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

共 8045 条记录，以下是51-60 订阅

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Investigating the Advantages and Limitations of Math Modeling for Wireless sensor networks 5th

Investigating the Advantages and Limitations of Math Modelin...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Zaidi, Taskeen Ansari, Tabish Sindhuri, Bonda Prema Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

this paper investigates the blessings and obstacles of math modeling for wireless sensor networks (WSNs). WSNs may be immediately evaluated on a performance basis or analyzed to address protection and reliability troubles. However, mathematical models ignore the sensors’ heterogeneity and the networks’ distributed nature. Similarly, the mission is to validate the results to ensure actual performance. Other WSNs that need attention and mathematical modeling include their management algorithms, optimizations, MAC protocols, power management, and *** comparison, sure advantages exist in math modeling, understanding complex problems, coming across new solutions and situations, and the capacity to expect effects. this paper aims to review the advantages and obstacles of math modeling for WSNs and concludes by providing hints on how WSNs can gain from mathematical modeling. Math modeling holds the splendid capacity for wireless sensor networks (WSNs) because it comprehensively clarifies complex actual-global troubles. Math modeling offers an established framework to conceptualize, examine, and endorse answers for diverse WSN deployments. Its relative predictability and simulation accuracy can assist in perceiving hidden insights and generate reliable outcomes. However, it has some critical boundaries. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： modeling sensor simulation

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Wavelet-Based Time Series Forecasting for Wireless sensor networks 5th

Wavelet-Based Time Series Forecasting for Wireless Sensor Ne...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Murugan, R. Arya, Rakesh Kokila, S. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Wavelet-primarily based time series forecasting has been an effective device for Wi-Fi sensor network (WSN) packages. this approach has proven to provide correct forecasts for a selection of situations whilst enabling strength performance through decreased statistics storage and transmission bandwidth necessities. By means of extracting the vital time-related functions from sensor facts streams, records compression is executed at the same time as retaining the accuracy of the forecasts. Furthermore, with the aid of taking advantage of the multi resolution decomposition belongings of wavelets, this method can appropriately seize the temporal dynamics of the authentic signal. this permits better approximation of the underlying signal’s temporal correlations while as compared to other kinds of forecasting strategies. Additionally, wavelet-primarily based methods permit robust and green adaptation of the prediction fashions to changing sensor records, for this reason adapting the overall performance of the forecasts to exclusive situations. All these homes make wavelet-based time series forecasting an appealing choice for WSN programs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless sensor networks

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Blockchain-Based Multi-target Distributed Passive Localization 5th

Blockchain-Based Multi-target Distributed Passive Localizati...

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5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Song, Zijian Zhu, Jing Zheng, Hongyuan Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

this paper investigates the multi-target localization in distributed passive sensor system in terms of blockchain technology. In this distributed framework, each sensor captures and analyzes the angle measurement data of targets locally, and uploads the results onto blockchain network for coordination among nodes. Subsequently, smart contracts automatically associate data obtained from external sensors to establish an estimate of the target location. We deploy a novel position estimation smart contract using Remix on Ganache private blockchain. Meanwhile, we conduct simulations of the Electronic Support Measures (ESM) sensor and Angle of Arrival (AoA) measurement process using Matlab. Finally, Experimental simulations verify the effectiveness and feasibility of the proposed algorithm for target localization in distributed passive sensor networks leveraging blockchain technology.

关键词： Distributed Passive Localization AoA ESM sensor Blockchain Smart Contracts

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A Transformer-based framework for non-Cartesian MRI reconstruction 5

A Transformer-based framework for non-Cartesian MRI reconstr...

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

作者： Ji, Wuzheng Zhang, Ze Tan, Huiyuan Yang, Wenhui Wang, Hui Liu, Xin Wang, Qiuliang University of Science and Technology of China Anhui Hefei230026 China Ganjiang Innovation Academy Chinese Academy of Sciences Jiangxi Ganzhou341000 China Institute of Electrical Engineering Chinese Academy of Sciences Haidian Beijing100190 China University of Chinese Academy of Sciences Huairou Beijing101408 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Non-Cartesian reconstruction is a crucial technique for accelerating MRI. However, traditional non-Cartesian reconstruction algorithms often result in suboptimal image quality. Recently, deep neural networks have emerged as powerful tools for MRI reconstruction, yet their application to non-Cartesian acquisitions remains underexplored. Transformer-based approaches have shown impressive performance in image super-resolution, prompting us to explore their potential in this domain. To tackle these challenges, this paper introduces a novel framework that combines non-Cartesian image reconstruction techniques with a Transformer-based network. the proposed framework comprises nonuniform Fourier transform, image feature extraction, and image reconstruction modules. To assess the effectiveness of our approach, we performed experiments using the single-coil knee dataset from fastMRI. Compared to other methods, our proposed approach demonstrated a 2.024 dB improvement in PSNR and a 0.117 increase in SSIM under a 4x accelerated radial undersampling condition. © 2025 SPIE.

