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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641-650 订阅

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Accelerating Time to Science using CRADLE: A Framework for Materials Data Science 30

Accelerating Time to Science using CRADLE: A Framework for M...

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30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Nihar, Arafath Ciardi, Thomas G. Chawla, Rounak Akanbi, Olatunde Chaudhary, Vipin Wu, Yinghui French, Roger H. Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Case Western Reserve Univ Dept Mat Sci Cleveland OH USA

ISBN: (纸本)9798350383225

Modern materials science research problems present a challenge to data science and analytics as experiments generate Petabyte-scale spatiotemporal datasets that span a number of modalities and formats. Creating computing infrastructure and frameworks that support the scale and diversity of materials science data while remaining accessible for materials scientists to use is a non-trivial task. We have developed the Common Research Analytics and Data Lifecycle Environment (CRADLE) to solve the challenges of materials data science through a scalable research computing framework and cyber infrastructure that can (1) handle large-scale, heterogeneous datasets (2) provide a flexible toolbox for building machine learning pipelines that span from ingestion to model deployment (3) be accessible to research scientists with limited to extensive computational backgrounds and (4) utilize a myriad of low performance to high performance computer systems. CRADLE is a framework that integrates distributed systems like Hadoop and High-Performance computing (HPC) infrastructure to handle materials data at scale. This all enables the general materials data scientist to query Petabytes of data and train thousands of models in a parallel, distributed environment. We demonstrate three use cases for CRADLE to benchmark its capability to ingest and analyze spatiotemporal materials data at scale. These tasks span three data modalities: transforming 2.6 billion Photovoltaic time-series power measurements, training hundreds of deep learning models on Atomic Force Microscopy images, and ingesting 27 billion geospatial data points. CRADLE exemplifies an overarching framework that accelerates time to science, extends to other domains with similar challenges, and expands the horizon of data science and research.

关键词： scalable infrastructure distributed computing Hadoop AI4Science materials data science

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Security threat detection performance analysis of a distributed architecture WSN 19

Security threat detection performance analysis of a distribu...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

作者： Arreaga, Nestor X. Estrada, Rebeca Blanc, Sara Noboa, Andres Vera, Nelson ESPOL Polytechnic University Escuela Superior Politecnica del Litoral Campus Gustavo Galindo Km. 30.5 Via Perimetral Guayaquil090902 Ecuador ITACA Universitat Politecnica de Valencia Camino de Vera s/n Valencia Valencia46022 Spain

IoT technologies are becoming more and more common in our daily activities because the networks they create are capable of collecting information, monitoring and controlling remotely. However, these devices are not exempt from security attacks, as they become vulnerable entry points to data networks. The use of traditional methods to secure networks (e.g., Next Generation Firewalls (NGFW), encryption, etc.) is not recommended because the devices used in this type of network are limited in terms of computing power and storage availability (e.g., nodeMCU). In this paper, we propose to design two intrusion detection systems in embedded systems using machine learning (ML) algorithms, Artificial Neural Networks and K-means. In a distributed architecture Wireless sensor Network scenario (WSN), we evaluate their performance in terms of connection and response times, detection accuracy and intruder detection time. Simulation results show that both models are able to find irregularities in network traffic within milliseconds. © 2024 The Authors. Published by Elsevier B.V.

关键词： Network intrusion

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Mutual Information Analysis of Neuromorphic Coding for distributed Wireless Spiking Neural Networks

Mutual Information Analysis of Neuromorphic Coding for Distr...

