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

共 30762 条记录，以下是251-260 订阅

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Improved Virtual Force Algorithm-Based Fuzzy Clustering in Mobile Wireless sensor Networks 21

Improved Virtual Force Algorithm-Based Fuzzy Clustering in M...

引用

21st ieee international Symposium on Parallel and distributed Processing with Applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yang, Jintao Wang, Minghua Chen, Wenbo Xie, Shujing University of South China School of Electrical Engineering Hengyang China

ISBN: (纸本)9798350329223

With the rapid development of wireless sensor networks(WSNs), Mobile Wireless sensor Networks (MWSNs) are receiving increased attention. MWSNs affect the performance of clustered networks, such as coverage and lifetime. In order to improve the coverage and the lifetime, this paper proposed fuzzy clustering method based on improved virtual force algorithm (IVFA-FC).IVFA-FC improves the network coverage and equalizes the distances between nodes through improved virtual force. Fuzzy clustering analyses multiple factors for selecting cluster head (CH) that change due to MWSN, so IVFA-FC uses fuzzy logic to optimize CH selection,to reduce the energy consumption of the network and to extend the network lifetime. In the experiments IVFA-FC is compared with the traditional virtual force algorithm and random wandering algorithm, IVFA-FC is effective in prolonging network lifetime,saving energy and improving coverage. © 2023 ieee.

关键词： Wireless sensor networks

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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism 25

Efficient Large Models Fine-tuning on Commodity Servers via ...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Yujie Lai, Zhiquan Liu, Weijie Wang, Wei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large models available to the general public. Previous solutions fail to achieve an efficient memory-balanced pipeline parallelism. In this poster, we introduce a memory load-balanced pipeline parallel solution. This solution balances memory consumption across stages on commodity GPU servers via NVLink bridges. It establishes a new pathway to offload data from GPU to CPU by using the PCIe link of adjacent GPUs connected by the NVLink bridge. Furthermore, our method orchestrates offload operations to minimize the offload latency during large model fine-tuning. Experiments demonstrate that our solution can balance the memory footprint among pipeline stages without sacrificing training performance. © 2023 ieee.

关键词： Program processors

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Toward a compact low-cost electronic interface for photoacoustic based gas sensors

Toward a compact low-cost electronic interface for photoacou...

引用

ieee international Instrumentation and Measurement Technology conference (I2MTC)

作者： Fort, Ada Mugnaini, Marco Panzardi, Enza Vignoli, Valerio Univ Siena Dept Informat Engn & Sci Siena Italy

ISBN: (纸本)9798350380903;9798350380910

The increasing need for remote monitoring within distributed sensor networks has underscored the importance of developing compact and cost-effective sensing solutions. One specific area of emphasis in this context is the application of gas sensing. This work proposes the feasibility of a cost-effective, space-efficient, and smart electronic interface for a photoacoustic-based gas sensor aimed to avoid the typical use of sophisticated and bulky readout instruments, that pose challenges in integration into smart, low-cost, stand-alone acquisition systems. The proposed electronic interface is designed to adapt the challenging gas-dependent photoacoustic signal to be acquired by an ADC interface with limited voltage full-scale and processed with low computational resource, characteristic of low-power systems. The designed conditioning electronic focuses on the optimization of system gas sensitivity in the concentration range of interest with optimal resolution. The performance of the developed prototype was experimentally validated for NO2 sensing within the concentration range of 5 ppm to 45 ppm. The assessed gas sensitivity is 65 mV/ppm of NO2, enabling the attainment of a system resolution of 660 ppb of NO2. The results demonstrate high responsiveness and robustness to noise interference. Furthermore, the impact of the characterization setup, specifically the mass flow rate of the gas, on the evaluated performance of the sensing system was explored.

