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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8114 条记录，以下是171-180 订阅

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An Improved Privacy-Preserving Multi-factor Authentication Protocol for Wireless sensor networks 1

引用

5th international conference on Data and Information Sciences, ICDIS 2023

作者： Kanwar, Shaurya Prajapat, Sunil Gautam, Deepika Kumar, Pankaj Dharamshala176215 India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Wireless sensor networks (WSNs) have garnered significant attention for their ability to collect and transmit data in various applications. However, ensuring secure communication in WSNs poses significant challenges due to resource constraints and the inherent vulnerability of WSNs to various attacks. Recently, Renuka et al. proposed a multi-factor authentication protocol for WSNs. In this paper, we thoroughly examined Renuka et al. protocol, and several vulnerabilities are demonstrated, which namely comprise privileged insider attacks, stolen smart card attacks, and ephemeral secret leaking attacks. In addition, this paper proposes an improved protocol to increase communication security in WSNs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Wireless sensor networks

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An Energy Efficient Clustering for Underwater Acoustic sensor networks using 6G Technology 5

An Energy Efficient Clustering for Underwater Acoustic Senso...

引用

5th IEEE international conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Siddiqui, Shams Tabrez Hashmi, Syed Ghyasuddin Ali, Mohammed Maqsood Syed, Ziauddin Qidwai, Khalid Ali Salim, Abu Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia Applied College Jazan University Department of Business Administration Jazan Saudi Arabia

ISBN: (纸本)9798350351378

these underwater Acoustic sensor networks (UWSNs) present unique challenges due to the harsh and dynamic underwater environment, making efficient communication protocols essential for their operation. this paper introduces an innovative Energy Efficient Clustering Routing Protocol (EECRP) specifically designed for UWSNs, leveraging the advancements of 6G technology. EECRP employs a distributed clustering mechanism and energy-efficient routing strategy to optimize energy consumption, prolong network lifetime, and enhance communication reliability. the protocol integrates with 6G technologies, such as terahertz communication and quantum communication security, to further enhance performance and security in UWSNs. through simulation and comparative analysis, EECRP demonstrates superior performance compared to existing routing protocols in terms of energy efficiency, throughput, and scalability. EECRP's contributions lie in its novel approach to routing in UWSNs, emphasizing energy efficiency, sustainability, and integration with advanced technologies. this research paves the way for the adoption and deployment of EECRP in various underwater applications, facilitating scientific research, environmental monitoring, and technological innovation in underwater communication systems. © 2024 IEEE.

关键词： Scalability

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Study on the application of transfer learning in small sample image classification of military equipment 5

Study on the application of transfer learning in small sampl...

引用

5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Lu, Leiji Yu, Hongxia Xiao, Hongju Bao, Lei Department of Information Engineering Army Academy of Artillery and Air Defense Hefei China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

To address the issue of insufficient military equipment sample data, which cannot meet the training requirements of deep neural networks and tends to cause overfitting, this paper introduces transfer learning technology to solve the small-sample classification problem for military equipment. By constructing a multi-type sample training set and fine-tuning the convolutional layers of pre-trained models, specific target classifiers are trained. Practice has proven that the application of transfer learning in small-sample classification tasks saves model training time, resolves issues related to model overfitting and strong dependence on data labels, and effectively improves the accuracy of image classification based on deep learning for small samples of military equipment. © 2025 SPIE.

关键词： Deep neural networks

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Image Generation Using AI with Effective Audio Playback System 5

Image Generation Using AI with Effective Audio Playback Syst...

