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

共 3166 条记录，以下是111-120 订阅

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Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models 25

Communication Analysis for Multidimensional Parallel Trainin...

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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： Lai, Zhiquan Hao, Yanqi Li, Shengwei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. The efficiency of parameter communication among training devices/processes is often the performance bottleneck of large model training. Analysis of parameter communication mode and traffic has important reference significance for the research of interconnection network design and computing task scheduling to improve the training performance. In this paper, we analyze the parametric communication modes in typical 3D parallel training (data parallelism, pipeline parallelism, and tensor parallelism), and model the traffic in different communication modes. Finally, taking GPT-3 as an example, we present the communication in its 3D parallel training. © 2023 ieee.

关键词： Deep neural networks

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Solar Irradiance Forecasting Utilizing distributed and Federated Learning 25

Solar Irradiance Forecasting Utilizing Distributed and Feder...

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作者： Zhang, Hao Yan, Ke College of Information Engineering China Jiliang University Hangzhou China China Jiliang University National University of Singapore Singapore

ISBN: (纸本)9798350330014

Photovoltaic energy generation and prediction are crucial for the integration between solar plants and smart grid. Accurate solar radiation prediction is one of the most important research topics for solar energy generation. Existing deep learning methods require sufficient data for training for achieving the desired results. In traditional deep learning modeling, the data sets required for model training usually come from various data in different regions and organizations, and it is difficult to ensure data security and privacy protection. Federated learning can effectively utilize the data optimization model while ensuring data security and privacy, but there is still the risk of privacy leakage by analyzing the parameters uploaded by the client. To effectively utilize data optimization models while ensuring data security and privacy, we propose a method based on a federated learning framework and N-BEATS to achieve interpretable time series prediction of solar irradiance. The model builds the N-BEATS model on the solar irradiance datasets of multiple institutions in a distributed method, does not share local data to protect institutional data privacy, the concept of differential privacy (DP) is introduced, Gaussian noise is added to the parameters of the client before the server-side aggregation processing, the model parameters of each distributed node are collected through TCP communication for federated average, and the parameters are returned to each distributed node. The experimental results show that compared with other deep learning models, the model has better prediction accuracy under the premise of ensuring data security and privacy. © 2023 ieee.

关键词： Solar radiation

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False Data Injection Attacks Against High Voltage Transmission systems 19

False Data Injection Attacks Against High Voltage Transmissi...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, dcoss-IoT 2023

作者： Radoglou-Grammatikis, Panagiotis Zafeiropoulou, Magda Atanasova, Maria Zlatev, Pencho Giannakidou, Sofia Lagkas, Thomas Argyriou, Vasileios Markakis, Evangelos K. Moscholios, Ioannis Sarigiannidis, Panagiotis University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece Sofia1784 Bulgaria K3Y Ltd Sofia1612 Bulgaria International Hellenic University Department of Computer Science Kavala Campus 65404 Greece Kingston University London Department of Networks and Digital Media SurreyKT1 2EE United Kingdom Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete71004 Greece University of Peloponnese Department of Informatics and Telecommunications Tripolis22100 Greece

ISBN: (纸本)9798350346497

The digitisation of the smart electrical grid provides several advantages and valuable services, such as self-monitoring, pervasive control and smart healing. However, despite the benefits of this progression, critical cybersecurity and privacy issues are raised due to the vulnerabilities of legacy Electrical Power and Energy systems (EPES) and the evolution of stealthy cyberthreats and malware. In this paper, we give emphasis to False Data Injection Attacks (FDIAs) that can affect the EPES State Estimation (SE). In particular, we investigate two FDIA categories, namely: (a) Global Positioning System (GPS) Spoofing Attacks and (b) ieee C37.118 FDIAs against an actual testbed emulating a high-voltage ieee 9-Bus transmission grid. Finally, we provide a relevant Intrusion Detection System (IDS) capable of detecting the aforementioned FDIAs. The evaluation analysis demonstrates the impact of the above FDIAs and the efficiency of the proposed IDS. © 2023 ieee.

关键词： Legacy systems

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Design and Evaluation of a K8s-based System for distributed Open-Source Cellular Networks 19

Design and Evaluation of a K8s-based System for Distributed ...

