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

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Tools for Digitalization of Public and Economic Services 3

Tools for Digitalization of Public and Economic Services

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3rd ieee international conference on System Analysis and Intelligent computing, SAIC 2022

作者： Petrenko, Anatolii National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' System Design Department Kyiv Ukraine

The article considers the technology of creating tools to support digitalization tasks to perform complex computational scenarios based on service-oriented (microservice) architecture in a cloud environment. © 20... 详细信息

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

关键词： Cloud computing Web services Knowledge based systems Medical services sensor systems sensors Dynamic programming

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Cooperative Charging as Service: Scheduling for Mobile Wireless Rechargeable sensor Networks 41

Cooperative Charging as Service: Scheduling for Mobile Wirel...

引用

41st ieee international conference on distributed computing systems (ICDCS)

作者： Xu, Jia Hu, Suyi Wu, Sixu Zhou, Kaijun Dai, Haipeng Xu, Lijie Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781665445139

Wireless Power Transmission (WPT) has been widely used to replenish energy for Wireless Rechargeable sensor Networks. However, the charging service model, which is of the essence to commercial WPT, has not emerged so far. In this paper, we present a wireless charging service model from the perspective of cooperative charging economics, and formulate the Cooperative Charging Scheduling (CCS) problem for joint optimization of rechargeable devices' charging cost and moving cost. We first propose two intragroup cost sharing schemes to sustain the cooperation among devices. Then, the approximation algorithm CCSA of the CCS problem is proposed based on greedy approach and submodular function minimization. Furthermore, we model the large-scale CCS problem as a coalition formation game and present a game theoretic algorithm CCSGA. We show that CCSGA finally converges to a pure Nash Equilibrium. We conduct simulations, and field experiments on a testbed consisting of 5 chargers and 8 rechargeable sensor nodes. The results show that the average comprehensive cost of CCSA is 27.3% lower than the noncooperation algorithm and is only 7.3% higher than the optimal solution on average. In field experiments, CCSA outperforms the noncooperation algorithm by 42.9% in terms of comprehensive cost on average. Moreover, CCSGA is much faster than the approximation algorithm and is more suitable for large-scale cooperative charging scheduling.

关键词： WRSN cooperative charging service submodular function coalition formation game Nash Equilibrium

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Deep Learning based Multi sensor Data Fusion for Radar and IFF

Deep Learning based Multi Sensor Data Fusion for Radar and I...

引用

2022 ieee international conference on Electronics, computing and Communication Technologies, CONECCT 2022

作者： Sapna Tm, Dhipu Rajesh, R. Drdo Centre for Airborne Systems Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Multi sensor data fusion is the process of deriving more accurate, consistent and useful information by integrating data from multiple sensors than that can be achieved using individual sensor. Conventional methods for data fusion require complex models with computationally expensive methods. This paper proposes a deep neural network based method for multi sensor data fusion using detections from both Radar and IFF sensors. A deep learning based Long Short Term Memory (LSTM) model is used for data fusion. LSTM models overcome the problem of exploding and vanishing gradients present in Recurrent Neural Networks. Bi-directional LSTM is used for forming tracks from sensor observations using proposed association. The proposed fusion system architecture can be extended to sensors other than Radar and IFF also. The tracks generated by using detections from both sensors are closer to ground truth than tracks generated from individual sensors. Data fusion from multiple sensors improves the accuracy of a wide range of applications such as target tracking and battlefield surveillance. © 2022 ieee.

关键词： Deep neural networks

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Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences

Recursive Binding for Similarity-Preserving Hypervector Repr...

引用

ieee international conference on Fuzzy systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / international Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Rachkovskij, Dmitri A. Kleyko, Denis Int Res & Training Ctr Informat Technol & Syst Kiev Ukraine Lulea Univ Technol Lulea Sweden Univ Calif Berkeley Redwood Ctr Theoret Neurosci Berkeley CA USA Res Inst Sweden Intelligent Syst Lab Kista Sweden

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representations of large fixed dimensionality. A critical step in designing the HDC/VSA solutions is to obtain such representations from the input data. Here, we focus on a wide-spread data type of sequences and propose their transformation to distributed representations that both preserve the similarity of identical sequence elements at nearby positions and are equivariant with respect to the sequence shift. These properties are enabled by forming representations of sequence positions using recursive binding as well as superposition operations. The proposed transformation was experimentally investigated with symbolic strings used for modeling human perception of word similarity. The obtained results are on a par with more sophisticated approaches from the literature. The proposed transformation was designed for the HDC/VSA model known as Fourier Holographic Reduced Representations. However, it can be adapted to some other HDC/VSA models.