关键词： Deep neural networks

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Optimizing Wireless sensor Network Connectivity Using Math Modeling Techniques 5th

Optimizing Wireless Sensor Network Connectivity Using Math M...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Gobi, N. Bhatnagar, Gunjan Bhavana, A. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Wi-Fi sensor networks (WSNs) are extensively deployed in various programs and have an increasing significance within the net of things (Iota). However, their connectivity remains a chief challenge, especially in large-scale networks, as it's far crucial for reliable communication and green statistics aggregation. To address this project, this paper presents a unique method to optimize the connectivity of WSNs through utilizing mathematical modeling techniques. Mainly, nonlinear optimization methods are hired to acquire a surest deployment sample for a given community configuration that maximizes the connectivity metric. the proposed method is proven to provide better connectivity outcomes than conventional solutions and is analyzed each theoretically and empirically with extensive simulations. Simulation outcomes additionally show that the method is robust to various node deployment techniques. Furthermore, it is evaluated using exceptional styles of environments and a huge variety of community configurations. the offered outcomes demonstrate the versatility and scalability of the proposed optimization method in optimizing the connectivity of WSNs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： sensor nodes

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Objective Functions in High-Density Internet of things networks - A Performance Evaluation 5th

Objective Functions in High-Density Internet of Things Netwo...

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5th international conference on Computing Science, Communication and Security

作者： Gul, Safia Malik, Bilal Ahmad Banday, M. Tariq Univ Kashmir Srinagar India

ISBN: (纸本)9783031751691;9783031751707

the Internet of things has sparked a profound revolution in networking by enabling the collection of vast sensor data, serving as the foundation for smart applications such as homes, cities, and industries. However, achieving such solutions is challenging due to resource constraints such as limited computational power, storage, and memory in IoT devices. the routing protocol (RPL), the most commonly utilized routing algorithm, employs objective functions (OFs) to determine path selection based on diverse metrics. the accurate evaluation and testing of these OFs are paramount in developing effective routing solutions. this paper thoroughly analyses the results of testing standard objective functions, focusing on addressing load imbalance and congestion issues. the analysis aims to ascertain the suitability of existing standardized objective functions as optimal routing solutions for IoT networks, given the results of the challenges posed by load balancing. this paper evaluates the efficacy of various objective functions used in RPL, providing insights to improve the routing performance and evaluate IoT devices' reliability, energy efficiency, load balancing, and overall network performance. the results of testing standard objective functions using the Contiki OS tool are meticulously compared. the analysis seeks to assess the suitability of the existing standardized objective functions as the most optimal routing solution for IoT networks.

关键词： RPL DODAG OF0 MRHOF Congestion

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Research on air interface interference recognition in mobile communication networks based on LSTM autoencoder 5

Research on air interface interference recognition in mobile...

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

作者： Sun, Hongwei Hu, Mingwei Wang, Baoji Chen, Yue Fu, Jingwen Huang, Jia Jiangsu Automation Research Institute Lianyungang222006 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100094 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Sankuai Online Technology Co. Ltd. Beijing100102 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Due to the diverse nature of interference signals in mobile communication frequency bands, feature extraction is challenging, and the rapid and automated identification of behaviors such as illegal frequency usage and malicious interference are difficult. Moreover, evaluating the precise impact of interference signals on communication networks is challenging. In response to this problem, this paper proposes an air interface interference recognition technology for communication links based on base station measurement reporting data. It constructs an interference recognition model to achieve automatic interference identification and evaluates the accuracy of interference recognition. On the base station side, this paper utilizes channel measurement data such as signal strength, signal quality, and signal-to-noise ratio exported by the base station. It uses a Long Short-Term Memory (LSTM) autoencoder model to learn normal signal patterns for interference discrimination and evaluates the impact of interference on communication networks. this method achieves precise identification of fixed-frequency interference on the base station side and evaluates the impact of interference on communication networks. Experimental results show that the base station-side approach achieves an F1 score of 0.99 in identifying fixed-frequency interference, outperforming One-Class Support Vector Machine (OCSVM), Principal Component Analysis (PCA), and Isolation Forest (IForest) methods under similar conditions. © 2025 SPIE.

关键词： Base stations

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5th international Symposium on Signal and Image processing, ISSIP 2024

5th International Symposium on Signal and Image Processing, ...