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19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Palomares, Javier Coronado, Estefania Rincon, David Shuaib Siddiqui, Muhammad i2CAT Fdn Barcelona Spain Univ Politecn Catalunya UPC Dept Network Engn Barcelona Spain Univ Castilla La Mancha Albacete Spain

ISBN: (纸本)9798350333398

Virtualization in cellular networks is one of the key areas of research where technologies, infrastructure and challenges are rapidly changing as 5G system architecture demands a paradigm shift. This paper aims to study the viability and the performance of cloud-native infrastructures for hosting network functions. The selected frameworks implement both the 4G and the 5G stacks and their network functions. This work considers a variety of scenarios for enabling the deployment of a distributed and open-source cellular network: a baremetal setup, an all-docker-based setup and the proposed Kubernetes setup. Moreover, an analysis of the impact that the Radio Access Network (RAN) and the Core Network (CN) have on computational resource utilization is presented as the network conditions vary. The design proposed in this work has been validated and analyzed using the proposed prototype and testbed. This paper proposes a design to increase resource usage flexibility and performance and reduction of deployment time. The analysis of the gathered data reveals that the deployments of containerized cellular networks display better performance in terms of flexibility, low startup times, and ease of deployment while consuming the same resources as the non-containerized.

关键词： Virtualization NFV SDN distributed systems 4G 5G Performance Evaluation

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Agile Energy-Optimized Routing and Delay-Conscious Protocol for Underwater Wireless Acoustic sensor Networks 2

Agile Energy-Optimized Routing and Delay-Conscious Protocol ...

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2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Jalaja, S. Divya, P.S. Nisha, A.P. Divya, J. Jayapradha, S. Manjushree, S. Veltech High Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350382952

Wireless Underwater sensor Networks serve a multitude of purposes, including disaster avoidance, contaminant monitoring, overseas research, oceanographic data acquisition, supported routing, and strategic surveillance. However, there are several constraints that affect their effectiveness, such as higher energy consumption, limited bandwidth, node mobility, and asynchronous clocks. In this paper, an Energy-Efficient Depth Centric Clustering Routing (EEDCCR) protocol is proposed to overcome these constraints. The idea behind the EEDCCR protocol is that nodes at similar depths and higher energy levels form clusters seeking for cluster heads, whereas non-communicating nodes retry to join clusters or enter into sleep mode, which reduces energy consumption and lengthens the longevity of the system, unlike existing systems in the literature. The protocol's efficacy is analyzed and authenticated through NS-2 simulation, focusing on parameters such as, throughput, packet delivery ratio (PDR) and end-to-end (E2E) latency in comparison to DBR, Co-DRAR, and DRAR protocols. © 2024 ieee.

关键词： Data acquisition

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Multiple Early-Exits Strategy for distributed Deep Neural Network Inference

Multiple Early-Exits Strategy for Distributed Deep Neural Ne...

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international conference on distributed computing in sensor systems (dcoss)

作者： Marion Sbai Niki Trigoni Andrew Markham Department of computer science University of Oxford Oxford United Kingdom

Existing work on multi-camera network mainly focused on centralized systems, the role of the cameras being limited to capture images and send them to the cloud. However, this approach is impractical when the network connection is limited or variable. Our methodology distributes DNN inference across multiple edge clients and the cloud using early-exits and bottlenecks for time and accuracy requirements. In limited communication scenarios like LoRa, our solution improves average latency by 80 % compared to traditional cloud solutions.

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VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in distributed IoT 25

VPFL: A Verifiable Property Federated Learning Framework Aga...

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作者： Wu, Yuanxiang Cheng, Hang Guan, Lin Liu, Peihao Chen, Fei Wang, Meiqing College of Computer Science and Big Data Fuzhou University Fuzhou China

ISBN: (纸本)9798350330014

The development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed Internet of Things (IoT). However, existing FL frameworks still suffer from invisible attacks in IoT environments, such as free-rider attacks, backdoor attacks, and model theft. In this paper, we propose a Verifiable Property Federated Learning (VPFL) framework to overcome the above invisible attacks. We present a black-box watermarking task distribution mechanism to prevent free-rider attacks by verifying the property of local models. Our adversarial fine-tuning embedding technique can not only eliminate backdoors in global models, but also simultaneously embed white-box watermarks into model parameters to prevent model theft. Comprehensive experimental evaluations demonstrate that our framework outperforms state-of-the-art schemes in terms of security and feasibility against invisible attacks. © 2023 ieee.

关键词： Internet of things

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Approaches for the Design of sensor Networks for Pest Monitoring in Farming Applications

Approaches for the Design of Sensor Networks for Pest Monito...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： David Niederprüm Jan Schlichter Sven Pullwitt Lars Wolf TU Braunschweig Braunschweig Germany

Invasive species have increasingly become a problem in recent years. Climate change and changing environmental conditions allow them to conquer new habitats. E.g. the Brown Mamorated Stink Bug (lat. halyomorpha halys) (BMSB) has been spreading across Europe and causes significant damage to fruit crops in Italy. To be able to quickly respond to such threats, it is important to understand which environmental conditions are beneficial or harmful for such pests. In this paper, we present a sensor network to help determine the relevant environmental factors for detecting and implementing countermeasures against such pests. We highlight the challenges of designing the system and show the lessons we learned during the deployment and operation of eight months.

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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 ...

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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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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...

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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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