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

共 30780 条记录，以下是1531-1540 订阅

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Research on A distributed Cooperative Transmission Scheme with Edge computing

Research on A Distributed Cooperative Transmission Scheme wi...

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2023 international conference on Networks, Communications and Intelligent computing, NCIC 2023

作者： Chen, Hua-Min Fang, Ruijie Chen, Tao Li, Hui Li, Fan Lin, Shaofu Faculty of Information Technology Beijing University of Technology Beijing China Beijing China Network Optimization Center China Unicom Beijing Branch Beijing China

ISBN: (纸本)9798350371949

As wireless communication services continue to advance, the demand for reliability and latency has become increasingly stringent. This is especially crucial in areas like personal consumption and the Industrial Internet of Things (IIoT). To address the need for Hyper Reliable and Low-Latency Communication (HRLLC) and cater to diverse user equipment (UE) capabilities, this paper presents a novel approach with a focus on optimizing both latency and energy efficiency. It introduces a distributed geo-location aware UE aggregation transmission scheme based on edge computing for HRLLC services. This scheme includes a communication procedure designed to enhance reliability and reduce latency, wherein some UEs or particular higher end devices play as coordination nodes for packet duplication through multi-path simultaneously. Considering the system signaling overhead and signal processing capability of each cooperation node (CN), a multiple-user scheduling algorithm with geo-location awareness under different coordination nodes is performed at each cooperation node, with a restriction of latency requirement and energy efficiency, i.e., a lower Energy-Delay Product (EDP) performance. The research presented in this paper offers valuable insights into the development of distributed cooperation systems for reliability and delay sensitive services. © 2023 ieee.

关键词： Edge computing

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Cooperative Resource Sharing Between Agents in Multi-distributed Fog Nodes 14

Cooperative Resource Sharing Between Agents in Multi-Distrib...

引用

14th international conference on Information and Communication systems, ICICS 2023

作者： Malkawi, Anfal Shurman, Mohammad Jordan University of Science and Technology Network Engineering and Security Dept. Irbid Jordan

ISBN: (纸本)9798350307870

Cloud computing provides high computation resources to the user's applications. Some of the user's applications are time-sensitive. Therefore, processing these applications in traditional cloud computing centers might fail due to the high latency between the cloud layer and the user equipment layer. Thus, to solve this issue, fog computing is deployed near the user's nodes layer to extend the cloud layer and provide computation resources with low latency. The fog computing layer may contain limited devices compared to the cloud layer. As a result at peak hours, when multiple tasks are requested from IoT applications to the fog nodes, communication congestion might occur and the processing time will significantly increase. In addition, the fog layer may contain limited resources and limited power, and many of the requested tasks will be offloaded to the cloud layer for processing. Consequently, the purpose of the fog layer is not accomplished. In this paper, we propose a cooperation scheme between fog nodes to process tasks requested from IoT nodes with minimum service latency and maximum throughput. © 2023 ieee.

关键词： Fog

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Secure parallel Outsourcing Scheme for Large-scale Matrix Multiplication on distributed Cloud Servers 29

Secure parallel Outsourcing Scheme for Large-scale Matrix Mu...

引用

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

作者： Wang, Yinlong Tao, Yunting Kong, Fanyu Gu, Zhaoquan Yu, Jia Zhang, Hanlin Binzhou Polytechnic College of Information Engineering Shandong Binzhou256603 China Shandong University School of Software Shandong Jinan250101 China Shandong Sansec Information Technology Co. Ltd Jinan250101 China School of Computer Science and Technology Shenzhen China Qingdao University College of Computer Science and Technology Shandong Qingdao266071 China

ISBN: (纸本)9798350330717

Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. This paper presents a parallel matrix multiplication outsourcing scheme based on Cannon's algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme's correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data. © 2023 ieee.

关键词： Cloud computing Data privacy Matrix multiplication Parallel outsourcing

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A Multi-sensor Data Fusion Method for Vehicles Based on Improved GNN Association 7

A Multi-sensor Data Fusion Method for Vehicles Based on Impr...

