prediction entails overcoming significant challenges related to quantum computing hardware and the development of new quantum machine learning algorithms and frameworks. Ultimately, QNNs could revolutionize traffic prediction and numerous other fields. © 2023 IEEE.

关键词： Decision trees

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An Implementation of MMSE Demodulation Algorithm with Reduced Computational Complexity and without Performance Loss

An Implementation of MMSE Demodulation Algorithm with Reduce...

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2024 Systems of Signal Synchronization, Generating and Processing in Telecommunications, SYNCHROINFO 2024

作者： Kreyndelin, v.B. Grigorieva, E.D. Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798350373448

The estimation (demodulation) algorithm, optimal for the criterion of the minimum mean square error (MMSE algorithm), is very often used various areas. In the process of developing communication systems, there is a problem data dimension increase. As a result, the dimension of the matrices and vectors included in the MMSE algorithm increases. This, in turn, leads to an increase in the load on computing devices that implement signal processing algorithms, including the MMSE algorithm. Therefore, it needs to reduce the complexity of the MMSE algorithm while maintaining its noise immunity. The paper proposes a new simplified implementation of the MMSE algorithm, based on the use of various known fast algorithms for multiplying complex numbers and matrices, as well as matrix inversion. In this case, if possible, the properties of matrices are used, for example, the properties of Hermitian and symmetry. In the previous work of the authors, a simplified implementation of the MMSE algorithm has already been proposed. This work proposes its further development. The approach to implementing the MMSE algorithm described in this work allows to reduce a computational complexity in 1.3... 2.15 times (with dimensions of matrices and vectors from 32 to 1024) compared to its traditional implementation without using fast algorithms. The additional gain in computational complexity is 1.07... 1.15 times. These gains are due only to the organization of the computational process and are achieved without reducing the noise immunity of the MMSE algorithm. © 2024 IEEE.

关键词： Mean square error

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Remote Diabetic Retinopathy Screening with IoT and Machine Learning on Edge devices 1

Remote Diabetic Retinopathy Screening with IoT and Machine L...

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1st International conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Deshmukh, Ram Shalini, S. vani, v Divya Kn, Narasimha Murthy Alzubaidi, Laith H. Raj, vijilius Helena School of Engineering Sr University Telangana Warangal India J.N.N Institute of Engineering Department of Mathematics India Institute of Aeronautical Engineering Hyderabad Dundigal India Department of Civil Engineering Karnataka Bengalore India The Islamic University Najaf Iraq New Horizon College of Engineering Department of Applied Sciences Bangalore India

ISBN: (纸本)9798350330915

This study presents a novel method of screening for diabetic retinopathy using edge devices the Internet of Things and machine learning. The developed remote screening system ensures broad accessibility as well as affordability by overcoming geographical barriers. While edge computing maximizes real-time analysis, the integration of sophisticated machine learning algorithms improves diagnostic accuracy. The investigation of socio-technical subtleties is guided by the interpretivist philosophy. The outcomes show a strong architecture, effective models, as well as revolutionary effects on accessibility. A critical assessment finds the good points and continuous improvements. Suggestions place a strong emphasis on scaling issues and the ongoing improvement of machine learning models. In order to secure data management and keep up with changing healthcare needs, future research suggests combining blockchain technology with sophisticated imaging modalities. This study advances early detection, enhances accessibility to healthcare, and advances remote screening technologies. © 2023 IEEE.

关键词： Diabetic Retinopathy Edge computing IoT (Internet of Things) Machine Learning Remote Healthcare

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Automated Tea Leaves Recognition: Multi-Classification Using YOLOv5 and Inception v3 Model 14

Automated Tea Leaves Recognition: Multi-Classification Using...

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

作者： Joshi, Naman Kumar, Deepak Kukreja, vinay Sarangi, Pradeepta Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350335095

The quality and productivity of tea harvests around the world are greatly impacted by tea leaf diseases. To stop the spread of these diseases and put effective control measures in place, early and precise diagnosis is essential. Deep learning algorithms have become efficient devices for image classification tasks in recent years, opening up exciting possibilities for disease identification in agricultural sectors. This study focuses on identifying tea leaf conditions using the YOLOv5 and Inception v3 models. The research technique required gathering a broad dataset that included photos of both healthy and diseased tea leaves. A total number of 6556 patches in the form of different red rust, red spider, sunlight scorching, and healthy tea leaves have been extracted which have been used for training and testing purposes in the Inception v3 model. Several hyperparameters such as epochs, batch size, and momentum are initialized to achieve high performance. The YOLOv5 model achieves the highest mAP at 0.78 with a threshold value (0.5). During testing, the Inception v3 model achieves a high F1-score (94.3%) in the Red rust tea leaves classification. The outcomes showed the promise of the improved Inception v3 model as a trustworthy tool for disease identification in tea plantations and the efficiency of the model in detecting tea leaf illnesses conditions. Future applications for the multi-classification of tea leaves using the YOLOv5 and Inception v3 models are very interesting. © 2023 IEEE.

关键词： Image classification

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