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

共 8124 条记录，以下是391-400 订阅

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An Approach to Effective processing of Information from Smart Home sensors using Machine Learning Methods 5

An Approach to Effective Processing of Information from Smar...

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5th international conference on Control in Technical Systems, CTS 2023

作者： Saenko, Igor B. Lauta, Oleg S. Vasiliev, Nikita A. Sadovnikov, Vladimir E. St. Petersburg Federal Research Center of the Russian Academy of Sciences Saint Petersburg Russia Admiral Makarov State University of Maritime and Inland Shipping Saint Petersburg Russia Military Academy of Communications Saint Petersburg Russia

ISBN: (纸本)9798350304664

the article analyzes various machine learning algorithms that can be used to process information from sensors in a smart home system. Based on the results of the analysis, a new approach to efficient processing of information received from smart home sensors is proposed, based on the use of the MobileNet Convolutional Neural Network architecture and the TensorFlow machine learning software library, as well as the TensorBoard monitoring tool. Experiments have shown that the proposed approach allows achieving a sufficiently high accuracy and efficiency of information processing. © 2023 IEEE.

关键词： Convolutional neural networks

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Maintenance of Large Scale Wireless sensor networks 5

Maintenance of Large Scale Wireless Sensor Networks

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Zhang, Quancheng Dong, Chuchu Yu, Fengqi Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665485081

With the wide applications of large-scale wireless sensor networks (WSNs), the WSN maintenance becomes more and more significant. However, little work has been done in such area. there is even no clear definition for the WSN maintenance. In this paper, after overviewing the problem related work, we provide a problem formulation of WSN maintenance. then a series of definitions associated with WSN maintenance are given. Finally, the affect factors of WSN maintenance are analyzed and some research topics are listed. © 2022 IEEE.

关键词： Wireless sensor networks

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An optimal design for sensor radiation performance test system 5

An optimal design for sensor radiation performance test syst...

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5th international conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Chen, Demin Zhang, Xi Peking University College of Engineering Beijing China

ISBN: (纸本)9798350309034

Wireless MEMS sensors play irreplaceable roles in current internet of things (IOT). How to accurately measure their radiation performance directly determines the working distance. Current wireless sensor transmission power is normally characterized by spectrum analyzers, including SRMC (State Radio Regulatory Commission of the People's Republic of China) certification tests, from which the radiation power cannot be evaluated accurately. Here we design a wireless sensor radiation performance test system to optimize the data analysis process and obtain accurate radiation power. through introducing a 3 - grouping range method, we can identify and delete the distorted data. Further supplementing new data, accurate radiation power can be evaluated. Experimental results reproduce the predicted process that the total radiation power and the direction diagram are greatly improved when introducing the optimized test data processing. through systematic comparison with that from the third-party laboratory of the gold machine, the deviation between the data tested by our system and those from the third party is within the acceptable range, evidently indicating that the proposed method can be effectively applied for the accurate characterization for wireless sensor radiation performance, accelerating its development efficiency. © 2023 IEEE.

关键词： data optimization radiation performance wireless sensor

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Graph Neural Network-Based Malicious Node Detection to Improve the Security of Wireless sensor networks 5th

Graph Neural Network-Based Malicious Node Detection to Impro...

引用

5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Kumar, K. Khari, M. School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi110067 India

ISBN: (纸本)9789819761050

Wireless sensor networks (WSNs) are designed with small and energy-efficient sensor nodes (SNs), which are made up of microcontroller and sensors, are a critical module, which is used to collect data and to perform computations. Numerous industries include these networks, for a variety of domains like health care, monitoring, eco-friendly, business, and martial operations. though Wireless sensor networks (WSNs) are widely used due to their ability to exchange large volumes of data, their extensive usage presents unique challenges. sensor nodes (SNs), which are the basic building blocks of WSNs, play a crucial role in addressing these challenges and ensuring efficient data transmission. To preclude information breaches and failure of network, security measures are crucial. Using Artificial Intelligence (AI) methods, such as Decision Tree (DT), Support Vector Machine (SVMs), Naïve Bayes (NB), and Random Forest (RF), for detection malicious nodes based on difference between predicted and real sensor readings is a good method to increase the security of WSN. In the real-time applications, SNs aspect for anomalies, and the base station performs classification using ML methods. Conversely, existing methods have limitations which include high false positive rate and inadequate sensitivity to detect offensive patterns. therefore, the authors deploy a Graph Neural Network (GNN)-based ML approach to detect malicious nodes in order to reduce these faults and enhance the Confidentiality, Integrity, and Availability (CIA) of networks. the proposed model revealed the outcomes, which have an accuracy rate of 98% and 98% for precision, recall, and F1 support. the model executes better than prior approaches, allowing to a relative investigation, which marks an important improvement in the security of WSNs in a diverse of applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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An Overview of Detection of Forest Fires Using Intelligent Systems 5