引用

5th IEEE international conference for Emerging Technology, INCET 2024

作者： Inbavalli, A. Sakthidhasan, K. Krishna, G. IFET College of Engineering Tamilnadu Villupuram India Department of CSE IFET College of Engineering Tamilnadu Villupuram India

ISBN: (纸本)9798350361155

this paper presents an innovative approach to image generation and audio playback through the fusion of deep convolutional neural networks (CNNs) and natural language processing. We propose a system that generates textual captions from images and converts these captions into high-quality audio narratives. the deep CNN model is trained to understand and describe visual content effectively. We then integrate a text-to-speech system to convert these descriptions into audio, ensuring a seamless synchronization with the images. Our experiments demonstrate the system's capability to produce accurate and contextually relevant audio descriptions, enhancing accessibility and user experience for visually impaired individuals. the application of this technology spans across various domains, including digital content accessibility, educational materials, and assistive technology. this paper discusses the methodology, experimental results, challenges, and potential applications, highlighting the significance of this innovative approach in bridging the gap between visual and auditory perception. © 2024 IEEE.

关键词： Convolutional neural networks

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On Distributed Simulation Modeling of Large Scale networks 5

On Distributed Simulation Modeling of Large Scale Networks

引用

5th international Science and Technology conference Modern Network Technologies, MoNeTec 2024

作者： Tyushev, M.V. Smelyanskii, R.L. Moscow State University Faculty of Computational Mathematics and Cybernetics Moscow Russia

ISBN: (纸本)9798350369007

this paper addresses the challenges in distributed simulation modeling of large-scale networks (LSIM - consist of hundreds of thousands of nodes). the focus is on the efficient mapping of LSIM onto a distributed simulation environment (DSE). the main contribution is the descriptions of the solutions of the two problems: (1) clustering the LSIM graph, where each node represents a data processing point, into a clustered-graph where each node is a cluster of LSIM nodes;and (2) mapping this clustered-graph onto a DSE graph, where each vertex represents a computational server. the research operates under the assumptions that all LSIM nodes generate identical computational workloads, all DSE servers are identical, and DSE network channels have heterogeneous bandwidths. © 2024 IEEE.

关键词： Digital elevation model

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Chinese Text Error Correction Based on PE-T5 Model 24

Chinese Text Error Correction Based on PE-T5 Model

引用

5th international conference on Computing, networks and Internet of things (CNIOT)

作者： Deng, Hua Xu, Kang Li, Rongsheng Qi, Yifei State Grid Yantai Power Supply Co Yantai Peoples R China State Grid Shandong Elect Power Co Jinan Peoples R China

ISBN: (纸本)9798400716751

Chinese text error correction is to automatically detect errors or inappropriate expressions in a given Chinese text and output correct correction results according to the context and application scenarios. Existing research ideas mainly focus on two categories: rule-based and depth model-based, where depth model-based methods mainly focus on solving alignment-based errors and perform poorly on non-alignment-based errors. And with the scarcity of manually labeled data, few works have considered using self-supervised training methods to pre-train the models. therefore, for the common multiple and omitted word errors in electric power article writing, this paper proposes a generative model-based error correction method for Chinese text. A large-scale error correction dataset is first constructed using a rule-based self-supervised approach and pretrained using a generalized domain dataset. then, in the incremental training phase, lexically supervised signals are added inside the model to enhance the model's error detection effect by means of lexical combination. Finally, the model is fine-tuned using a domain-specific self-supervised dataset. the effectiveness of the method in this paper is demonstrated by analyzing the experimental results in comparison with other models.

关键词： Deep learning,Text error correction,Self-supervision T5 Natural language processing

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Human Activity Recognition Based on Neural networks and Accelerators 5

Human Activity Recognition Based on Neural Networks and Acce...

引用

5th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Hu, Xinrui Dalian Minzu University Dalian China

ISBN: (纸本)9798350368284

this study aims to recognise accelerometer-based human activities using ANN models. We used the UCI-HAR dataset, which is a widely used publicly available dataset containing accelerometer data for six daily activities (walking, walking up stairs, walking down stairs, sitting, standing and lying down). In order to improve the accuracy of the recognition, we performed detailed pre-processing of the data, including data cleaning, feature extraction and normalisation. the preprocessed data is fed into the designed ANN model. During the model training process, we used the prediction rate, recall rate and F1 score as performance evaluation metrics. the experimental results show that our ANN model can effectively recognise different activity types, showing high accuracy and robustness. this study not only provides a new approach for human activity recognition, but also provides a valuable reference for the development of intelligent health monitoring systems. © 2024 IEEE.

