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检索条件"任意字段=4th IEEE International Conference on Distributed Computing in Sensor Systems"

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Intelligent Driving sensor Data Tampering Risk Detection Based on Convolutional Neural Network 4

Intelligent Driving Sensor Data Tampering Risk Detection Bas...

引用

4th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2023

作者： Zhang, Yanan Jiang, Zexin Liu, Tianyu CATARC Intelligent and Connected Technology Co. Ltd. China

ISBN: (纸本)9798350343113

With the continuous development of intelligent driving technology, the reliability of sensor data is crucial to ensure driving safety and improve the performance of automatic driving. However, in the face of increasingly sophisticated and intelligent attack methods, the risk of tampering with sensor data is also increasing. therefore, this study aims to develop an intelligent driving sensor data tampering risk detection method based on convolutional neural networks to effectively address this challenge. this paper firstly analyzes the potential threat of sensor data tampering to driving safety and system performance in intelligent driving system. Secondly, we propose a risk detection framework based on convolutional neural networks to identify and detect tampering of sensor data. the framework combines the advantages of convolutional neural networks to determine whether the data has been tampered with by learning the feature representation of sensor data. We designed a deep convolutional neural network model with multiple convolutional layers and pooled layers, and trained and validated it with a large amount of real driving data. To evaluate the performance of the proposed method, we constructed a dataset containing different types of attacks and compared it with other commonly used tamper detection methods. © 2023 ieee.

关键词： Convolution

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An Optimization-based Non-contact Transient Voltage Measurement Method by Integrated Optical Electric Field sensor 4

An Optimization-based Non-contact Transient Voltage Measurem...

引用

4th international conference on Smart Grid and Energy Internet, SGEI 2024

作者： Sun, Yongsen Qian, Bo Xu, Yang Xie, Tianxi Chen, Yang Yang, Huaiyuan Department of Electrical Engineering Tsinghua University Beijing China State Grid Jiangsu Electric Power Co. Ltd. Nanjing210000 China State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing211103 China

ISBN: (纸本)9798350368314

Transient overvoltage in power systems, caused by lightning or switching, are a critical reliability constraint. Traditional contact measurement methods have limitations, prompting the development of non-contact measurement technologies. Integrated Optical Electric Field sensor (IOES), based on Pockels effects, offers broad advantages for non-contact voltage measurement. this paper introduces an optimization-based decoupling method for multi-phase coupling scenarios in power system such as three-phase transmission lines and verifies its correctness with a non-contact transient voltage measurement system using IOES. the method involves obtaining a simulation decoupling matrix and then finding the "closest" matrix under constraints using optimization techniques. the decoupling results by the optimization-based method outperformed the simulation method in accuracy, demonstrating the effectiveness of the proposed method for non-contact voltage measurement in AC power systems. ©2024 ieee.

关键词： Voltage measurement

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Research on sensor networks optimization based on edge computing 4

Research on sensor networks optimization based on edge compu...

引用

4th international conference on Green Communication, Network, and Internet of things, CNIoT 2024

作者： Liu, Jiawei School of Mechanical Engineering University of Jinan Jinan China

ISBN: (纸本)9781510685710

A sensor network is a novel type of distributed network comprising sensor nodes that perform data collection, information processing, and communication functions. the network is employed in a multitude of fields, including earthquake monitoring, agricultural production, smart homes, and battlefield reconnaissance. In this work, we propose a novel optimization model that employs edge computing nodes for data preprocessing and distribution in sensor networks. this approach markedly diminishes the computational load on the central server by conducting data processing and filtering at the network's edge, consequently reducing the volume of data that must be transmitted to the central server. In particular, we have developed a collaborative computing algorithm that allows multiple edge nodes to share computing tasks, thereby enhancing the speed and accuracy of data processing. Furthermore, the dynamic resource allocation algorithm is employed to adaptively adjust the resource allocation of edge nodes in response to real-time network conditions and task requirements, thereby enhancing the overall efficiency and reliability of the network. Our innovative approach to data preprocessing, including advanced filtering and compression at the edge nodes, and our dynamic resource allocation strategy, which adaptively adjusts resources based on real-time network conditions and mission needs, is significantly better than existing technologies. the experimental analysis indicates that the proposed method exhibits superior performance in terms of data processing speed and network latency compared to the traditional sensor network architecture. © 2024 SPIE.

关键词： sensor nodes

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distributed Healthcare Information System Based on High-Concurrency Microservice Architecture 4

Distributed Healthcare Information System Based on High-Conc...