引用

7th CAA international conference on Vehicular Control and Intelligence, CVCI 2023

作者： Ge, Chunhao Wang, Yuhong Dept. China University of Petroleum Qingdao China

ISBN: (纸本)9798350340488

Intelligent driving technology has become a hot direction in the automotive industry. Accurate and stable tracking and fusion of target information from multiple heterogeneous onboard sensors are prerequisites for intelligent vehicles to make correct decisions. To reduce target measurement errors and improve the stability of perception results under the constraints of low computing power of vehicle microcontrollers, this paper proposes a framework for multi-sensor data fusion in vehicles. Addressing the problem of temporary target disappearance due to sensor occlusion and the presence of irrelevant signals and noise, a single-sensor target tracking method based on Kalman filter and the lifespan approach is designed. To overcome the issue of erroneous associations commonly encountered in traditional global nearest neighbor (GNN) methods, an improved GNN data association method is proposed. It involves designing a target correction method for single-sensor tracking, followed by the design of a tracking gate setting method. The association cost is used for secondary target selection within the gate, and a weighted similarity function is defined to establish target associations. Finally, a convex combination fusion algorithm is used to fuse data from multiple sensors. © 2023 ieee.

关键词： distributed data fusion global nearest neighbor intelligent vehicles Kalman filter state estimation

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distributed Ledger Technologies for Managing Heterogenous computing systems at the Edge 9

Distributed Ledger Technologies for Managing Heterogenous Co...

引用

9th ieee international conference on Internet of Things - systems, Management and Security (IOTSMS)

作者： Hernandez, Daniel Montero Queralta, Jorge Pena Westerlund, Tomi Univ Turku Turku Intelligent Embedded & Robot Syst TIERS Lab Turku Finland

ISBN: (纸本)9798350320459

The increased use of Internet of Things (IoT) devices -from basic sensors to robust embedded computers- has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established as a standard architecture for developing IoT solutions, since it can optimize the workload and capacity of systems that depend on cloud services by deploying necessary computing power close to where the information is being produced and consumed. However, as the network scale in size, reaching consensus becomes an increasingly challenging task. distributed ledger technologies (DLTs), which can be described as a network of distributed databases that incorporate cryptography, can be leveraged to achieve consensus among participants. In recent years DLTs have gained traction due to the popularity of blockchains, the mostwell known type of implementation. The reliability and trust that can be achieved through transparent and traceable transactions are other key concepts that bring IoT and DLT together. We present the design, development and conducted experiments of a proof-of-concept system that uses DLT smart contracts for efficiently selecting edge nodes for offloading computational tasks. In particular, we integrate network performance indicators in smart contracts with a Hyperledger Blockchain to optimize the offloading on computation under dynamic connectivity solutions. The proposed method can be applied to networks with varied topologies and different means of connectivity. Our results show the applicability of blockchain smart contracts to a variety of industrial use cases.

关键词： DLT Edge computing IoT Hyperledger Fabric Smart Contracts Sensing systems

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Smash: Flexible, Fast, and Resource-efficient Placement and Lookup of distributed Storage 23

Smash: Flexible, Fast, and Resource-efficient Placement and ...

引用

2023 ACM SIGMETRICS international conference on Measurement and Modeling of Computer systems, SIGMETRICS 2023

作者： Liu, Yi Shi, Shouqian Xie, Minghao Litz, Heiner Qian, Chen University of California Santa Cruz Santa Cruz United States

Smash is a new placement and lookup method for distributed storage systems. It achieves full placement flexibility and low DRAM cost to store ID-to-location mappings, two desired features that could not be achieved si... 详细信息

ISBN: (纸本)9798400700743

关键词： Dynamic random access storage

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A Fuzzy approach for load balancing in Blockchain-based Software Defined Vehicular Networks 18

A Fuzzy approach for load balancing in Blockchain-based Soft...

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18th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Alouache, Lylia Sylla, Tidiane Mendiboure, Leo Aniss, Hasnaa CY Cergy Paris Univ ETIS UMR 8051 CNRS Lab F-95000 Cergy France Univ Gustave Eiffel COSYS ERENA Lab F-77447 Marne La Vallee France

ISBN: (数字)9781665469753

ISBN: (纸本)9781665469753

Software Defined Vehicular Networks (SDVN) are increasingly being considered for vehicular communication systems as they could provide both flexibility, programmability and improved performance. The SDVN control plane should be distributed between a set of SDN controllers managing specific geographical areas and network functions (load balancing, firewall, etc.) to ensure a high level of scalability. In this environment, the Blockchain technology could be used to guarantee the coordination of the different SDVN controllers. Therefore, many works have demonstrated the benefits of Blockchain-based SDVN architectures. However, they did not investigate the optimization of the underlying Blockchain network. That is why, in this paper, we propose a high-performance multi-level Blockchain architecture which could enable SDVNs to scale up. We also introduce a Fuzzy logic-based mechanism allowing the real-time reconfiguration of the Blockchain network depending on the actual SDVN requirements: traffic increase, integration of a new controller, etc. We finally demonstrate, through experimentation, the benefits of our approach compared to existing solutions in terms of latency, CPU, throughput and scalability.

