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

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Secure Authentication and Session Key Management Scheme for Distributed sensor networks 5

Secure Authentication and Session Key Management Scheme for ...

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5th international conference on Computing, Electronics and Communications Engineering. iCCECE 2022

作者： El Zouka, Hesham A. Hosni, Mustafa M. Arab Academy for Science Technology and Maritime Transport Computer Eng. Dept College of Engineering and Technology Alexandria Egypt Managing Director of Omikron Technologies Electrical Engineering Dept Faculty of Engineering Alexandria Egypt

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

Wireless sensor networks are liable to attacks because of the complicated operations they perform especially the node deployment one. Security and energy consumption represent two big flaws in Wireless sensor networks and this paper is discussing how those networks can be secured against network violations and attacks while preserving the energy consumption in the network. therefore, handling this problem using security schemes should ensure a moderate consumption of energy and for this target a secure hybrid session key management scheme is proposed where most of the operations taking place depend on cryptography and the heart of cryptography is the keys which are used to ensure that the energy consumption will remain limited and the security issue will be guaranteed. Benchmark schemes have been of great help in analyzing the proposed scheme to come out with the targeted conclusion ensuring its efficiency in comparison with other benchmark schemes and its ability to limit energy consumption and to present quite an effective tool in network environment that is able to secure management and key agreements. © 2022 IEEE.

关键词： Energy utilization

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An Intelligent Failure Free Data Communication Model using Predicted Remaining Deliveries Scheme 5

An Intelligent Failure Free Data Communication Model using P...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Dhanasekaran, S. Vijayakarthik, P. Gobinath, D. Nithiyanandam, N. Kumar, A. Sivanesh Shifani, S. Agnes Muthuraman, U. Kalasalingam Univ Dept Informat Technol Srivilliputtur Tamil Nadu India Sir M Visvesvaraya Inst Technol Dept Informat Sci & Engn Bengaluru India Sri ManakulaVinayagar Engn Coll Dept CSE Pondicherry India Bharath Inst Higher Educ & Res Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India JP Coll Engn Dept Elect & Elect Engn Tenkasi 627852 Tamil Nadu India Francis Xavier Engn Coll Dept EEE Tirunelveli India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Using wireless sensor networks as a case study;this system examines the issue of excessive power usage. In hostile environments, where circumstances vary rapidly, wireless sensor nodes might suffer from abrupt connection quality as well as node status changes. As the network quality and node state fluctuate, so does the end-to-end latency for every sensor-node. sensor nodes, on the other hand, have a finite amount of power;therefore, extending the network's lifespan is a major challenge. Predicted Remaining Deliveries (PRD) is a distinctive and uncomplicated routing statistic that combines elements like as transmission capacity, connection dependability, and end-to-end latency and distance to give higher network performance. the PRD balances specific connections and end-to-end latencies in order to successfully depict the state of nodes over time. Using simulated tests, PRD exceeds ETX and other recently established measurements in terms of end-to-end delay and received packets.

关键词： Wireless sensor networks Link-delay Energy Efficiency Predicted Remaining Deliveries PRD

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Data processing method of high dynamic gravity measurement for unmanned surface vehicle at sea 5

Data processing method of high dynamic gravity measurement f...

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5th international conference on Geoscience and Remote Sensing Mapping, ICGRSM 2023

作者： Xiang, Minzhi Chai, Hongzhou Wu, Xingtao Tang, Minqiang Fan, Haopeng Zhang, Yong Yang, Zhen Pla Strategic Support Force Information Engineering University Zhengzhou China Tianjin Navigation Instrument Research Institute Tianjin China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou China South China Sea Marine Survey Center Ministry of Natural Resources Guangzhou China

ISBN: (纸本)9781510672789

During high dynamic gravity measurements conducted on unmanned surface vehicle, the presence of low-frequency noise caused by the vertical and horizontal motion disturbances of the carrier in conjunction with the low-frequency excitation noise from the sensor, results in a direct mixture within the frequency band of the gravity signal. Admittedly, conventional filtering techniques such as finite impulse response (FIR) or infinite impulse response (IIR) filtering prove insufficient in eliminating the measurement noise, ultimately leading to a decrease in gravity measurement *** this regard, this paper proposes the use of the kalman smoothing method as a replacement for the traditional frequency domain low-pass filtering technique. this method allows for the identification of gravity anomaly information even in the presence of noise by employing optimal estimation *** that the gravity measurement data is processed offline, this paper further utilizes the optimal fixed interval smoothing algorithm to process the gravity measurement data obtained from unmanned surface vehicle. this algorithm enhances the accuracy beyond what is achievable with traditional frequency domain low-pass filtering techniques. To validate the effectiveness of our proposed algorithm, we have conducted processing on real sea test data, confirming its efficacy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Frequency domain analysis

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Node Deployment Strategies and Challenges in Underwater Wireless sensor Network

Node Deployment Strategies and Challenges in Underwater Wire...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Layolin Benisto L C Rajeev Sukumaran Agnel Shyam Kumar C Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur India