关键词： Deep neural networks

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G-SwinJSCC: Combining Transformer and GCN for Wireless Image Transmission 5

G-SwinJSCC: Combining Transformer and GCN for Wireless Image...

引用

5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Qi, Kechao Chen, Guolong Jia, Yun Zhang, Tianxiang School of Information and Electronic Engineering Shandong Technology and Business University Yantai China

ISBN: (纸本)9798350386943

In this paper, we have designed a more efficient visual transformer G-SwinJSCC, for semantic image transmission. Compared to most methods that rely solely on Convolutional Neural networks (CNN), we utilize a combination of Swin Transformer as the backbone network and GCN. We take advantage of the strengths of graph convolution in processing graph-structured data, and the ability of Transformers to capture global relationships and long-distance dependencies. Combining these two can fully leverage their respective advantages, extracting features from the local structure of the graph to better handle global and local features, thereby more effectively extracting the semantic features of the source data. this enhances the model's ability to handle different channel conditions. Experiments have also shown that our method has better performance compared to traditional deep JSCC, especially in high-resolution situations. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Development of Hybrid Hierarchical Routing Protocol for Wireless sensor networks 5

Design and Development of Hybrid Hierarchical Routing Protoc...

引用

5th international conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Raghunandan, G.H. Patil, Tarun Sri Srujan Hari, T. Bms Institute of Technology & Management Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331528539

Primarily employed to achieve this objective, wireless sensor networks (WSNs) are designed to track environmental or physical conditions. they obtain data from their environment efficiently and deliver it all at once to a specific location. the main issues are network longevity, usage of energy, and coordinated transmission, even though their efficacy is well established. this study proposes a unique approach to improve network endurance and address these issues. the technique seeks to improve WSN performance as a whole. In particular, a hybrid hierarchical routing system is presented to address problems with the current protocols, such as excessive energy usage in addition to high latency. For boosting the longevity of the WSN, the primary goals are to lower intra-cluster transmission and limit contact with base station. To lower quantity of energy spent while distributing to distant base stations (BS), the suggested approach encompasses gateway nodes, hybrid time synchronization, locality-sensitive hashing (LSH) based data aggregation technique, and cluster choice focused on centrality. To demonstrate effectiveness of the presented technique, a comparison with a traditional method is carried out, assessing several performance indicators. In regards to gauges of performance including latency, throughput, total dead nodes, network energy consumption, and data packets transmitted, hybrid approach outperforms conventional approaches. © 2024 IEEE.

关键词： Base stations

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Self-Supervised Graph Neural networks for Enhanced Feature Extraction in Heterogeneous Information networks 5

Self-Supervised Graph Neural Networks for Enhanced Feature E...

引用

5th international conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Wei, Jianjun Liu, Yue Huang, Xin Zhang, Xin Liu, Wenyi Yan, Xu Washington University in St. Louis St Louis United States Fordham University New York United States University of Virginia Charlottesville United States Seattle United States Nanjing China Trine University Phoenix United States

ISBN: (纸本)9798331530334

this paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, traditional GNN methods may be overly dependent on the initial structure and attribute information of the graph, which limits their ability to accurately simulate more complex relationships and patterns in the graph. therefore, this study proposes a graph neural network model under a self-supervised learning framework, which can flexibly combine different types of additional information of the attribute graph and its nodes, so as to better mine the deep features in the graph data. By introducing a self-supervisory mechanism, it is expected to improve the adaptability of existing models to the diversity and complexity of graph data and improve the overall performance of the model. © 2024 IEEE.

关键词： Self-supervised learning

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