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5th international Symposium on Signal and Image processing, ISSIP 2024

ISBN: (纸本)9789819795147

the proceedings contain 31 papers. the special focus in this conference is on Signal and Image processing. the topics include: A Blockchain-Based Secure Framework for Storage and Access of Surveillance Video Data;encountering the Challenges and Awareness of Internet Security;Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights;Analyzing and Visualizing AI Decision-Making for Human-Centered Interaction and Trust;AI-ML-Based Handover Techniques in Next-Generation Wireless networks;castle Defender: Design and Implementation of Game Based on Pygame;optimizing Healthcare: Enhancing Disease Management with Recommendation Systems;optimizing Diabetes Prediction Models for Enhanced Health Data processing;comparative Analysis of Classification Models Using Various Feature Sets;An Early Detection of Tomato Plant Disease with Deep Reinforcement Learning and CNN;ioT-Powered Health Monitoring System for Protecting Vital Organs through Cloud-Based Diagnosis;LASKV: Lightweight Authenticated Session Key Agreement for Video Surveillance System;Data Analyses of Factors Influencing Sustainable Adoption of Online Public Services: An Extended UTAUT Analysis in North Macedonia;a Comprehensive Review on Real-Time Campus Compass;deep Learning Approaches for Early Detection of Bovine Respiratory Diseases in Cattle;identifying Suitable Cloud Storage Services from Cross Cloud Platforms Based on the Requirements;storage-Savvy Frame Recorder: Enhancing Storage Efficiency and Inspection Speed*;lossless Medical Image Compression Using Block-Wise Burrows–Wheeler Transform and Modified Run Length Encoding;advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software Systems;a New Way to Communicate: Deep Learning for Deaf and Dumb People;dependent Binomial: A Family of Distributions Derivable by Modifying a Base.

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Leveraging Exponential Smoothing for Time Series Analysis of Wireless sensor networks 5th

Leveraging Exponential Smoothing for Time Series Analysis of...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Ojha, Ananta Verma, Tushar K. Venkatachalam, Amirtha Preeya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

the abstract should summarize the contents of the paper in short terms, i.e. 150–250. this paper explores the applications of exponential smoothing techniques in evaluating time collection records generated using Wireless sensor networks (WSNs). Empirical results demonstrate that exponential smoothing outperforms conventional time series models, supplying users with extra accurate analysis and forecasting of WSNs. the essential blessings of this method encompass decreased errors in predicting destiny values, progressed reliability, and stepped forward performance in developing forecasts. this paper affords a comprehensive review of exponential smoothing and its packages in WSNs, including an assessment of the outcomes of applying those techniques and the capability of future research avenues. In addition, it gives hints for practitioners trying to leverage exponential smoothing for time series analysis of WSNs. Exponential smoothing is a critical time series forecasting method that may be applied to evaluate Wi-Fi sensor networks (WSN). It is miles primarily based on the belief that the fashion in a time collection is a clean continuation of past values. the rules work by calculating an exponentially weighted shifting average, wherein facts points are exponentially weighted and given more importance the closer they are to the present-day time point words. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： exponentially smoothing weighted

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Review of identification technologies for stray animal governance 5

Review of identification technologies for stray animal gover...

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

作者： Han, Puze College of Science and Engineering University of Minnesota Twin Cities Minneapolis55455 United States

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In recent years, societal changes have led to a growing prominence of pets in people's lives. However, uncontrolled pet reproduction in urban areas has given rise to a significant issue of stray animals, posing serious threats to human health and the environment. Conventional manual methods for counting stray animals face challenges in terms of efficiency and the risk of disease transmission. With technological advancements, image recognition, and sound identification, among other techniques, have emerged as crucial tools to address this issue. Image recognition, leveraging intuitive statistics based on external features, combined with the low-power attributes of sound identification and the health assessment capabilities of thermal imaging, collectively provide comprehensive technological support for stray animal population statistics. In the realm of image algorithms, both traditional target detection algorithms and deep learning methods such as RCNN and Faster RCNN employ convolutional neural networks to accurately identify and locate stray animals. Regarding sound algorithms, traditional Gaussian mixture models and hidden Markov models, as well as deep learning techniques involving convolutional neural networks, have effectively enhanced the accuracy of stray animal sound recognition. the integration of image and audio in a hybrid method significantly enhances stray animal monitoring. Employing advanced techniques in video tracking and sound recognition, this approach offers an efficient and practical solution, crucial for wildlife ecosystem surveillance and conservation. Research indicates that the application of deep learning methods in the domains of image and sound has significantly advanced compared to traditional approaches. In terms of image processing, I utilized the YOLO algorithm to perform grid division, feature extraction, and loss computation steps to achieve stray animal detection, demonstrating outstanding performance. through the application of t

关键词： Animals Detection and tracking algorithms Object detection Animal model studies Image processing Education and training Diseases and disorders Positron emission tomography Deep learning Expectation maximization algorithms

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