Underwater Wireless sensor networks (UWSNs) are gaining importance for a wide range of applications, from environmental monitoring to underwater exploration. However, the challenging underwater environment poses significant obstacles to reliable data transmission. this study focuses on the role of deployment nodes in UWSNs, addressing the need for extended communication range, signal amplification, and data reliability. the study presents an in-depth exploration of node deployment, communication protocols, and the unique challenges faced in underwater environments. By strategically placing and managing nodes, UWSNs can overcome these challenges and improve overall network performance. UWSNs integrate a number of critical deployment strategies designed for land-based sensor networks. However, because to the unique 3-D properties of UWSNs, not all of these methodologies are immediately relevant. Underwater networks function in three dimensions, posing additional issues such as sensor node mobility driven by water currents and significant communication delays. the goal of this research is to look at various node deployment methodologies in order to shed light on the difficulty of sensor node deployment. the study intends to investigate various node deployment strategies in order to shed light on the complexities of sensor node deployment in UWSNs. Furthermore, the research performs an exhaustive literature analysis on node deployment, diving into difficulties and obstacles faced during deployment, and extensively analyzing the performance of various node deployment approaches. this research provides useful insights for optimizing node deployment in UWSNs, opening the path for increased communication efficiency in the difficult underwater realm.

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General Forgery Face Detection: Against Generative Adversarial networks Using Knowledge Distillation 5

General Forgery Face Detection: Against Generative Adversari...

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5th international conference on Data Science and Information Technology, DSIT 2022

作者： Liu, Wei Sun, Jingdong Shenzhen Vanke Meisha Academy Shenzhen China Carnegie Melon University Pittsburgh United States

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

Generative adversarial networks (GANs) have been widely used for fake target face generation in film-making and journalism. What backfired is that GANs are being wildly misused to impersonate credible people and publish illegal, misleading, and confusing information to the public. To our dismay, however, the main problem with previous fake face detection methods is their inability to generalize to distinguish different GANs. To address this challenge, this paper begins by further analyzing the weaknesses of GAN-based generators. Our experiments revealed that the recent fake faces generated by GANs are still not robust enough because it does not consider enough pixels. Inspired by this finding, we design a novel convolutional neural network that uses frequency texture augmentation and knowledge distillation to enhance its global texture perception, effectively describe textures at different semantic levels in images, and improve robustness. Experimental results show that our model achieves better and more consistent performance in image processing or cross-domain settings, especially when images are subject to Gaussian noise. © 2022 IEEE.

关键词： Generative adversarial networks

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Hybrid Energy Efficient Fuzzy C-Means with Bear Smell Search Algorithm in Wireless sensor networks 5

Hybrid Energy Efficient Fuzzy C-Means with Bear Smell Search...

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5th international conference on Computational Intelligence and networks, CINE 2022

作者： Abraham, Robin Vadivel, M. Sathyabama Institute of Science and Technology Chennai India Department of ECE VidyaJyothi Institute of Technology Aziz Nagar ChilkurBalaji Road Telangana Hyderabad India

ISBN: (纸本)9781665464659

Energy efficiency is one of the primary deficiencies in wireless sensor networks. the sensor nodes present in the networks contain small battery, which cannot be changed or recharged again. Hence, the use of energy must be carefully monitored and preserved in these battery-operated networks. Energy optimization is recently the popular technique incluster-based routing protocols. the clustering methodology works under the act of group of sensor nodes where each group selects one head member called cluster head. To enhance the lifespan of the network with high energy efficiency, a Fuzzy C-means algorithm is offered in this articlefor clustering the sensor nodes. the cluster head is selected by using particle swarm optimization- Leven berg Marquardt (PSO-LM) algorithm to maximize the energy efficiency and diminish the quantity of dead sensor nodes in the network. Finally, a shortest path is selected via bear smell search algorithm to transmit the data to the sink node. the proposed method is experimentally evaluated and the results are compared in terms of packet delivery ratio and energy efficiency. the experimental outcome revealed that the proposed technique out performs other optimization algorithms and produced better results. © 2022 IEEE.

关键词： sensor nodes

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Complex-Valued Convolutional Neural Network Classification of Hand Gestures from Radar Images 5th

Complex-Valued Convolutional Neural Network Classification o...

引用

5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Khandan, Shokooh Costen, Fumie Manchester Metropolitan University Chester Street ManchesterM1 5GD United Kingdom The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom

ISBN: (纸本)9789819638628

Radar-based hand gesture recognition is an emerging field valued for its contactless and robust capabilities in human-computer interaction (HCI), signal processing, and computer vision [1] [2]. Despite its growing importance, the accuracy of hand gesture recognition remains insufficient [12]. HCI using dynamic hand gesture recognition is potentially an effective and natural method for a user to control a device. the hypothesis of the research in this area is that radar-based gesture recognition will be more effective than vision-based methods, because they are not susceptible to light conditions, and can detect and classify targets, providing rich Doppler information [7]. Its applications include enhancing accessibility in assistive technologies, improving smart device control, and enabling touch-less interfaces for healthcare and industrial environments. Various deep neural network architectures, such as multi-layer perceptrons (MLP) [4], convolutional neural networks (CNN) [5], recurrent neural networks (RNN) [6] and combinations like CNN-RNN [17], have been explored. However, these methods often handle radar images, which are naturally complex-valued (CV), using real-valued (RV) techniques. So far, researchers approaches often involve splitting CV numbers into real and imaginary parts, this can lead to increased network parameters and loss of correlation between these components. CV networks, though potentially richer in representation, face challenges due to a lack of necessary building blocks and therefore has not been utilised much. To address these issues and develop CV building blocks, we developed a novel CV algorithm for two-layer CV-CNN, performing all operations in the complex domain. Our model was tested on two hand gesture radar datasets, achieving 99% accuracy in binary classification for both datasets. the training converged much faster than the corresponding RV-CNN model and number of training parameters is half of RV-CNN. © the Author(s), under exc

关键词： Convolutional neural networks

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A Hybrid Neural Network and Graph theory based Clustering Protocol for Dynamic IoT networks 5

A Hybrid Neural Network and Graph Theory based Clustering Pr...

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5th Edition of the international conference on Advanced Aspects of Software Engineering, ICAASE 2022

作者： Merah, Malha Aliouat, Zibouda Batta, Mohamed Sofiane University Ferhat Abbas Setif 1 Lrsd Laboratory Faculty of Sciences Setif Algeria FEMTO-ST Institute Disc University of Bourgogne Franche-Comte Besançon France

ISBN: (数字)9781665492027

ISBN: (纸本)9781665492027

Internet of things has emerged as a revolutionary technology that holds promise in a wide range of applications. However, its deployment presents some difficulties since IoT networks are based on battery-empowered devices. Clustering techniques were introduced to conserve the energy of network devices. Recently, Machine Learning-neural network-based clustering techniques have proven their efficiency for topology management and network lifetime. Graph theory is a powerful mathematical and computational discipline that studies graphs, which are abstract models of network designs connecting objects and allowing significant decisions to be made that affect network performance. In this paper, a combination of neural networks and graph theory is done in order to perform cluster based routing in dynamic IoT networks called SOM-FW. the proposed method clusters sensor nodes using the artificial neural network method named Self-Organizing Maps, based on their distribution, providing a balanced CH distribution. the election of the cluster-head is based on the Floyd-Warshall algorithm, which computes the center of the cluster graph. A dynamic re-clustering strategy is adopted to handle novel incoming nodes, faulty or dead nodes, and CH premature death. the superiority of the protocol is extensively demonstrated in energy efficiency, and network lifetime. © 2022 IEEE.

关键词： Self organizing maps

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Radar Target Recognition with Self-Supplementary Neural Network 5

Radar Target Recognition with Self-Supplementary Neural Netw...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Huang, Xuhua Sun, Weize Huang, Lei Chen, Shaowu Li, Qiang College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665485890

Radar target recognition is an important problem in radar system and had been widely studied in the pass decades. In this paper, we first build a radar target recognition system that classifies the target based on i successive frames of echo values. A baseline model using the VGG-16 as backbone is then introduced. In order to perform target recognition under different values of i or number of frames, two neural networks, referred to as Independent Self-supplementary Radar Target recognition Network (ISRTNet) and Share Self-supplementary Radar Target recognition Network (SSRTNet), are then proposed. Both networks will feed two input data samples of different frames of echo values to the networks, and study the similarities between the two samples in order to achieve better recognition result. the ISRTNet, furthermore, can reduce the total number of network parameters and is suitable to be deployed in the system when it is required to perform the target recognition under different number of frames. Experimental results show that the proposed models can achieve outstanding recognition performance comparing to the baseline model. © 2022 IEEE.

关键词： Deep neural networks

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Semi-global depth prediction network based on post-processing module 5

Semi-global depth prediction network based on post-processin...

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5th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2022

作者： Liang, Liang Zhen, Wu Yu, Chen Yiping, Chen Mingming, Zhang State Grid Jiaxing Power Supply Company Jiaxing China State Grid Zhejiang Electric Power Co. Ltd Hangzhou China

ISBN: (纸本)9781665471978

the 3D reconstruction can be used for model making, robot navigation, virtual reality and autonomous pilot. Data sets and algorithms, Stereo matching method based on convolutional network has been widely concerned and achieved great success. Based on the GA-NET, this paper verifies the influence of SGM improved SGA and LGA on stereo matching based on deep learning algorithm. At present, most of the mainstream stereo matching networks use the residual of the output disparity of the simple convolutional network to tune. But, as a matter of fact, ordinary convolution kernel can not reduce various mode errors effectively. Simple convolutional networks sometimes smooth the whole result excessively in order to reduce the mean error. Based on the variable convolutional network and traditional stereo matching algorithm, this paper proposes and adds the Post-processing Module(PPM), which is used to find effective neighborhood points to update the disparity value, so as to reduce the mismatching rate of stereo matching. © 2022 IEEE.

关键词： Stereo image processing

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