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2024 international conference on Wireless for Space and Extreme Environments

作者： Savazzi, Pietro Vizziello, Anna Dell'Acqua, Fabio Univ Pavia Pavia Italy CNIT Consorzio Nazl Interuniv Telecomunicaz Unita Pavia Pavia Italy

ISBN: (纸本)9798350351118;9798350351125

Wireless spiking neural networks (WSNNs) enable energy-efficient communications, especially in contexts of edge intelligence and learning, for both terrestrial (beyond 5G/6G) and Earth-space networking systems. Recent research work has revealed that distributed wireless SNNs (DWSNNs) achieve good performance in terms of inference accuracy and savvy use of energy in edge devices, under the constraints of limited bandwidth and spike loss probability. This technology appears thus promising for wireless sensor networks (WSNs) in space applications, where energy constraints are a major limiting factor. In this work, we focus on neuromorphic impulse radio (IR) transmission techniques for DWSNNs, considering different coding algorithms that implement IR modulations. Our quantitative evaluation relies on information-theoretic measures that should relate with different aspects of performance. Such measures may help achieving the optimal balance in DWSNNs applications, and their definition and evaluation in a simulated environment constitutes the main contribution of this manuscript.

关键词： Energy-efficient communications earth-space networking systems information-theoretic measures IR neuromorphic coding spiking neural networks WSN

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Resource-Aware Classification via Model Management Enabled Data Stream Optimization 20

Resource-Aware Classification via Model Management Enabled D...

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20th ieee international conference on Pervasive computing and Communications (ieee PerCom)

作者： Suenkel, Michael Elmamooz, Golnaz Grawunder, Marco Phan Thai Hoang Rauch, Elke Schmeling, Lara Thurner, Stefan Nicklas, Daniela Univ Bamberg Chair Mobile Syst Bamberg Germany Carl von Ossietzky Univ Oldenburg Dept Comp Sci Oldenburg Germany Bavarian State Res Ctr Inst Agr Engn & Anim Husb Freising Weihenstephan Germany Ludwig Maximilians Univ Munchen Chair Anim Welf Ethol Anim Hyg & Anim Husb Dept Vet Sci Fac Vet Med D-80539 Munich Germany

ISBN: (纸本)9781665416436

The integration of machine learning (ML) approaches in sensor-based applications in the field of pervasive computing is becoming increasingly prominent due to the increasing number of sensor-based applications in general and the continuous adaption of ML approaches to new domains. Several ML models are used within a processing pipeline that operates on the same sensor data. Still, the cloud computing approach is a straightforward solution where all sensor data is sent to the cloud before processing, which is inefficient according to resource utilization. Appropriate management of the different processing tasks for ML models enhances resource utilization. This paper proposes an architecture for resource-aware classification empowered by an ML model management (MLMM) framework and a distributed data stream management system (DDSMS). First, the classification pipeline is decomposed and implemented as data stream operators. Second, ML models are retrieved from an MLMM framework considering preprocessing, segmentation, and feature alignment to enable an effective redundancy elimination. Finally, the classification pipeline is deployed using resource-aware operator placement optimization. The evaluation results on a real-world scenario of a sensor-based activity classification pipeline for dairy cows show that our approach can reduce network utilization up to 98.9%.

关键词： distributed data stream management machine learning model management redundancy elimination operator placement pervasive computing activity classification

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Are You Really Charging Me? 42

Are You Really Charging Me?

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Lin, Chi Yang, Ziwei Ren, Jiankang Wang, Lei Zhong, Wei Wu, Guowei Zhang, Qiang Dalian Univ Technol Sch Software Technol Dalian Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Wireless rechargeable sensor networks (WRSNs), which benefit from recent breakthroughs in Wireless Power Transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods concentrate on system performance improvement while little attention has been paid to security, making them vulnerable to novel attacks. In this paper, we develop a novel Charging Spoofing Attack (CSA), in which a mobile charger (MC) is charging a node intuitively. Nevertheless, it is launching an attack based on the nonlinear superposition principle of electromagnetic waves, causing the target node to be unable to receive any energy and finally exhausted in vain. First, we explain and model the nonlinear superposition effect through experiments, which points out the potential of launching such a novel attack. Second, we formalize the attacking problem as a charging uTility optimization problem with key noDe timE window constraints (TIDE). Then, we propose an approximation algorithm termed CSA to solve the TIDE problem with a bounded performance guarantee. Theoretical analyses are presented to exploit the feature of CSA. Finally, to demonstrate the outperformed features of our scheme, extensive simulations and test-bed experiments are conducted, revealing that CSA can exhaust at least 80% of key nodes without being detected.