An Overview of Detection of Forest Fires Using Intelligent S...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Choudhary, Vijaya Guha, Paramita Hussain, Shahadat Banga, Alisha Sengupta, Sudhriti Yadav, Shikha Galgotias University UP Greater Noida India CSIR-National Physical Laboratory New Delhi India Bennett University The Times Group UP Greater Noida India Amity Institute of Information Technology Auup UP Greater Noida India

ISBN: (纸本)9798350383522

Forest fire is one of the most common hazards in forests. It not only poses a threat to flora and fauna but to the entire ecology, bio-diversity, and the environment. To prevent any damage from happening, quick action is required. Robots can act as a great first responders in this regard. In this paper, different approaches to the design of mobile robots are discussed. Also, different techniques for the acquisition of data and navigation of robots are elaborated in detail. © 2024 IEEE.

关键词： Early Forest fire Detection mobile robots path planning robot locomotion sensor Nodes Wireless sensor networks

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CAVIaR crayfish algorithm enabled Deep Kronecker Network for Wormhole attack mitigation on Network Control System 5

CAVIaR crayfish algorithm enabled Deep Kronecker Network for...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Avina, A. Geetha, M.R. Department of Computer science and engineering Ponjesly College of Engineering TamilNadu Nagercoil629003 India Department of Electronics and Communication Engineering Ponjesly College of Engineering TamilNadu Nagercoil629003 India

ISBN: (纸本)9798350361155

Wireless sensor Network (WSN) is expansively employed in the Network Control System (NCS). Wormhole (WH) attack detection is a burdensome process, specifically over WSN. A distributed passive state of WH attack makes the network extremely challenging to identify. Here, the CAVIaR crayfish algorithm enabled Deep Kronecker Network (CCA-DKN) is introduced for mitigation of WH attack on NCS. Firstly, the WSN model is simulated and routing is executed by Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. then, three stages namely in-band (IB)-WH detection, out-of-band (OB)-WH detection, and Neighbour Ratio threshold (NRT) are accomplished for WH detection. After the accomplishment of the NRT stage, the transmission range stage is performed in OB-WH detection. thereafter, the Packet Delivery Ratio (PDR) and Round Trip Time (RTT) are done on IB-WH detection. Next, the detection of WH attack is performed by ResNeXt-DSAE. Furthermore, ResNeXt-DSAE is designed by combining ResNeXt and Deep Stacked Autoencoder (DSAE). Lastly, attack mitigation is executed, whereupon a percentage of data reduction is carried out by exploiting the Deep Kronecker Network (DKN) and it is trained by the CAVIaR crayfish algorithm (CCA). Furthermore, CCA is an assimilation of Conditional Autoregressive Value at Risk (CAViaR) and Crayfish Optimization Algorithm (COA). Additionally, CCA-DKN achieved high PDR and throughput of 0.769 and 0.533Mbps as well as low delay of 0.712sec. © 2024 IEEE.

关键词： Wireless sensor networks

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Improving Data Integrity for Gray Hole Attack Detection by using a Hash Signature Algorithm in WSN 5

Improving Data Integrity for Gray Hole Attack Detection by u...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Radhika, S. Srikanth, M.S. Anand, K. Saravanan, K. Southry, S. Sree Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Reva University School Computer Science and Engineering Karnataka Bangalore India Rajalakshmi Engineering College Department of Computer Science and Engineering Rajalakshmi NagarThandalam Chennai India R.M.D Engineering College Department of Information Technology Kavaraipettai Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India

ISBN: (纸本)9781665474672

Nowadays, sensor nodes are utilized in widespread distribution. thus, security issues are a significant apprehension in the Wireless sensor networks (WSN). the gray hole attack is a particularly damaging kind of assault that, in addition to bringing down WSNs, brings down whole networks and interferes with the flow of data. A hash signature algorithm (IGDH) is proposed to solve these problems to improve data integrity for Gray hole attack detection. During the process of route discovery in the network, this strategy makes use of an algorithm that generates a hash signature to detect the Gray hole attack. the recipient node gets the message from any sensor and verifies the sensor node hash signature. During data transmission, the recipient node checks the principal communication count (PCC) and size of the data packet;if the packet size is equal Accept the message;otherwise, the recipient node decides some packets are dropped and isolates that node is a grayhole attacker. the simulation results explain the proposed method has better packet delivery and minimizes the packet loss and miss detection ratio in the WSN. © 2023 IEEE.

关键词： sensor nodes

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A Comprehensive Development of Human Emotion Detection based on Facial Features using Digital Image processing Methodology 5

A Comprehensive Development of Human Emotion Detection based...