关键词： Vehicular Networks SDN distributed systems Consistency Blockchain Scalability Load Balancing

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Near-Linear Scaling Data Parallel Training with Overlapping-Aware Gradient Compression 29

Near-Linear Scaling Data Parallel Training with Overlapping-...

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

作者： Meng, Lin Sun, Yuzhong Li, Weimin Chinese Academy of Sciences Institute of Computing Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350330717

Existing Data Parallel (DP) trainings for deep neural networks (DNNs) often experience limited scalability in speedup due to substantial communication overheads. While Overlapping technique can mitigate such problem by paralleling communication and computation in DP, its effectiveness is constrained by the high communication-to-computation ratios (CCR) of DP training tasks. Gradient compression (GC) is a promising technique to obtain lower CCR by reducing communication volume directly. However, it is challenging to obtain real performance improvement by applying GC into Overlapping because of (1) severe performance penalties in traditional GCs caused by high compression overhead and (2) decline of Overlapping benefit owing to the possible data dependency in GC schemes. In this paper, we propose COVAP, a novel GC scheme designing a new coarse-grained filter, makes the compression overhead close to zero. COVAP ensures an almost complete overlap of communication and computation by employing adaptive compression ratios and tensor sharding tailored to specific training tasks. COVAP also adopts an improved error feedback mechanism to maintain training accuracy. Experiments are conducted on Alibaba Cloud ECS instances with different DNNs of real-world applications. The results illustrate that COVAP outperforms existent GC schemes in time-to-solution by 1.92x-15.39x and exhibits near-linear scaling. Furthermore, COVAP achieves best scalability under experiments on four different cluster sizes. © 2023 ieee.

关键词： data parallel distributed deep learning gradient compression Overlapping

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Enhancing Business Intelligence Through A Semi-distributed Blockchain Framework for Predictive Analytics On Sales Growth 2

Enhancing Business Intelligence Through A Semi-Distributed B...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Mullaikodi, C. Jagannathan, Sharath Kumar Jayanthi, N. Singh, Rakhi Tomar, Yesha Jaison, B. J. N. N Institute of Engineering Department of Ece Tamilnadu Tiruvallur India Data Science Institute Saint Peter's University NJ United States Chennai India Sgt University Faculty of Commerce and Management Haryana Gurugrm India School of Management Doon University Uttarakhand Dehradun India Rmk Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371055

The cutting-edge technologies in the ever-evolving sector of data analytics has been accelerated by the requirement for efficient and secure techniques of acquiring business intelligence. This approach provides a solution to the existing issues. In order to address the issues of data integrity, security, and transparency that are associated with the existing methodologies, there is a need for a paradigm shift that will lead to a solution that is more resilient. The traditional centralised data analytics solutions have their limitations when it comes to guaranteeing the accuracy and dependability of forecasts, and this is the most important aspect of the situation. A semi-distributed blockchain paradigm is proposed as a solution to the problems that are associated with centralised systems. This paradigm spreads the computing burden while maintaining a certain degree of decentralisation in order to enhance security. The approach that is currently being considered is the construction and deployment of a blockchain network that is semi-distributed and contains data analytics techniques. Smart contracts simplify the processes of data transmission and validation among participants, hence ensuring transparency and confidence in the predictive analytics model. These findings reveal an improvement in the system efficiency, data quality, and accuracy of forecasting. © 2024 ieee.

关键词： Business Intelligence Predictive Analytics Sales Growth Semi-distributed Blockchain Smart Contracts

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WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for distributed Surveillance 18

WideVision: A Low-Power, Multi-Protocol Wireless Vision Plat...

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18th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Scherer, Moritz Sidler, Fabian Rogenmoser, Michael Magno, Michele Benini, Luca Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (数字)9781665469753

ISBN: (纸本)9781665469753

The trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Exploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents WideVision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multiradio platform, including a LoRa interface, enabling medium and long-range communication, with a novel parallel RISC-V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC-V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter.

关键词： Wireless communication Training Wireless sensor networks Power measurement Microcontrollers Surveillance Battery charge measurement

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