引用

4th international conference on Information Science, Parallel and distributed systems, ISPDS 2023

作者： Guo, Kaifeng Gao, Rongjian Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9798350337181

With the digital transformation and increasing demand for informatization in the healthcare industry, traditional centralized information systems can no longer meet the requirements of high-concurrency access. However, microservices architecture offers a flexible and scalable solution that effectively addresses the complexity and high-concurrency access demands of healthcare information systems. In this paper, we propose a microservices system that adopts a distributed architecture combined with high-concurrency processing mechanisms. the system disperses various modules across different servers as micro-services, and each microservice can independently handle requests. this distributed and parallel processing approach improves system responsiveness and throughput while reducing the risk of single-point failures. To validate the feasibility and performance of the system, we conducted a series of experiments and evaluations. the results demonstrate that the distributed healthcare information system based on microservices architecture performs exceptionally well in handling large-scale data and high-concurrency access. the system not only provides efficient data storage and retrieval capabilities but also exhibits good scalability and fault tolerance. © 2023 ieee.

关键词： Fault tolerance

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Generating a distributed network attack dataset based on weather station IoT Cyber Physical systems 7

Generating a distributed network attack dataset based on wea...

引用

7th international conference on Artificial Intelligence, Big Data, computing and Data Communication systems (ICABCD)

作者： Johnson, David Roy, Kaushik North Carolina Agr & Tech State Univ Dept Comp Sci Greensboro NC 27411 USA

ISBN: (纸本)9798350387919;9798350387902

there has been a large increase in Internet of things (IoT) devices used in many parts of life. these devices, also called edge devices, have been known to be vulnerable to attackers and to be used as a medium for network attacks. A simple loT device uses one or more sensors to read data from the real world and performs a simple action based on the sensor readings, such as a motion sensor in a home turning on the lights when motion is detected. Building a dataset to model real-world network traffic, along with pseudo-realistic uses of network attacks is essential for creating an intrusion detection system (IDS). In this paper, we develop an loT network anomaly dataset using synthetic and real-world network attacks. Virtual machines with Ubuntu 22.04 were used to contain the network data in a sandboxed environment. For our edge devices, we use Le Potato by Libre Computers as an alternative to Raspberry Pi 3, which is a popular single board computer used for home automation and hobbyist projects where the Raspberry Pi acts as the computer for the smart device. Each device has Waveshare Weather sensor attached to it, which reads climate data such as temperature, humidity, and barometric pressure. the edge devices are located within the Cyber Defense and AI lab at North Carolina Agriculture and Technology State University. Real network attack tools were used to generate traffic to label the current data as attack data, along with the attack type. In the absence of the attack tools in use, the network traffic is labeled normal, or benign. the dataset is comprised of homogeneous data using environmental sensors that measure local climate data. In this study, we use XGBoost, LSTM+CNN, and Multilayer Perceptron along with various resampling methods to evaluate our synthetic dataset. In our results, we find that XGBoost produces the best performance at classifying attacks when using RandomUndersampler.

关键词： anomaly detection deep neural network network attack IoT Internet of things distributed learning federated learning

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A Comprehensive Review of distributed Generation and Demand Response Programs in Modern Power Networks 4

A Comprehensive Review of Distributed Generation and Demand ...

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4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Aishwarya, S. Nayanatara, C. Vanitha, A. Bala Saravanan, D. Muthumari, P. Shivam Kumar Pandey, S. Sri Sai Ram Engineering College Department of Electrical & Electronics Engineering India

ISBN: (纸本)9798331508845

distributed generation (DG) pertains to the generation of electricity from decentralized power plants, a vital consideration in the planning of contemporary power grid networks. this involves a careful assessment of both initial capital investments and associated carbon emissions. the primary advantages associated with the integration of these units encompass the augmentation of power availability and the improvement of overall grid reliability. this paper aims to review and assess demand response management (DRM), enhance the reliability and efficiency in wind power integration in the electricity grid, this study integrates (BESS) battery energy storage systems with renewable distributed generators. Additionally, it provides valuable references for future researchers aiming to improve the integration of wind power.. this review paper also offers a thorough analysis of earlier studies that sought to overcome the inherent variability of wind energy production and offer a means of storing and using surplus wind energy. the ultimate goal of this research is to develop energy systems that are more reliable, efficient, and sustainable that can accommodate an increasing proportion of renewable energy sources like wind power. A thorough examination of diverse methodologies, operational tactics, and model categorization is provided, wherein the categorization is contingent upon their respective advantages and disadvantages. © 2024 ieee.

关键词： Demand response

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U-shaped Dual Attention Transformer: An Efficient Transformer Based on Channel and Spatial Attention 4

U-shaped Dual Attention Transformer: An Efficient Transforme...