关键词： Wireless communication Wireless sensor networks System performance Wireless power transfer Programming Approximation algorithms Time factors

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Blockchain Machine: A Network-Attached Hardware Accelerator for Hyperledger Fabric 42

Blockchain Machine: A Network-Attached Hardware Accelerator ...

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42nd ieee international conference on distributed computing systems (ICDCS)

作者： Javaid, Haris Yang, Ji Santoso, Nathania Upadhyay, Mohit Mohan, Sundararajarao Hu, Chengchen Brebner, Gordon Xilinx Singapore Singapore Xilinx San Jose CA USA

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

In this paper, we demonstrate how Hyperledger Fabric, one of the most popular permissioned blockchains, can benefit from network-attached acceleration. The scalability and peak performance of Fabric is primarily limited by the bottlenecks present in its block validation/commit phase. We propose Blockchain Machine, a hardware accelerator coupled with a hardware-friendly communication protocol, to act as the validator peer. It can be adapted to applications and their smart contracts, and is targeted for a server with network-attached FPGA acceleration card. The Blockchain Machine retrieves blocks and transactions in hardware directly from the network interface, which are then validated through a configurable and efficient block-level and transaction-level pipeline. The validation results are then transferred to the host CPU where non-bottleneck operations are executed. From our implementation integrated with Fabric v1.4 LTS, we observed up to 12x speedup in block validation when compared to software-only validator peer, with commit throughput of up to 68,900 tps. Our work provides an acceleration platform that will foster further research on hardware acceleration of permissioned blockchains.

关键词： distributed ledger Scalability Pipelines Smart contracts Throughput Fabrics Blockchains

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distributed Energy Optimization for Mobile Networks Using Potential Games 21

Distributed Energy Optimization for Mobile Networks Using Po...

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21st ieee international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Wang, Zhizongkai Wang, Hanfei Wang, Zhongji Xiao, Yilin Zhao, Yunzhi Li, Xiaowen Chen, Xufeng Gao, Lin Hou, Fen Huang, Jianwei Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Guangdong Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Guangdong Peoples R China Univ Macau State Key Lab IoT Smart City Taipa Macau Peoples R China Univ Macau Dept Elect & Comp Engn Taipa Macau Peoples R China Huawei Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Sch Sci & Engn Shenzhen Key Lab Crowd Intelligence Empowered Low Shenzhen 518172 Guangdong Peoples R China Chinese Univ Hong Kong CSIJRI Joint Res Ctr Smart Energy Storage Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9798350363999;9798350364002

The rapid advancement of information and communication technology (ICT) has made the industry a significant contributor to global carbon emissions. As the foundation of ICT, next-generation mobile communication networks aim to be more powerful and energy-efficient. However, optimizing energy efficiency in real networks is challenging due to large-scale, multi-layer control variables and the dynamic environment. This paper addresses the energy efficiency optimization problem from a network perspective by controlling cross-layer variables including both cell activation status and cell priority, to minimize overall network energy consumption while ensuring user quality-of-experience, which poses an NP-hard mixed-integer nonlinear programming problem. To tackle this, we propose a non-cooperative game where each cell acts as a player, optimizing its activation status and reference signal transmission power (determining its priority). We show that the game is a potential game, guaranteeing the existence of Nash equilibrium and the convergence of simple distributed algorithms towards Nash equilibrium. We further show that the Nash equilibrium points of the game can (but not always) reach the global optimal energy efficiency. Simulation results show that our proposed method can reduce the total system cost (including both energy consumption cost and user experience loss) by up to 28% compared to existing methods in the literature. Moreover, the performance loss of our proposed method, compared to the global optimal solution, is less than 13.7%. In summary, this work offers a realistic network model, introduces a novel game-based method, and provides extensive performance evaluation, making a significant contribution to both industry and academia.

关键词： Energy Efficiency distributed Energy Optimization Potential Game

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AI-Driven Health Monitoring of distributed computing Architecture: Insights from XGBoost and SHAP 4

AI-Driven Health Monitoring of Distributed Computing Archite...

