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

共 30758 条记录，以下是321-330 订阅

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TSITE IP: A Case Study of Intellectual Property distributed Platform based on Cloud Services 23

TSITE IP: A Case Study of Intellectual Property Distributed ...

引用

23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Yang School of Intellectual Property Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

In recent years, the 'whole chain' development level of China's intellectual property creation, protection and application has been greatly improved. At the same time, cloud computing technology is booming, and intellectual property data distributed platforms based on cloud storage are emerging one after another. Firstly, this paper introduces the domestic intellectual property cloud platform services from the perspectives of government, state-owned enterprises and private enterprises;Secondly, four typical distributed platforms provided by commercial resources are selected to summarize the problems faced by the operation mode of domestic intellectual property services;Then, it compares and discusses the functions and service modes of domestic intellectual property distributed platform, and takes TSITE IP as an example, puts forward the design and construction strategies of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age. Finally, according to the development of contemporary information technology, this paper puts forward challenges and development direction for the future development of intellectual property platform. © 2021 ieee.

关键词： Computers Cloud computing Government distributed databases Intellectual property Market research Information age

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A low cost weather monitoring, PV and prediction system in East Africa 16

A low cost weather monitoring, PV and prediction system in E...

引用

international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Kagarura, Geoffrey Mark Hilleringmann, Ulrich Petrov, Dmitry Paderborn Univ Sensor Technol Grp Paderborn Germany

ISBN: (纸本)9798350309461

This paper is about the design of a low-cost system to monitor the weather and resulting PV generation for small-midsized microgrids in Africa. The setups are one 12kWp near-grid and 192-panel 76kWp off-grid systems. The weather parameters collected are wind speed, wind direction, temperature, panel temperature, humidity, and rainfall as well as in-plane, horizontal irradiation. Cloud coverage, a major influence of solar generation, is captured by two 100 degrees to 200 degrees view-field cameras per site. Moreover, we compare this precise data with internet data for variation, particularly with large-grid-area satellite data. A wide and ultra-wide capture of cloud images in multiple resolutions for analysis and development of prediction algorithms. Low-resolution images allow for easier transmission over the internet in remote East Africa. A more accurate analysis of the different cloud types and characteristics is enabled with the dynamic range of two set-ups in addition to a comparison of the consistency and accuracy of the various algorithms to be developed. The ultra-low-cost is achieved across weather stations and sky imaging using a Raspberry Pi-based setup. Beneficial redundancies from multiple sensors per site are expected due to the experimental setup nature.

关键词： weather monitoring PV prediction PV monitoring solar mini-grid microgrid

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VAKY: Scheduling In-network Aggregation for distributed Deep Training Acceleration 30

VAKY: Scheduling In-network Aggregation for Distributed Deep...

引用

30th ieee international conference on Parallel and distributed systems, ICPADS 2024

作者： Cui, Penglai Pan, Heng Zhou, Jianer Wu, Qinghua Wang, Zhaohua Li, Zhenyu Huawei Technologies Co. Ltd. Institute of Computing Technology Beijing China Chinese Academy of Sciences Computer Network Information Center Beijing China Peng Cheng Laboratory Shenzhen China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9798331515966

distributed machine learning (DML) has recently experienced widespread application. A major performance bottleneck is the costly communication for gradients synchronization. Recently, researchers have explored the use of programmable switches for in-network synchronous aggregation of gradients to mitigate the communication overhead. Nevertheless, the performance of in-network synchronous aggregation is significantly impacted by the stragglers. Unfortunately, the schedulers in existing DML systems are no longer effective in dealing with stragglers because of the ignorance of the aggregation progress that is offloaded from the parameter servers to the programmable switches. To address this gap, this paper presents VAKY, an adaptive scheduler specifically designed for in-network aggregation. At the heart of VAKY is the variable K-block sync method, where the aggregators stop waiting for updates from more workers once having received updates from the fastest K workers for each block of gradients. We propose an efficient solution that can dynamically choose the optimal values of K during the training process, in order to minimize the expected training completion time. We have integrated VAKY into PyTorch, and our experiments show that compared to the state-of-the-art in-network aggregation systems, VAKY improves the aggregation throughput by up to 40% and reduces the training time by 25%. © 2024 ieee.

