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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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Wireless Sensing Network's Cyber Security Procedures: A Research 5

Wireless Sensing Network's Cyber Security Procedures: A Rese...

引用

5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Singh, Harveer Dhiman, Neha Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India Chandigarh Engineering College Department of Computer Science & Engineering Jhanjeri India

ISBN: (纸本)9798350398267

Because WSNs (wireless sensor networks) are connected to the internet, they are an effective network for solving practical issues. A lightweight, equipment sensor with computational possibilities and limited power supplies is available in WSN as new technologies develop quickly. However, there is a significant security issue with this network. Because of the wireless communication channel, it is now challenging to guard against numerous attacks like DoS (Denial of Service). As a consequence, several different methods for identifying DoS attacks have indeed been suggested. Several indicators, including bandwidth, PDR (packet delivery ratio), and E2E (end to end) latency are calculated to assess the impact of an attack on a network. the simulations make use of real-world datasets. Even while simulations demonstrate that DoS attacks make WSNs less resilient, certain systems have been shown to be good at spotting DoS attacks. © 2022 IEEE.

关键词： Wireless sensor networks

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Research on Cluster-Based Routing Protocol for Wireless sensor networks 5

Research on Cluster-Based Routing Protocol for Wireless Sens...

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5th IEEE international conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Liu, Bangyan Zhang, Jiuchuan Zhu, Zhenxiong Chongqing University Chongqing400000 China University of Electronic Science and Technology of China Chengdu610000 China Nanjing Normal University Nanjing210028 China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

LEACH routing algorithm is an important routing algorithm in wireless sensor networks (WSN s), there exist imperfections in the LEACH algorithm, which can be summarized as the uneven number of cluster heads, the unreasonable distribution of cluster heads and inappropriate selection of cluster heads. Researchers has put forward algorithms based on LEACH as improvement. In this paper, we focused on analysis of inappropriate selection of clusters in LEACH routing algorithm from energy consumption approach. To solve this existing issue in LEACH routing algorithm, a new algorithm that maximum the efficiency of energy consumption is proposed in this paper, which is LEACH-OptCluster. this paper analyses the disadvantages of LEACH routing algorithm. LEACH-C routing algorithm is introduced as comparison with proposed algorithm. Simulation of LEACH-OptCluster is conducted through Matlab. With simulation results, further analysis indicates the proposed LEACH-OptCluster routing algorithm can prolong the WSNs network working time effectively through balancing the overall energy consumption of network nodes. © 2022 IEEE.

关键词： Wireless sensor networks

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Experimental Analysis for sensor Reduction to Depict Real-Time Applications through Regression Techniques 5th

Experimental Analysis for Sensor Reduction to Depict Real-...

引用

5th international conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Vinodha, K. Gopi, E.S. Vinnakota, Bapeswar National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli620015 India

ISBN: (纸本)9783031609343

the expenses and maintenance requirements rise along with the number of sensors. To solve this problem, strategically placing sensors in real-time applications is essential. this work presents a novel approach that leverages regression analysis to identify representative and non-representative sensors, leading to the removal of redundant sensors without compromising accuracy. the proof-of-concept study employed a fan as the test object to collect sensor data. However, the proposed method can be extended to real-time scenarios such as vehicles, engines, turbines, and gym or sports equipment. Establishing an approximate correlation between representative sensors (close to the item) and non-representative sensors (placed at a distance) can eliminate the need for a larger number of sensor placements. It utilizes different regression models and analyzes the gathered data to determine the use of representative and non-representative sensors. the evaluation process involves techniques such as correlation matrix and eigenvalues to identify usual sensors. A machine learning regression method is used to predict non-representative sensor outputs, including linear regressions (LR) and Gaussian regressions (GR). A mean square error (MSE) is calculated to compare the accuracy of predicted values obtained from both regression techniques. the proposed method offers several benefits, including simplified maintenance, reduced costs, and improved performance with fewer sensors. Accurately identifying representative sensors provides a practical solution for optimizing sensor deployment in real-time applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Eigenvalues and eigenfunctions

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IoT-Based Convolutional Neural networks in a Farm Pest Detection Using Transfer Learning 5th

IoT-Based Convolutional Neural Networks in a Farm Pest Detec...