引用

4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhai, Zhaoyuan Qiao, Peng Li, Rongchun Zhou, Zhen National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798331531225

Transformer-based methods have demonstrated remarkable performance on image super-resolution tasks. Due to high computational complexity, researchers have been working to achieve a balance between computation costs and performance. Restormer has achieved commendable balance by utilizing global channel attention. However, the performance is limited by insufficient local pixel reconstruction. In this paper, we propose a U-shaped Dual Attention Transformer (UDAT) with local-global receptive field, addressing the limitation of Restormer in local pixel reconstruction. We propose a dense window channel attention to enhance the local feature representation, more efficient in computational complexity. Experiments demonstrate that our UDAT achieve superior performance compared with Restormer on benchmark datasets, surpassing Restormer by 0.57 dB on the Urban100 dataset. On-par with SwinIR, our method reduces computational complexity by 3.2 times and improves inference speed by 2 times, achieving a better balance between computational costs and performance. © 2024 ieee.

关键词： Pixels

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Deep Graph Learning for DDoS Detection and Multi-Class Classification IDS

Deep Graph Learning for DDoS Detection and Multi-Class Class...

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4th ieee Annual international conference on Cyber Security and Resilience (ieee CSR)

作者： Saunders, Braden J. De Grande, Robson E. Carvalho, Glaucio H. S. Woungang, Isaac Brock Univ Dept Comp Sci St Catharines ON Canada Brock Univ Dept Comp Sci & Engn St Catharines ON Canada Toronto Metropolitan Univ Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9798350375367

Critical infrastructure systems have been preyed on by cyber criminals that target to disrupt their operations and national security. Among the most nefarious attacks, the distributed Denial of Service (DDoS) attack is wreaking havoc on the Telecommunications sector. this paper invests in the vision that Artificial Intelligence (AI) plays an important role in shoring up the cybersecurity of critical infrastructure providers by detecting and classifying malicious engagements. In this respect, we propose an efficient and dependable DDoS specialized intrusion section system (IDS). the proposed system is empowered by Graph Convolutional Networks (GCN), a deep learning technique, which is capable of capturing the topological and statistical information between the attack network and the victim network. the results show that the proposed GCN IDS can detect and classify multiple variations of DoS with a high confidence level.

关键词： distributed Denial of Service Deep Learning Graph Neural Networks Intrusion Detection systems

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Apportionment of overvoltage responsibility for distributed photovoltaic distribution network based on improved Raiffa solution method 4

Apportionment of overvoltage responsibility for distributed ...

引用

4th international conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2024

作者： Duan, Shuyin Zheng, Xiye Yu, Lei Luo, Zongwen Liu, Yinliang Zeng, Wei Liu, Tong Huang, Boxiong Electric Power Research Institute Co. Ltd. CSG Guangzhou China Jieyang Power Supply Bureau Guangdong Power Grid Co. Ltd. Jieyang China

ISBN: (纸本)9798350356472

Aiming at the overvoltage problem generated by distributed PV's large-scale access to the distribution network, an overvoltage apportionment method based on the improved Raiffa method is proposed, with a view to determining the overvoltage responsibility of each PV user. Taking the voltage loss as the characteristic function, the overvoltage responsibility of each distributed PV is calculated by Raiffa solution method, the correction factor of the improved Raiffa solution method is calculated by using the gray correlation analysis method, and the model of the improved Raiffa solution method is constructed, and finally, the example of ieee-33 node distribution system is used to carry out the simulation validation, and the result of the example shows that the improved Raiffa solution method integrates the influence of the correction factor, and the apportionment result is better than that of the improved Raiffa solution method, and the overvoltage responsibility of each distributed PV user is determined. the example results show that the improved Raiffa solution method comprehensively considers the influence of the correction factor, and the apportionment results are more reasonable compared with the traditional Raiffa method. © 2024 ieee.

关键词： DC distribution systems

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Adaptive Service Strategy and Implementation of sensor Network for Monitoring 4

Adaptive Service Strategy and Implementation of Sensor Netwo...

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4th international conference on Digital Signal and Computer Communications, DSCC 2024

作者： Chen, Wei School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China

ISBN: (纸本)9781510681538

Adaptability of sensor network systems is an important technology that enables them to automatically adjust their working parameters according to changes in the environment, in order to achieve more efficient and reliable performance. sensor network systems typically require real-time collection and processing of large amounts of data, which takes up transmission time. To solve the problem that some sensitive events are not distinguished in the transmission process, this paper provides an adaptive method for data transmission in sensor networks. the priority of nodes is determined through lookup tables and statistical data, different priorities are set for different services, and different execution parameters are given according to the size of priorities, so that devices with higher priorities can achieve priority transmission. this paper provides stronger differentiated service mechanism support for ieee 802.15.4 standard. © 2024 SPIE.

关键词： Wireless sensor networks

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