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检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

共 5102 条记录，以下是91-100 订阅

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Federated Learning and Cloud Integration for Enhanced Anomaly Detection in Wireless sensor Networks using LSTM Models and Raspberry Pi Node 3

Federated Learning and Cloud Integration for Enhanced Anomal...

引用

3rd ieee World conference on Applied Intelligence and computing, AIC 2024

作者： Gayathri, S. Surendran, D. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Sathyamangalam638401 India Karpagam College of Engineering Othakkal Mandapam Department of Information Technology Tamil Nadu Coimbatore641032 India

ISBN: (纸本)9798350384598

Wireless sensor Network (WSN) is increasingly utilized for monitoring and surveillance in various domains, including environmental monitoring, industrial automation, and healthcare. However, the sheer volume of data generated by WSN poses challenges for anomaly detection, especially in resource-constrained environments. This paper proposes a novel approach that combines federated learning and cloud integration to address the challenges of anomaly detection in WSN. Leveraging federated learning allows for collaborative model training across distributed sensor nodes while preserving data privacy. Cloud integration provides scalable computational resources for model aggregation and storage, enabling efficient anomaly detection. The implementation utilizes Long Short-Term Memory (LSTM) models, well-suited for capturing temporal dependencies in sensor data. Raspberry Pi-based WSN nodes are employed for their low-power consumption and flexibility, enabling deployment in diverse environments. Communication between sensor nodes and the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, ensuring efficient data transmission. Cloud integration is realized using Microsoft azure, offering robust cloud infrastructure for model aggregation and management. TensorFlow Federated (TFF) framework is utilized for federated learning, enabling collaborative model training across distributed nodes. Experimental evaluation demonstrates the effectiveness of the proposed approach, achieving significant improvements in anomaly detection accuracy and efficiency. Specifically, our approach achieves up to a 20% increase in detection accuracy compared to centralized approaches. Furthermore, integrating federated learning and cloud infrastructure results in a 25% reduction in processing time, enabling real-time anomaly detection capabilities. © 2024 ieee.

关键词： sensor nodes

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

引用

3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 ieee.

关键词： distributed ledger

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Research on wide-area distributed PV regional power prediction technology 3

Research on wide-area distributed PV regional power predicti...

引用

3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Huan, Jiafei Zhang, Weitao Jiang, Jixuan Zhang, Liping Zhou, Ling Wu, Han State Grid North China Branch Dept Dispatching & Control Ctr Beijing Peoples R China State Power Rixin Technol Co Ltd Dept Power Grid Business Beijing Peoples R China State Power Rixin Technolog Co Ltd Dept Power Grid Business Beijing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The power grid in China urgently needs to strengthen the monitoring of extensive distributed PV plants and improve power prediction accuracy. The traditional single-station prediction model for centralized PV plants is not suitable for wide-area distributed photovoltaic plants, which are characterized by decentralized installation and small capacity. This paper proposes third-generation regional prediction technology for distributed PV. This technology considers the distribution of installed capacity and geographical topography, thereby avoiding issues such as differences in power generation efficiency and errors from single selection points. It proposes building a regional prediction model based on various actual data inputs, using districts and counties as the basic units. To enhance the accuracy of regional predictions, the average daily root mean square error (RMSE) accuracy rate has reached more than 85%.

关键词： renewable energy distributed PV power prediction regional forecast model

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Deep Learning Framework for Sign Language Recognition Using Inception V3 with Transfer Learning 3

Deep Learning Framework for Sign Language Recognition Using ...

引用

3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kirubavathi, G. GuruAkshya, C. Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Sign language recognition poses unique difficulties due to the complexities of hand gestures and movements. Real-world applications often require robust models capable of handling diverse environmental conditions and variations in signing styles. Transfer learning, a technique that utilizes knowledge from pre-trained models, has emerged as a promising method to address these challenges. Although transfer learning has been successful in various visual recognition tasks, its application to American Sign Language (ASL) recognition remains relatively unexplored. This research showcases the effectiveness of combining transfer learning with advanced CNN architectures like Inception V3 for American Sign Language (ASL) recognition. Through fine-tuning Inception V3, adjusting learning rates, applying data augmentation, and rigorous evaluation, the model achieved an impressive 99.12% accuracy on test datasets. These findings represent a significant improvement in ASL recognition accuracy compared to baseline models. The success of this method underscores the importance of fine-tuning and data augmentation in advancing accessible and reliable sign language recognition technology for practical use. © 2024 ieee.

关键词： Deep learning

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Research on Optimal Allocation of Integrated Resources of Mergers and Acquisitions Based on distributed Cloud computing 3

Research on Optimal Allocation of Integrated Resources of Me...

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3rd international Joint conference on Information and Communication Engineering (JCICE)

作者： Zhou, Lifen Guangdong Baiyun Univ Sch Accountancy Guangzhou Peoples R China

ISBN: (纸本)9798350388732;9798350388725

This paper proposes a scheduling algorithm based on fast edge resource placement, so as to further improve the effect of bilateral resource integration of the mergers and acquisitions (M&A). Among them, the random demand model is taken as the basic resource scheduling method, and the algorithm is optimized by introducing edge computing and user random demand, thereby further improving the resource scheduling effect of mergers and acquisitions. The simulation results show that under different merger and acquisition resources, Zipf values, and types of resources, compared with traditional mean-based scheduling algorithms, the scheduling algorithm based on fast edge resource placement have higher resource scheduling benefits and significantly reduced time complexity. Therefore, it can be concluded that the scheduling algorithm based on fast edge resource placement has good performance, and it can carry out resource scheduling with good effect. In addition, applying the proposed algorithm in the actual enterprise M&A scenario can help enterprises to carry out better resource management, which is feasible and has certain practical application value.

