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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
597
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

766
Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
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Related Experiment Video

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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Experimental Performance Analysis of a Scalable Distributed Hyperledger Fabric for a Large-Scale IoT Testbed.

Houshyar Honar Pajooh1, Mohammad A Rashid1, Fakhrul Alam1,2

  • 1Department of Mechanical and Electrical Engineering, Massey University, Auckland 0632, New Zealand.

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|July 9, 2022
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Summary

This study evaluates Hyperledger Fabric (HLF) performance for Internet of Things (IoT) systems. Findings reveal performance bottlenecks and offer a framework for optimizing blockchain in large-scale IoT applications.

Keywords:
Hyperledger Fabricblockchainlatencyperformancescalabilitythroughput

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Cybersecurity

Background:

  • Blockchain offers secure data management for decentralized Internet of Things (IoT) applications.
  • Current blockchain platforms face limitations in processing massive transaction volumes from IoT devices.
  • A comprehensive approach for integrating blockchain into IoT systems is lacking.

Purpose of the Study:

  • To empirically measure the performance of Hyperledger Fabric (HLF) in large-scale, distributed IoT environments.
  • To identify performance bottlenecks within HLF for blockchain-based IoT applications.
  • To present a performance monitoring model for HLF tailored to IoT requirements.

Main Methods:

  • Developed and implemented a performance monitoring framework for HLF.
  • Evaluated HLF performance under varying network workloads in a distributed setting.
  • Assessed key performance indicators: throughput, latency, network size, scalability, and peer capacity.

Main Results:

  • The study identified specific performance bottlenecks in HLF relevant to IoT applications.
  • The proposed monitoring framework effectively evaluated HLF performance with detailed, real-time insights.
  • HLF's capacity to handle large-scale IoT network demands was quantitatively assessed.

Conclusions:

  • The developed framework provides a scalable solution for monitoring blockchain performance in IoT.
  • Understanding HLF bottlenecks is crucial for its effective adoption in large-scale IoT systems.
  • This research contributes to optimizing blockchain integration for future IoT applications.