关键词： PA gas sensor conditioning system gas sensors NO2 measurements low-cost conditioning systems PA gas sensors PA gas sensor interface low power gas sensor

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Learning-Driven Zero Trust in distributed computing Continuum systems

Learning-Driven Zero Trust in Distributed Computing Continuu...

引用

2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Murturi, Ilir Donta, Praveen Kumar Pujol, Victor Casamayor Morichetta, Andrea Dustdar, Schahram Distributed Systems Group Tu Wien Vienna1040 Austria

ISBN: (纸本)9798350304602

Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in distributed computing Continuum systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable for the computing continuum (e.g., computing entities with limited connectivity and visibility, etc.). At the same time, implementing decentralized ZT in the computing continuum requires understanding infrastructure limitations and novel approaches to enhance resource access management decisions. To overcome such challenges, we present a novel learning-driven ZT conceptual architecture designed for DCCS. We aim to enhance ZT architecture service quality by incorporating lightweight learning strategies such as Representation Learning (ReL) and distributing ZT components across the computing continuum. The ReL helps to improve the decision-making process by predicting threats or untrusted requests. Through an illustrative example, we show how the learning process detects and blocks the requests, enhances resource access control, and reduces network and computation overheads. Lastly, we discuss the conceptual architecture, processes, and provide a research agenda. © 2023 ieee.

关键词： distributed computer systems

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Optimized Bidirectional Long Short-Term Memory Model for distributed Denial of Service Attack Detection in Wireless sensor Networks 3

Optimized Bidirectional Long Short-Term Memory Model for Dis...

引用

3rd international conference on Smart Technologies and systems for Next Generation computing, ICSTSN 2024

作者： Poongodi, K. Mithuna, S. Rajkumar, S. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India

ISBN: (纸本)9798350391565

The rapid expansion of Wireless sensor Networks (WSNs) has made them a critical component in various applications, from environmental monitoring to military surveillance. However, their inherent vulnerabilities make them prime targets for distributed Denial of Service (DDoS) attacks, that have the potential to significantly impair data integrity and network performance. This paper presents an advanced approach for DDoS detection in WSNs using an optimized Bidirectional Long Short-Term Memory (Bi-LSTM) network. The proposed method leverages the strengths of Bi-LSTM in capturing temporal dependencies and patterns within network traffic data, providing a robust mechanism for identifying anomalous activities indicative of DDoS attacks. To enhance the detection accuracy and efficiency, introduce a novel optimization technique that fine-tunes the BiLSTM parameters, balancing the trade-offs between detection performance and computational overhead. Extensive simulations demonstrate that our optimized Bi-LSTM model significantly outperforms traditional machine learning approaches and standard LSTM configurations. The results underline the potential of this approach in fortifying WSNs against sophisticated DDoS threats, ensuring reliable and secure network operations. © 2024 ieee.

关键词： Denial-of-service attack

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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...

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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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Event-Triggered Switched Observers for Networked distributed Multi-Agent systems via Cloud computing 6

Event-Triggered Switched Observers for Networked Distributed...

引用

6th international conference on Electronic Engineering and Informatics, EEI 2024

作者： Zhang, Chen Ren, Hongru Ma, Hui Zhou, Qi School of Automation Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798350353594

This paper designs event-triggered switched observers for the networked distributed multi-agent systems based on the cloud computing systems. Firstly, a novel cloud computing system architecture is designed, in which each group of agents can interact with each others through distributed cloud nodes, and each cloud node also serves as an event-triggered switched observer to observe the topologies of the systems. Secondly, event-triggered switched observers are designed, which can assist distributed agents in accurately obtaining information from the leader, even with continuous switching topologies. Then, through stability analysis, we demonstrate the effectiveness of the observers. Finally, we prove the feasibility of the observers through simulation. © 2024 ieee.

关键词： distributed cloud

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The Smart Optimization of Security systems in Wireless Personal sensor Networks 2

The Smart Optimization of Security Systems in Wireless Perso...