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5th international conference on Computing Science, Communication and Security

作者： Jani, Keyurbhai A. Chaubey, Nirbhay Kumar Panchal, Esan Tripathi, Pramod Yagnik, Shruti Gujarat Technol Univ Comp IT Engn Ahmadabad 382424 Gujarat India Ganpat Univ Comp Sci Mahesana 384012 Gujarat India Govt Polytech Informat Technol Gandhinagar 382027 Gujarat India Indus Univ Comp Engn Ahmadabad 382115 Gujarat India

ISBN: (纸本)9783031751691;9783031751707

In this study explores agriculture pest detection using transfer learning with IoT devices, evaluating VGG16, VGG19, Inception, and Xception CNN architectures with the agripest dataset. VGG16 and VGG19 show effective learning with consistent accuracy improvements. Inception V3 exhibits strong training but with variability in validation metrics, while Xception demonstrates robust performance and strong generalization to new data. the integrated system utilizes cameras, sensors, and drones for real-time image processing through a gateway and cloud server with a customized agripest dataset. Transfer learning generates a deployable.h5 file for pest identification. the generated custom model deployed on a gateway or server to classifies pests, alerting farmers through SMS, dashboard, or mobile app notifications. this synergy between machine learning and IoT offers rapid and precise pest detection in agriculture.

关键词： IoT deep learning CNN pest detection cloud farm gateway

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Long Short-term Dynamic Graph Neural networks: for short-term intense rainfall forecasting 5

Long Short-term Dynamic Graph Neural Networks: for short-ter...

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5th international conference on Machine Learning and Natural Language processing (MLNLP)

作者： Xie, Huosheng Wang, WeiJie Fuzhou Univ Fuzhou Fujian Peoples R China

ISBN: (纸本)9781450399067

In practice, accurate and timely forecasting of short-term intense rainfall is critical, but the problem is extremely difficult because to its complicated spatial-temporal association. Although several spatial-temporal series forecasting methods have been used to rainfall prediction, these models continue to suffer from inadequate modeling of data's complicated intrinsic connection. We provide a new short-term intense rainfall prediction model that use two graph generators to model data correlations under distinct semantics, followed by a graph convolution module for information integration to fully extract data spatial-temporal information. Finally, a variant of recurrent neural network is employed to extract the temporal dependence. the experimental results on both datasets show that the model can model the spatial and temporal dependence across the data more effectively than the baseline model, and further improve the model's predictive performance for short-term intense rainfall.

关键词： Short-term intense rainfall spatial-temporal correlation Graph Convolution

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Analysis of Commercial Cryptography Application and Verification in Intelligent Connected Vehicles 5

Analysis of Commercial Cryptography Application and Verifica...

引用

5th international conference on Computers, Information processing and Advanced Education, CIPAE 2024

作者： Li, Yuning Shao, Xuebin Dong, Changqing Bao, Yue Zhao, Yaqing China Automotive Technology Research Center Co. Ltd Tianjin China Co. Ltd Tianjin China

ISBN: (纸本)9798331527662

the purpose of this project is to solve the information security problems existing in the information of commercial vehicles. the fusion of SM2 packet encryption and SM3 encryption is used to realize the confidential transmission of vehicle information. Virtual security gateway is constructed by CANoe method. the SM2 algorithm is used to encrypt the new value of the message and the initial data area of the message to reduce the risk of plaintext attack. the SM3 method is used to ensure the source validity. the VN1610 car is taken as an example to carry out the field experiment, and the high load performance of CAN bus is verified. the experiment proves that the security communication protocol constructed by SM2 and SM3 encryption methods can reach the data transmission rate of the gateway in terms of computing time. the calculation processes of power-up initialization, public key pair generation, key export, SM2 encryption and encryption of TBOX and PEPS are obtained based on SaleaeLogic8 software. It is found that the high time of SM2 encryption and encryption is mainly due to the complexity of SM2 algorithm itself and the excessive computing overhead of MIARCL. © 2024 IEEE.

关键词： Gateways (computer networks)

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Attribute graph node anomaly detection based on training strategy optimization 5

Attribute graph node anomaly detection based on training str...

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5th international conference on Computer Communication and Network Security, CCNS 2024

作者： Song, Pengfei Lu, Hao Cao, Ning Zhang, Shuwei College of Information Science and Engineering Hohai University Nanjing China

ISBN: (纸本)9781510681842

Attributional network anomaly detection is increasingly becoming a focal point of academic research due to its applications in critical domains like social networking and financial fraud. this task faces many challenges due to the differences in attributes between anomalous nodes and others, as well as their complex interactions. While existing shallow methods fall short in concurrently addressing both attributes and structures, leading to suboptimal performance, methods based on deep learning have made significant advances in enhancing anomaly detection. However, they still lack sufficient exploitation of anomaly information. In response to the aforementioned issue, this paper introduces an attribute graph node anomaly detection method based on training strategy optimization (ANATSO). the model constructs a secondary view through edge perturbation and samples subgraphs from each view. these subgraphs are then used to initialize networks for subgraph-node and node-node instance pairs. Additionally, a phased training process is implemented, where the outcomes from the initial phase are used to optimize the node input processing in the subsequent phase, ultimately calculating anomaly scores for each node. this research was evaluated on five benchmark datasets, and the results demonstrate that compared to traditional baseline methods, our model significantly improves the precision of anomaly detection. © 2024 SPIE.