关键词： hyperdimensional computing vector symbolic architectures distributed representation hypervector data structures sequence representation similarity preserving transformation shift equivariance recursive binding

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distributed Cloud computing Architecture in Hyperspectral Remote Sensing Image Classification under Big Data 5

Distributed Cloud Computing Architecture in Hyperspectral Re...

引用

5th ieee international conference on Information systems and Computer Aided Education, ICISCAE 2022

作者： Feng, Kai Wu, Yi Shaanxi Academy of Aerospace Technology Application Co. Ltd. Xi'an China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

By using the cloud computing platform that has achieved excellent commercial results to perform parallel classification processing of massive remote sensing data, it can meet the requirements of improving the parallel processing efficiency of massive remote sensing data. The purpose of this paper is to study the application of distributed cloud computing architecture in hyperspectral remote sensing image classification based on big data. The related technologies of cloud computing are expounded, and the model of SCSRC, a sparse representation classification method based on spatial correlation constraints, is proposed. By analyzing the principle and solution method of SCSRC model, a distributed classification algorithm DP-SCSRC based on spatial correlation constraint sparse representation is proposed on the Spark platform, and the following optimization methods are designed for the core steps. Finally, through experiments with different computing nodes and different amounts of computing data, it is proved that the DP-SCSRC algorithm has a good distributed speedup ratio and data scalability under the premise of ensuring the classification accuracy. © 2022 ieee.

关键词： distributed cloud

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Exploring the Landscape of distributed Graph Clustering on Leadership Supercomputers

Exploring the Landscape of Distributed Graph Clustering on L...

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2024 ieee international conference on Big Data, BigData 2024

作者： Sattar, Naw Safrin Kapocius, Abigail Lu, Hao Halappanavar, Mahantesh Wang, Feiyi Oak Ridge National Laboratory Oak RidgeTN United States University of North Carolina at Charlotte CharlotteNC United States Pacific Northwest National Laboratory RichlandWA United States

ISBN: (纸本)9798350362480

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains. © 2024 ieee.

关键词： Scalability

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Evolutionary Hybrid Algorithms to Pioneer Localization in Wireless sensor Networks

Evolutionary Hybrid Algorithms to Pioneer Localization in Wi...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Balachandran, G. Krishna, R. Vivek Ranjith, S. Venkata Siva Prasad, C.H. Jeppiaar Engineering College Department of Electronics and Communication Engineering Chennai600119 India Jeppiaar University School of Engineering and Technology Chennai600119 India

ISBN: (纸本)9798350319200

This research article delves deep into the intricate realm of wireless sensor network (WSN) localization, driven by the critical objective of minimizing localization errors. This pursuit of accuracy prompts the utilization of hybrid algorithms that seamlessly fuse Particle Swarm Optimization (PSO) + Firefly Optimization (FF) and Genetic Algorithm (GA) + FF. Drawing inspiration from nature-inspired computing paradigms, these hybrid algorithms represent a novel approach to addressing the localization challenge. By synergizing Particle Swarm Optimization (PSO) and Firefly Optimization (FF), this study pioneers a powerful solution that propels advancements in solving the intricate WSN localization problem. The convergence of these two algorithms brings forth substantial enhancements in accuracy, efficiency, and efficacy, reflecting the potential of such hybrid methodologies to revolutionize localization strategies. The research's contribution lies not only in the successful application of these hybrid algorithms but also in their conceptual innovation, exemplifying the potency of nature-inspired approaches in the realm of computing. In a world increasingly reliant on accurate localization, the outcomes of this study hold implications for a wide array of applications, ranging from surveillance and environmental monitoring to healthcare. Through this exploration of evolutionary hybrid algorithms, this article not only addresses the immediate challenge of WSN localization but also lays the foundation for future advancements in the field. © 2023 ieee.

关键词： Firefly Optimization (FF) Genetic Algorithm (GA) hybrid algorithms localization errors Particle Swarm Optimization (PSO) Wireless sensor Network (WSN)

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A Frequency-aware Grouping Strategy for Stateful Operators in distributed Stream Processing systems 29

A Frequency-aware Grouping Strategy for Stateful Operators i...