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4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Sun, Xiaoxuan Yao, Yue Wang, Xiaoye Li, Pochun Li, Xuan Mountain View United States Northeastern University PortlandME United States Western University LondonON Canada Northeastern University BostonMA United States Columbia University New YorkNY United States

ISBN: (纸本)9798331528973

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of the entire system. In view of the lack of accuracy and interpretability of traditional methods in node health status judgment, this paper proposes a health status judgment method based on XGBoost and combines the SHAP method to analyze the interpretability of the model. Through experiments, it is verified that XGBoost has superior performance in processing complex features and nonlinear data of edge computing nodes, especially in capturing the impact of key features (such as response time and power consumption) on node status. SHAP value analysis further reveals the global and local importance of features, so that the model not only has high-precision discrimination ability but also can provide intuitive explanations, providing data support for system optimization. Research shows that the combination of AI technology and computer system optimization can not only realize the intelligent monitoring of the health status of edge computing nodes but also provide a scientific basis for dynamic optimization scheduling, resource management and anomaly detection. In the future, with the in-depth development of AI technology, model dynamics, cross-node collaborative optimization and multimodal data fusion will become the focus of research, providing important support for the intelligent evolution of edge computing systems. © 2024 ieee.

关键词： Electronic health record

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Analysis of the distributed Network Integrated Online Sports Detection System

Analysis of the Distributed Network Integrated Online Sports...

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6th international conference on Intelligent computing and Control systems, ICICCS 2022

作者： Wang, Yongjun Huazhong University of Science and Technology Sports Institute Wuhan 430000 China

ISBN: (纸本)9781665410359

This paper uses a variety of research methods to investigate the implementation effect of online physical education teaching in a primary school during the new crown pneumonia epidemic. The results found that the online teaching strategy is insufficient in the specific implementation process, focusing on the local decision algorithm of wireless sensor network distributed sports online detection and the data fusion algorithm of the system fusion center, and the classical Bayesian and Neyman- Pearson's theory is analyzed, aiming to build a variety of communication and docking platforms, improve the professional quality of physical education teachers, cultivate students' habit of self-exercise, realize the orderly development of the daily online teaching system of physical education classes, and provide the basis for the future development of physical education teaching during the epidemic period. Provide reference and reference. © 2022 ieee.

关键词： Wireless sensor networks

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sensor Network System with Dynamic Service Deployment Depending on Network Condition for Smart Aquaponics

Sensor Network System with Dynamic Service Deployment Depend...

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ieee international conference on Consumer Electronics (ICCE)

作者： Murakami, Ryota Yamamoto, Hiroshi Ritsumeikan Univ Grad Sch Informat Sci & Engn 1-1-1 Noji Higashi Kusatsu Shiga 5258577 Japan Ritsumeikan Univ Dept Informat Sci & Engn 1-1-1 Noji Higashi Kusatsu Shiga 5258577 Japan

ISBN: (纸本)9781665491303

There is an urgent need to automate and improve the efficiency of agricultural work, and IoT systems are attracting attention to increase the efficiency of agricultural operations. This study focuses on Aquaponics, an efficient agricultural method that combines hydroponics and aquaculture. Aquaponics operations need to not only manage the environment for plants, bacteria, and fish but also ensure that the plants and fish are growing properly. Therefore, we propose sensor network system that aims to improve the efficiency of aquaponics operations by analyzing information on the state of the growing environment obtained from various sensors, as well as the growth rate of plants and fish. Furthermore, to reduce the workload of administrators for device management, we propose an efficient management system with a container orchestration platform that dynamically deploys applications to nodes installed in the field. The system adopts a highly available architecture for farms where the condition of the network communication environment fluctuates. The proposed architecture is equipped with dynamic service deployment that automatically switches between edge and cloud computing based on the network conditions. Through performance evaluation, we confirm that containers equipped with the proposed analysis method can be automatically deployed and operated on edge devices using the container orchestration platform.

关键词： Internet of Things (IoT) containerization Kubernetes cloud computing edge computing aquaponics

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