关键词： Graph neural networks

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IRS-Aided Caching with Channel Power-Delay-Profile via DDPG and Self-Supervision Learning 9

IRS-Aided Caching with Channel Power-Delay-Profile via DDPG ...

引用

9th international conference on Computer and Communication Systems (ICCCS)

作者： Huang, Huang Zhou, Fasheng Guangzhou Univ Sch Elect & Commun Engn Key Lab On Chip Commun & Sensor Chip Guangdong Hi Guangzhou Peoples R China

ISBN: (纸本)9798350350227;9798350350210

the 5G information age requires a large number of marginal nodes distributed around users to provide digital services. By caching and processing data at edge nodes close to users, edge caching technology can effectively collaborate with cloud computing and edge computing to improve the transmission efficiency as well as network response time. Meanwhile, intelligent reflective surface (IRS) is a promising technology for achieving high reductions in hardware cost and power consumption as compared to traditional relaying systems. It provides reliable and scalable backhaul transmission for base stations and access points in ultra-dense networks, which is in line with the requirements of 5G green technology. therefore, in this paper, we propose a collaborative edge caching method that effectively combines IRS-aided wireless communication and channel power-delay-profile to design joint caching decisions in a centralized manner through limited cache capacity, improving the quality of content delivery in information transmission. then we proposed a deep deterministic policy gradient learning method based on the framework of deep reinforcement learning along with self-supervision learning to predict IRS phases and collaborative caching strategies. the numerical simulation results show that the proposed algorithm has better convergence speed and learning accuracy than that of the existing algorithms, and is expected to be applied for future massive and dynamical content delivery applications.

关键词： Deep deterministic policy gradient (DDPG) edge caching intelligent reflecting surface average power-delay-profile(APDP) spectrum efficiency (SE) self-supervision learning

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Assistive Stick for Visually Impaired People 5

Assistive Stick for Visually Impaired People

引用

5th international conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Khan, Danish Asad Zamir, Mohammed Asif Umar, Mohammad Sarosh Haider, Zafaryab Jamia Millia Islamia DO Comp Engn New Delhi India Indian Inst Technol SJMSOM Mumbai Maharashtra India Aligarh Muslim Univ IIRU Aligarh Uttar Pradesh India

ISBN: (纸本)9781665476478

Blind or visually impaired people have difficulty navigating the street and are also unaware of nearby landmarks. they need an assistant (e.g. a cane, human, dog, e.t.c.) to go from one place to another place. In this modern era, it is possible to develop smart solutions with the help of modern technologies like deep learning (DL), natural language processing (NLP), internet of things (IoT), etc. that can assist them without any human intervention. A smart stick is one of the solutions that can help them with navigation and get to know the surrounding places. this paper proposed a smart solution and developed a smart stick to help visually impaired people with the use of DL and NLP and devices like the Raspberry Pi, an ultrasonic sensor, and a camera. this smart stick can detect obstacles using an ultrasonic sensor within the range of 1.5 metres and alert the person by generating audio as a warning message. the stick is also able to capture images from its surroundings using the camera and can read the text present in the captured image. the text in the image is detected using a deep learning model called the EAST text detector and recognised using an OCR Engine tesseract. Furthermore, the recognised text is converted into audio so that the person can hear about their surroundings. All calculations and processing are done on the Raspberry Pi, and the generated audio can be heard by using a speaker(earphone or headphone). the proposed solution is successfully implemented and demonstrated, and it is found to be very efficient and cost-effective.

关键词： OCR Ultrasonic sensor EAST Tesseract Text Recognition Raspberry Pi Deep Learning Natural Language processing

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Review on Video Super Resolution: Methods and Metrics 5

Review on Video Super Resolution: Methods and Metrics

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Shiva Reshma, R. thomas, Joshua College of Engineering Trivandrum Department of Electronics and Communication Trivandrum India

ISBN: (纸本)9798350334128

Video Super Resolution (VSR) is a wide area in image processing, where numerous research works are going on. Super Resolution (SR) means the upstaging of Low Resolution (LR) frames to High Resolution (HR) frames with minimal loss in image quality. the goal of this paper is to give the comprehensive review of various VSR methods like Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Gated Recurrent Unit (GRU), Spatial Temporal Transformer (STT), Fractional-Sine Cosine Algorithm (F-SCA), Recurrent Back-Projection Network (RBPN), Temporally-Deformable Alignment Network (TDAN). Finally, the readers will get more ideas about VSR based methods and metrics. © 2023 IEEE.

关键词： Generative adversarial networks

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