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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411-420 订阅

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Supporting Real-time Networkwide T-Queries in High-speed Networks 42

Supporting Real-time Networkwide T-Queries in High-speed Net...

引用

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

作者： Wang, Yuanda Wang, Haibo Ma, Chaoyi Chen, Shigang Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Traffic measurement is key to many important network functions. Supporting real-time queries at the individual flow level over networkwide traffic represents a major challenge that has not been successfully addressed yet. This paper provides the first solutions in supporting real-time networkwide queries and allowing a local network function (for performance, security or management purpose) to make queries at any measurement point at any time on any flow's networkwide statistics, while the packets of the flow may traverse different paths in the network, some of which may not come across the point where the query is made. Our trace-based experiments demonstrate that the proposed solutions significantly outperform the baseline solutions derived from the existing techniques.

关键词： High-speed networks Real-time systems Time measurement Servers Security distributed computing

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Decentralized Road Conditions Assessment for Smart Mobility Management in Abu Dhabi 19

Decentralized Road Conditions Assessment for Smart Mobility ...

引用

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

作者： Albasir, A. Ben-Othman, J. Zayed Univ Coll Technol Innovat CTI Dubai U Arab Emirates Univ Paris Saclay CNRS Cent Supelec Paris France Univ Sorbonne Paris Nord Villetaneuse France

ISBN: (纸本)9798350333398

Mobility management is crucial for urban planning, and accurate road condition information is necessary for safe and efficient transportation. In the context of this study, mobility refers to the quality of the road and the level of service provided to road users. To this end and to advance the state-of-the-art in improving mobility management strategies, we propose a new capability for our ongoing work on the Decentralized Road Traffic Monitoring (DRTM) system: road conditions assessment, aimed at enhancing mobility management for Abu Dhabi city. Our DRTM system uses smartphone cameras, Vehicular Ad-hoc Networks (VANETs) on vehicle-to-vehicle (V2V) mode, and federated learning to extract and classify road features, such as potholes, cracks, and accidents, impacting overall mobility. The system comprises a smartphone app and a decentralized network of participants, providing real-time insights about road conditions. Our experimental results demonstrate the system's effectiveness in providing precise and up-to-date information about road conditions, with increased scalability and efficiency compared to centralized methods. By utilizing federated learning, the system ensures data security and privacy.

关键词： Mobility Management Intelligent Transportation systems distributed Traffic Monitoring Computer Vision Federated Learning Object Detection

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Algorithm and Hardware Co-Design for Efficient PMCMC Acceleration on FPGA

Algorithm and Hardware Co-Design for Efficient PMCMC Acceler...

引用

9th international conference on Signal and Image Processing (ICSIP)

作者： Wang, Fulong Peng, Shiyu Gong, Yuanhao Li, Minghan Liu, Shuanglong Hunan Normal Univ Coll Hunan Prov Key Lab Low Dimens Quantum Struct & Quantum Contr Key Lab Phys & Devices Postmoore Era Changsha Peoples R China

ISBN: (纸本)9798350350920

The particle Markov-chain Monte Carlo (PMCMC) method is a stochastic algorithm that combines Particle Filters (PFs) and Markov-chain Monte Carlo (MCMC) techniques. This approach is widely used in Bayesian inference for high-dimensional state spaces and nonlinear, non-Gaussian dynamic systems. However, current PMCMC accelerators face significant challenges due to their intensive computational complexity and the intricate particle routing, limiting their application in real-time scenarios. To address these challenges, we propose a novel distributed PMCMC method that leverages parallel computing to enhance hardware execution speed. Additionally, our method introduces a particle exchange scheme that not only resolves the accuracy issues caused by particle routing in distributed PMCMC but also achieves faster computing speed. Our design is implemented on a Xilinx Kintex-7 xc7k480t FPGA device. Experimental results demonstrate that our accelerator is nearly 65x faster than CPU performance, and provides speedups up to 5x compared to existing FPGA-based accelerators.

关键词： Particle Markov-chain Monte Carlo (PMCMC) Particle Filter (PF) Field Programmable Gate Arrays (FPGAs) distributed PMCMC Hardware Acceleration

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distributed ESP-Now CSI Human Activity Recognition for Few-Shot Energy Saving in Building Automation 14

Distributed ESP-Now CSI Human Activity Recognition for Few-S...

引用

14th ieee international conference on Cyber Technology in Automation, Control, and Intelligent systems, CYBER 2024

作者： Bauchspiess, Adolfo Mane, Tanmay Furst-Walter, Iris Becker, Jurgen ENE/FT University of Brasília Brazil ITIV/KIT Karlsruhe Germany

ISBN: (纸本)9798331506056

Device-free sensing with wifi signals has gained great attention from the community to enhance building automation systems. In such systems, energy efficiency can be improved through predictive forecasting of occupancy as well as human activities carried out in the room. In this context, we propose a wifi-network of eight ESP32 modules, where we measure Channel State Information (CSI) and couple the sensor data with few-shot bundled majority vote models to perform Human Activity Recognition (HAR). We achieve an accuracy of 74% for HAR. Those are promising results for HAR as a basis for low-power building automation solutions using COTS in single antenna sensor setup. © 2024 ieee.