引用

29th ieee international conference on Parallel and distributed systems, ICPADS 2023

作者： Sun, Dawei Chen, Zhe Lv, Weilong Gao, Shang Rong, Jia China University of Geosciences School of Information Engineering Beijing100083 China Deakin University School of Information Technology Waum PondsVIC3216 Australia Monash University Department of Data Science & AI ClaytonVIC3800 Australia

ISBN: (纸本)9798350330717

Current optimization for stateful flow processing computation tends to focus on load balancing without considering the utilization of downstream instance resources. To address this issue, we propose a data stream grouping method called Fa-Stream, specifically designed for stateful operators and incorporating field values frequency-awareness. Fa-Stream is implemented in three main aspects: (1) A data stream grouping model is built using Count-Min Sketch and Gated Recurrent Unit (GRU) to predict and analyze the frequency of field values. It selectively chooses high-frequency field values, and the communication distance model and instance resource constraint model are designed to adjust the weights of downstream instances for high-frequency field values. (2) A cyclic access routing table is generated, and weights are dynamically adjusted by a rebalancing scheme to avoid load skewness. Consistent hash grouping is implemented for low-frequency field values, and dual mapping is used to prevent large-scale migration caused by scaling. To validate the effectiveness of Fa-Stream, comparative experiments between Partial Key Grouping (PKG) and Fa-Stream are conducted using the Storm platform. Results demonstrate that Fa-Stream improves tuple throughput by 12.3%, reduces system delay by 14.2%, and increases load balancing degree by 42.8%. Furthermore, fa-Stream exhibits efficiency and stability across different data skews and tuple input rates. © 2023 ieee.

关键词： Big Data Data Stream Grouping Load Balance Stateful Operator Stream computing

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*** Framework for Automated Code Scaling and Execution on Multiple distributed Machines 18

*** Framework for Automated Code Scaling and Execution on Mu...

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18th ieee international conference on Computer Science and Information Technologies, CSIT 2023

作者： Chaplia, Oleh Klym, Halyna Lviv Polytechnic National University Department of Specialized Computer Systems Lviv Ukraine

ISBN: (纸本)9798350360462

Microservices is an architectural approach in software development where the complex solution is created from small, independently deployable services that communicate through defined APIs. Microservices find applications in many domains, including banking, finance, healthcare, e-commerce, government, military, logistics, and gaming. Microservice architecture provides general principles of how the solution containing small services should *** research proposes a *** framework for automated code sharing between services and execution in the environment of a distributed system. The proposed framework built on top of *** contains a tiny code base and mechanisms for sharing the user-developed code among many instances of the microservices inside one *** was selected for this framework because it is a tiny framework and library for building server applications. Also, it can be run on embedded devices, cell phones, or any device with a small OS. © 2023 ieee.

关键词： Cloud computing architecture

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Research and Development of Grid Infrastructure Intelligent Monitoring Platform Based on distributed Real-time sensor Network 3

Research and Development of Grid Infrastructure Intelligent ...

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3rd ieee international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Yan, Yan Wang, Chunli Zhang, Na Chen, Bingqian Chen, Xilong Chen, Xiaomin State Grid Fujian Economic Research Institute Fujian Fuzhou350012 China

ISBN: (纸本)9781665472784

The construction of the power grid is a huge project involving various aspects such as electricity, communication and transportation. distributed systems have the characteristics of convenient and flexible nonlinear data collection, so higher requirements are put forward for distributed equipment monitoring. At present, domestic research on intelligent network technology is still in its infancy. However, with the rapid development of social and economic levels, more and more demands for power supply security and reliability have also increased. At the same time, the traditional power grid has serious network failure problems in the construction process, it cannot adapt to the modern communication system of modern cities, which has caused a huge impact on the power grid construction. The distributed real-time monitoring platform can timely detect the problems in the urban power system for the power supply department, and provide reliable, accurate, stable and safe services. This paper first analyzes the distributed optical fiber sensing technology, which uses the unique characteristics of optical fibers to connect the distributed optical fiber sensor array to the power grid monitoring network, and then uses the traditional power transmission method to monitor the urban distribution network in real time, determines grid operating status through data analysis and simulation measurements. Secondly, this paper analyzes the software scheme and implementation of remote real-time monitoring system, which provides technical support for the construction of distributed real-time monitoring system. Finally, this paper proposes the data transmission based on distributed gateway and monitoring network communication, and uses network routing protocol to monitor and protect parameters such as grid voltage, current and signal in real time, so as to realize the real-time monitoring of grid voltage. © 2023 ieee.

关键词： Electric power distribution

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