关键词： Personnel training

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A Hybrid Epoch Scheduling Scheme of Mixed Workloads with Different Types of Jobs on distributed Processors 20

A Hybrid Epoch Scheduling Scheme of Mixed Workloads with Dif...

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20th ACS/ieee international conference on Computer systems and Applications (AICCSA)

作者： Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9798350319439

With the advances in the large-scale computing platforms, jobs that run on such systems are becoming complex and they present increased variability. In these systems, the workload usually has complex structure including jobs with different characteristics. Along with regular jobs, there are jobs represented as linear workflows (LWs) or even jobs which are bags of LWs. Consequently, efficient scheduling algorithms should be employed to ensure satisfactory performance as well as fair processing of jobs. This research aims to study appropriate scheduling algorithms of mixed workloads consisting of simple jobs including one task only, along with bags-of-LWs (BoLWs). Different techniques for scheduling are studied in a set of distributed resources. Extensive simulation experiments are carried out to investigate their effectiveness for various system utilization levels and variability in job/task service demands. The results demonstrate that the efficacy of the scheduling schemes depends on the variability level in job/task service demands, the system load, and the level of fairness that is considered as satisfactory.

关键词： distributed systems scheduling performance fairness

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Use of sensor Networks with Self-Organizing Algorithm to Increase the Agricultural Productivity 5

Use of Sensor Networks with Self-Organizing Algorithm to Inc...

引用

5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Lahoti, Saurabh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350383522

Traditional farming methods are becoming incapable of keeping up with global population growth. As a result, innovative farming ideas are desperately necessary to meet the food needs of a growing population. Intelligent farming systems based on sensor networks with self-organizing algorithms have gained popularity in recent years as a means of increasing agricultural production. In this study, we will use sensor networks with self-organizing algorithms to boost agricultural productivity. Finally, the paper describes the future applications of appropriate technology in agricultural production. © 2024 ieee.

关键词： Agricultural Productivity Self-Organizing Algorithm sensor Networks

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distributed Energy Harvesting and Power Management Units for Self- Powered In-Fabric Sensing Networks 67

Distributed Energy Harvesting and Power Management Units for...

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67th ieee international Midwest Symposium on Circuits and systems (MWSCAS)

作者： Liu, Xinjian Agrawal, Anjali Tanaka, Akiyoshi Calhoun, Benton H. Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

ISBN: (纸本)9798350387186;9798350387179

With the advancements in power and size reduction of integrated circuits, millimeter-scale self-powered systems can be seamlessly integrated into textiles or a single fiber strand, revolutionizing body sensor network applications. Energy harvesting and power management units (EHPMUs) face new design requirements for in-fabric systems including full autonomy, ultra-low quiescent power, high efficiency, and mm-scale footprint. Additionally, they must efficiently coordinate energy across distributed subsystems for enhanced system viability and scalability in a fiber or fabric network. This article examines current trends and innovative techniques for sub-mu A EHPMUs. It presents two case studies of sub-mu A distributed EHPMU designs: the first explores a switched-capacitor EHPMU featuring one-rail power sharing and cooperative dynamic-voltage-and-frequency-scaling;the second discusses a triple-input hybrid-inductor-capacitor multi-output EHPMU that offers improved efficiency, reduced size, multi-rail power sharing for heterogeneous fiber networks. We summarize the tradeoffs and key design considerations necessary for mm-scale EHPMUs designed for self-powered distributed fiber networks.

关键词： energy harvesting and power management unit distributed system cooperative power management self-powered system system-in-fiber fabric sensing network hybrid-inductor-capacitor ultra low-power internet-of-things system-on-chip

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Reinforcement Learning for Minimizing Communication Delay in Edge computing 42

Reinforcement Learning for Minimizing Communication Delay in...

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

作者： Rajashekar, Kolichala IIT Bhilai Sejbahar India

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. In order to minimize the communication delay between the IoT devices and the edge devices, we need a sophisticated approach for assignment IoT devices to the edge devices. Most of the heuristics solutions previously used to tackle the problem faced issues being solution stuck at local optima and high computational over head. To that end, researchers used reinforcement learning (RL) algorithms to explore the search space to get near optimal solutions. For our work, we consider RL based algorithms and show the preliminary results.