关键词： resource scheduling distributed structure cloud computing algorithm optimization

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Automated Mining Equipment Monitoring: Enhancing Efficiency through sensor Technology and Predictive Maintenance 3

Automated Mining Equipment Monitoring: Enhancing Efficiency ...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Dharun Anand, S. Divakar, S. Cephas, I. Ganiss Roger, L. St. Joseph's Institute of Technology Department of Electrical and Electronics Engineering OMR Chennai India

ISBN: (纸本)9798331509675

The mining industry is pivotal to the economy, yet it faces significant challenges in maintaining equipment reliability and efficiency. Downtime of machinery can lead to substantial financial losses, operational delays, and safety risks. Mining operations are inherently complex, involving significant investments in machinery and equipment [1]. The efficiency and reliability of mining equipment are crucial for maintaining operational profitability and safety. With advancements in sensor technology and data analytics, automated equipment monitoring systems are becoming pivotal in enhancing operational effectiveness. This paper presents a comprehensive design for a system that utilizes sensors for real-time monitoring of mining equipment performance and health. By implementing predictive maintenance alerts, this system aims to mitigate downtime and enhance operational efficiency in mining operations [2]. © 2025 ieee.

关键词： Predictive maintenance

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Quality of Service in Wireless sensor Networks: A Comparison of Routing Protocols based on Machine Learning 3

Quality of Service in Wireless Sensor Networks: A Comparison...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Prabha, K. Lakshmi Vijayasri, P. Saumiya, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331509675

This paper focuses on the improvement of Quality of Service (QoS) in Wireless sensor Networks by using Machine Learning (ML) techniques to optimize routing protocols. It addresses the essential QoS challenges such as network lifespan, energy efficiency, delay minimization, and packet loss reduction using ML-based methods against those of conventional routing techniques. The study sheds light on how ML could be used to detect and overcome bottlenecks in the performance of a network to improve the overall efficiency. Simulation results show the massive improvements in QoS parameters, thereby strongly reaffirming the feasibility and potential of the ML-driven approaches to overcome the inherent limitations of traditional routing protocols in WSNs. © 2025 ieee.

关键词： Packet loss

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Remaining-Capacity-Based distributed Control for Harmonic Sharing in Islanded Microgrids 3

Remaining-Capacity-Based Distributed Control for Harmonic Sh...

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3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Du, Haochen Huang, Lucong Zhou, Xiang Duan, Jiaqi Hao, Yuxin Yang, Hengchen Yang, Jiadong Wang, Yang Sichuan Univ Coll Elect Engn Chengdu Peoples R China State Grid Corp China State Grid Sichuan Elect Power Co Chengdu Peoples R China

ISBN: (纸本)9798350375145;9798350375138

One of the challenges encountered by islanded microgrids is voltage distortion, which is induced by non-linear loads. The unreasonable distribution of harmonic power among distributed inverters can lead to the overloading of inverters. Existing studies achieve harmonic power sharing based on fixed value, which lacks flexibility. Therefore, this paper proposes a distributed harmonic sharing strategy which is based on remaining capacity. The main objective of this strategy is to achieve harmonic power sharing according to the remaining capacity by means of a consensus algorithm. To achieve this, harmonic power and remaining capacity are calculated in real-time, and a consensus protocol is designed to guide the adjustment of virtual impedance. Additionally, a fuzzy controller is designed to adjust the speed of virtual impedance changes based on the operating state of each inverter to achieve better dynamic performance. Finally, comparative analysis and experimental verification were conducted.

关键词： microgrids harmonic sharing fuzzy control consensus protocol

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Enhanced Soil Nutrient-NPK Measurement Using Electrical Conductivity and Temperature 3

Enhanced Soil Nutrient-NPK Measurement Using Electrical Cond...

引用

3rd international conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Dattatreya, Sneha Sain, Manoj Khurana, Archit Jena, K. Chatterjee, Gaurav The Lnmiit Department of Ece Jaipur India

ISBN: (纸本)9798331528201

To satisfy the growing demand of population, opti-mizing the level of soil micronutrients, i.e., Nitrogen (N), Phosphorus (P), and Potassium (K) (known as NPK), plays a crucial role. However, the traditional methods for determining the soil nutrient level are accurate but time-consuming, expensive, and laborious. This study uses a deep learning approach to predict the soil NPK levels, holding electrical conductivity (EC) and temperature (T) as inputs and using a 7-in-1 soil sensor. A dataset was developed using different soil samples. A neural network model was developed to train and test the discovered dataset. The model achieved promising results with 99% accuracy with lower mean squared error (MSE) value, demonstrating rapid and accurate soil nutrient sensing potential. This research provides a cost-effective solution to model a soil sensor to streamline precision agriculture and optimize fertilization techniques. © 2024 ieee.

关键词： agriculture deep learning electrical conductivity Soil NPK sensor temperature

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A Wireless sensor Networks in Localization Using Support Vector Machines 3

A Wireless Sensor Networks in Localization Using Support Vec...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Kataria, Ansh Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Location data is essential in a wide variety of contexts. The localization challenge refers to the process of determining a sensor node's physical position. Identifying unknown nodes from the known positions of existing nodes is the primary difficulty. In this work, we offer a fuzzy-logic based range-free localization strategy. Received strength of signal (RSS) from anchor nodes in close proximity to sensor nodes is utilized in the localization method, and fuzzy analysis is used to establish a relationship among RSS and distance in order to assess the precision of localization. Through comprehensive simulations, we compared our suggested technique to various current methods. The results of the simulations validate the usefulness of the suggested method. © 2023 ieee.

关键词： Fuzzy Localization Fuzzy Logic systems Localization Wireless sensor Network

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