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5th international conference on Intelligent Communication Technologies and Virtual Mobile networks, ICICV 2024

作者： Karthikayani, K. Rawat, Rishikesh Soundararajan, S. Kalai Lakshmi, T.R. Seekoli, Avinash Moharekar, Tejashree Tejpal Srm Institute of Science and Technology Chennai India Bansal College of Engineering Department of Computer Science & Engineering India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India School of Management Studies Sathyabama Institute of Science and Technology Chennai India St.Martins Engineering College Department of Cse Secunderabad India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India

ISBN: (纸本)9798350385649

this research introduces the Human Emotion Detection utilizing Facial Features (HEDFF) framework, presenting a comprehensive and advanced approach to emotion analysis. Focused on the integration of the AffectNet dataset, renowned for its diversity in annotated facial expressions, our methodology employs digital image processing techniques. Gabor filtering enhances relevant features crucial for subsequent analysis, followed by Convolutional Neural Network (CNN) utilization for robust feature extraction. the EfficientNet architecture refines emotion classification, optimizing efficiency. the AffectNet dataset serves as a cornerstone for model training and evaluation, contributing to the framework's efficacy. Gabor filtering during data preprocessing emphasizes critical textures. the CNN architecture captures intricate facial features, and EfficientNet optimizes classification efficiency. Particle Swarm Optimization (PSO) fine-tunes hyperparameters, enhancing the overall model performance. Rigorous evaluations against existing benchmarks showcase promising results in accuracy, precision, recall, and F1-score, with an outstanding overall accuracy of 98.87%. this research makes a significant contribution to the evolving landscape of emotion detection, providing a robust and efficient framework applicable across diverse domains. From human-computer interaction to mental health assessments, the proposed methodology demonstrates its versatility. © 2024 IEEE.

关键词： Feature extraction

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Enhancing Agricultural Yield Predictions with Real-Time IoT sensor Data and Machine Learning Integration 5

Enhancing Agricultural Yield Predictions with Real-Time IoT ...

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5th international conference on IoT Based Control networks and Intelligent Systems, ICICNIS 2024

作者： Chandiraprakash, N. Chinnasamy, A. Ashok, M. Malla Reddy College of Engineering Department of Artificial Intelligence and Machine Learning Hyderabad India School of Computing Department of Data Science and Business Systems SRMIST kattankulathur campus Chennai India Malla Reddy College of Engineering Hyderabad India

ISBN: (纸本)9798331518097

this research extends previous studies on the effects of climate change on agricultural yield predictions by integrating advanced Machine learning (ML) methods with real time environmental sensing data. It builds on the basic usage of Long Short-Term Memory (LSTM) and Random Forest (RF) methods by employing real time data streams from IoT based field sensors that measure soil moisture, nutrient levels and pest activity. the aim is to enhance the prediction model's adaption to changing climatic circumstances, therefore increasing their utility in agricultural management. this study proposes a dynamic adaptation feature that uses real time data to make timely adjustments to crop management predictions. Moreover, the integration of Convolutional Neural Network (CNN) with LSTM is being investigated to increase the spatial and temporal accuracy of these predictions. By integrating conventional climatic data with detailed micro environmental elements from IoT sensors, this method obtains a aspects which affects the crop growth. this study also focuses on developing a userfriendly interface that allows farmers to easily access predictive developments and recommendations, allowing for more informed decision making. this technique is predicted to make substantial advances in precision agriculture, giving more effective methods for regulating agricultural productivity in dynamic climatic variability. these results may improve food security and sustainability by optimizing resource utilization and reducing the environmental impact of farming operations. © 2024 IEEE.

关键词： Smart agriculture

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A Review of Image Enhancement Methods Based on Stochastic Resonance of Neuronal networks 5

A Review of Image Enhancement Methods Based on Stochastic Re...

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5th IEEE international conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Sun, Ximin Niu, Pingjuan Li, Yong Li, Xiaoming Zhao, Bingzhen Zhang, Bin Liu, Dan Sun, Bo State Gride Ecommerce Technology CO. LTD Tiangong University Laboratory of Metaverse in Electric Power Industry Tian'jin China Tiangong University Laboratory of Metaverse in Electric Power Industry Tian'jin China State Gride Ecommerce Technology CO. LTD Laboratory of Metaverse in Electric Power Industry Tian'jin China

ISBN: (纸本)9798350355925

Stochastic resonance has always been a research hotspot in the field of image processing. the ability of the nervous system to detect signals is closely related to nonlinear and collective behavior in neural mediators. Neuronal noise is the most significant factor influencing this ability, and it is a natural endogenous fluctuation in brain activity. Experimental and theoretical studies have shown that noise is beneficial, not harmful, for the normal functioning of the nervous system. In this regard, it is generally agreed that noise of appropriate intensity can participate in the rhythmic activity of the brain, and that noise-induced oscillations enhance the performance of weak signal processing, especially when the frequency of the signal is approximately noise-induced rhythmic oscillations. this behavior in the biological nervous system can be explained by the concept of 'stochastic resonance'. Correspondingly, the stochastic resonance mechanism of neurons is used to denoise the image, which has a good effect. In this paper, we first introduce an image processing method using random resonance of neurons. then, the commonly used evaluation indicators in the field of image processing are sorted out. Finally, the development trend of image processing using random resonance of neurons is prospected. © 2024 IEEE.

关键词： Circuit resonance

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