关键词： Wi-Fi

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Energy Saving Oriented Electricity Load Calculation of High-speed Train Group: A distributed Computation Method

Energy Saving Oriented Electricity Load Calculation of High-...

引用

2024 lEEE international conference on Advanced Information, Mechanical Engineering, Robotics and Automation, AIMERA 2024

作者： Zhang, Zhe Sun, Yani Baihetiyaer, Muhetabaier Beijing Jiaotong University School of Traffic and Transportation Beijing China

ISBN: (纸本)9798350343335

Electricity load calculation of high-speed train group is one critical step for traction power system design. The energy-saving oriented electricity load calculation of high-speed train group is time-consuming. A distributed computation framework is proposed to improve the computing efficiency of solution algorithm by splitting the train group into single trains and dividing the railway lines into various sections. The experiment results show that the computing efficiency has been improved by 80.9% and becomes more stable under distributed computation environment. © 2024 ieee.

关键词： Power systems computer aided design

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Energy-Efficient Routing in Underwater Wireless sensor Networks Through Cluster-Dragonfly Optimization 3

Energy-Efficient Routing in Underwater Wireless Sensor Netwo...

引用

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

作者： Anitha, D. Husain, Saif O. Velpula, Srikanth Rajani, N. Dineshkumar, R. School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Tamilnadu Kattankulathur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Dept.of Ece Bengaluru India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

In this paper, develop a power control scheme for underwater wireless sensor networks that takes into account the impact of communication ranges on network performance, including energy utilization and delay overhead. The primary objective of our scheme is to assign a differentiated optimal communication radius for each sensor node, enhancing overall network efficiency. The proposed method utilizes energy-aware Cluster-Dragonfly Optimization (CDFO), but it might face challenges in handling large-scale optimization, and the scalability of the algorithm may be limited when dealing with high-dimensional or complex optimization. Although the proposed CDFO method exhibits high performance compared to existing methods, such as Self-Adaptive Glow Worm Swarm Optimization (SA-GWO), Improved Coyote Optimization Algorithm (ICOA), Hybrid Cat Cheetah Optimization algorithm (HCCOA), and Chaotic Search-and-Rescue Optimization (CSRO), it may still face challenges in terms of handling large-scale optimization. Nevertheless, the proposed CDFO yields impressive results, achieving a Packet Delivery Ratio (PDR) of 99%, Packet Loss Ratio (PLR) of 4%, Energy Consumption of 9%, and Alive Node count of 330. These outcomes outperform existing methods in the context of Underwater WSN. © 2024 ieee.

关键词： Optimization

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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in distributed computing systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

引用

2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Hu, Qinglong Bi, Jing Zhang, Wei Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China City University of Hong Kong Dept. of Computer Science 999077 Hong Kong College of Computer Beijing University of Technology Beijing100124 China University of Connecticut Dept. of Computer Science and Engineering StorrsCT06269 United States Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively. © 2024 ieee.

关键词： Quality of service

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Optimizing distributed Communication: A Comparison of Load Balancing Strategies in gRPC and Apache Thrift 15

Optimizing Distributed Communication: A Comparison of Load B...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rao, Subhiksha Suresh Mishra, Sahil Seshadri, Samarth Bhaskaran, Sreebha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Efficient load balancing is essential for distributed systems to have outstanding performance and scalability. This study compares the load balancing strategies of two popular distributed application frameworks, gRPC and Apache Thrift. The features of each algorithm are examined, along with their advantages and disadvantages. To give a helpful evaluation, the performance of these algorithms is tested using key factors like throughput, latency, resource utilisation, and scalability. The comparison looks at the trade-offs involved in selecting different algorithms for distributed system load balancing and identifies the scenarios in which one method might be more suitable. ©2024 ieee.

关键词： Scalability

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Assessing the Performance of Autonomous and Adaptive Communications systems in Wireless sensor Networks

Assessing the Performance of Autonomous and Adaptive Communi...

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2024 international conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Kunal Gaur, Sunil Kumar Haripriya Mechanical Engineering Vivekananda Global University Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper examines the overall performance of autonomous and adaptive communications systems in Wi-Fi sensor networks. Self-sustaining communications systems are those capable of adjusting their radio configurations to satisfy the situations provided by the changing surroundings without external interplay. Such a method could enable Wi-Fi node verbal exchange to be extra dependable and electricity-green. On the other hand, in contrast to independent structures, adaptive communications systems depend upon interaction between distinct nodes in the network to attain the highest quality configuration at the rate of multiplied complexity. These techniques were evaluated by evaluating the overall performance of two varieties of sensor networks, a self-sustaining communication community and an adaptive conversation community. The contrast's consequences confirmed that adaptive conversation systems can outperform independent communication systems beneath joyous occasions and venture-specific requirements. © 2024 ieee.

关键词： communications interaction multiplied radio requirements

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distributed Multi-GPU Community Detection on Exascale computing Platforms

Distributed Multi-GPU Community Detection on Exascale Comput...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Sattar, Naw Safrin Lu, Hao Wang, Feiyi Halappanavar, Mahantesh Oak Ridge Natl Lab Oak Ridge TN 37830 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9798350364613;9798350364606

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics Processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed Parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

关键词： multi-GPU Louvain community detection clustering hybrid HIP MPI

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