关键词： Reinforcement learning Real-time systems Delays Space exploration Internet of Things distributed computing Edge computing

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Hybrid Adam Coati Optimization with DLSTM for Biomedical Parameter Prediction Using sensor 3

Hybrid Adam Coati Optimization with DLSTM for Biomedical Par...

引用

3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Rajesh, L. Mohan, H.S. Bindiya, M.K. Visvesvaraya Technological University Dayananda Sagar Academy Of Technology And Management Department Of Information Science And Engineering Karnataka Bengaluru India Karnataka Bengaluru India Sjb Institute Of Technology Department Of Computer Science And Engineering Karnataka Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

A real-time patient monitoring system procesfses a wide range of biomedical parameters and can store and safely capture biomedical parameters. It lowers the diagnosis time of the several diseases that are prevalent in healthcare services. Accurate prediction of biomedical parameters can significantly reduce healthcare costs and assist in effective decision-making. This research proposes a deep learning approach with hybrid optimization for biomedical parameter prediction by using sensors. At first, a healthcare centre with many patients having various disorders is considered. Several biomedical sensors are placed on the patients and these sensors include a respiratory Rate (RR) sensor, Heart rate (HR) sensor, Pulse Rate (PR) sensor, Ambulatory Blood Pressure (ABP)-sensor, and Saturation of Peripheral Oxygen (SpO2). Based on these sensed parameters, biomedical parameter prediction by using sensor based Deep Long Short-Term Memory (DLSTM)is executed. The DLSTM is trained using the proposed Hybrid Adam Coati Optimization(HAdCO). Here, the HAdCO is developed by incorporating of Coati Optimization Algorithm (COA) with Adam Optimizer. Furthermore, the HAdCO-DLSTM is evaluated by utilising metrics, like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). The HAdCO-DLSTM had a minimal value of MAE at 0.0063, MSE at 0.0062, and RMSE at 0.0078. © 2024 ieee.

关键词： Long short-term memory

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OCDE: Adaptive Differential Evolution Algorithm in distributed Intelligent systems 16

OCDE: Adaptive Differential Evolution Algorithm in Distribut...

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16th international conference on Cyber-Enabled distributed computing and Knowledge Discover, CyberC 2024

作者： Wang, Minghao Du, Shaoyi Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an China

ISBN: (纸本)9798331506896

In autonomous driving, the optimization theory and algorithms for distributed intelligent systems are essential for enhancing vehicle decision-making capabilities, path planning, and environmental perception. Evolutionary algorithms, as a global optimization method inspired by biological evolution, is widely used in these scenarios. Unfortunately, the performance of evolutionary algorithms relies on the properties of the solution space, the optimization strategy, and parameter settings, and their efficiency also requires more rigorous evaluation. This study focuses on differential evolution algorithm, examining the properties of the solution space for optimization problems, the preferences of strategies and parameters in different optimization scenarios, and setting new standards for efficiency evaluation. Based on the experimental conclusions, modifications are made to our algorithm to further verify the correctness of our findings. All experimental results are deduced based on the CEC2017 benchmark problem suite from the ieee Congress on Evolutionary Computation (CEC). © 2024 ieee.

关键词： Anthropogenic

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Topology Aware Cluster Configuration for Minimizing Communication Delay in Edge computing 42

Topology Aware Cluster Configuration for Minimizing Communic...

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

作者： Rajashekar, Kolichala Paul, Souradyuti Karmakar, Sushanta Sidhanta, Subhajit IIT Bhilai Sejbahar India IIT Guwahati Gauhati India

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. Since the generalized assignment problem being NP-Hard, an optimal assignment of IoT devices to the edge cluster is hard. We propose the application RL based heuristics to obtain a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge devices are overloaded. We demonstrate that our algorithm outperforms the state-of-the-art.

关键词： Clustering algorithms Real-time systems Delays Topology Internet of Things distributed computing Edge computing

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