引用

2nd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Das, Sunanda Sindhu Madhuri, G. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Wireless personal sensor networks (WPSNs) are an emerging technology that provides a platform for monitoring and collecting data from a variety of sources. These networks are used to monitor environmental, health, and even lifestyle factors. As the use of WPSNs continues to grow, it is important to consider the security implications of using these networks. WPSNs rely on a variety of technologies to ensure secure communication between devices. These include encryption, authentication, secure protocols, and secure key management. Encryption is used to protect data from being intercepted and decoded by an unauthorized user. Authentication is used to verify the personality of the user and the device attempting to access the WPSN. Secure protocols are used to ensure secure communication between the devices and the server. Finally, secure key management is used to manage and store the encryption keys used to protect the data. In addition to encryption, authentication, and secure protocols, WPSNs must also be designed with a robust access control system. Access control systems are used to limit access to the WPSN based on user credentials and other factors. This assist ensure that authorized users alone can access the data stored on the WPSN. © 2023 ieee.

关键词： Authentication

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UFed-GAN: Secure Federated Learning over Wireless sensor Networks with Unlabeled Data 59

UFed-GAN: Secure Federated Learning over Wireless Sensor Net...

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Wijesinghe, Achintha Zhang, Songyang Qi, Siyu Ding, Zhi Univ Calif Davis Davis CA 95616 USA Univ Louisiana Lafayette Lafayette LA 70504 USA

ISBN: (纸本)9798350304060;9798350304053

The rising demand for deploying low-latency data analysis and protecting privacy in a cloud-based setting has led to the emergence of federated learning (FL) as an important learning paradigm over wireless sensor networks. Due to the success of FL, generative models such as generative adversarial networks (GANs) are now utilized in FL to provide higher privacy and utility. However, existing GAN-based FL approaches are power-hungry which poses unbearable demands on resource-limited distributed users. Considering practical learning systems involving limited computational power and unlabeled data over wireless networks, this work investigates FL in a resource-constrained and label-free data environment. Specifically, we propose a novel framework known as UFed-GAN that captures sensor-side data distribution without local classification training. We analyze the convergence and privacy of the proposed UFed-GAN. Our experimental results demonstrate the strong potential of UFed-GAN in addressing limited computational resources and unlabeled data while preserving privacy.

关键词： Federated learning unlabeled data data privacy generative adversarial networks

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A distributed Scheduling Method for Networked UAV Swarm based on computing for Communication

A Distributed Scheduling Method for Networked UAV Swarm base...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Chen, Runfeng Li, Jie Chen, Yiting Huang, Yuchong Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9781665491907

UAV swarms have attracted much attention for post-disaster search and rescue, pollution monitoring and traceability, etc., where distributed scheduling is required to arrange careful tasks and time quickly. The market-based methods are widely favored but they rely on the environmentally influenced communication network to complete negotiation, while the onboard computing of UAV is robust and redundant. This paper proposes a distributed scheduling method for networked UAV swarm based on computing for communication, which trades a modest increase in computing for a significant decrease in communication. First, by analyzing the task removal strategies of two representative methods, the consensus-based bundle algorithm (CBBA) and performance impact (PI) algorithm, a new removal strategy is proposed, which expands the exploration of the bundle and can potentially reduce communication rounds. Second, the proposed task-related optimization method can extract task conflict nodes from the native communication protocol, and use the sampling and estimation strategies to resolve task conflicts in advance. Third, historical bids are cleverly used to infer others' locations, which is necessary for task-related optimization. Fourth, to verify the algorithm in real communication, a hardware-in-the-loop (HIL) ad-hoc network simulation system is constructed, which uses real network protocols and simulated channel transmissions. Finally, the HIL Monte Carlo simulation results show that, compared with CBBA and PI, the proposed method can significantly reduce the number of communication rounds and the total scheduling time, without increasing the communication protocol overhead and loss of optimization.

关键词： Unmanned aerial vehicles (UAV)

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