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Related Concept Videos

Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Distributed Loads: Problem Solving01:21

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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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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Distributed Loads01:19

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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.
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Short-distance Transport of Resources02:12

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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Efficient Matching-Based Parallel Task Offloading in IoT Networks.

Usman Mahmood Malik1,2, Muhammad Awais Javed1, Jaroslav Frnda3,4

  • 1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan.

Sensors (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for parallel task offloading in fog computing (FC) networks. The proposed matching-based approach significantly reduces task latency, improving efficiency in 6G systems.

Keywords:
Internet of Thingsexternalities problemfog computingmatching theorypartial task offloadingtask offloading

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Area of Science:

  • Computer Science
  • Telecommunications Engineering
  • Network Computing

Background:

  • Fog computing (FC) is crucial for future 6G networks, offering enhanced computation and reliability.
  • Parallel offloading, a key FC concept, splits tasks for distributed processing but faces sub-task allocation challenges.
  • Existing methods struggle with efficient sub-task splitting and mapping to fog nodes.

Purpose of the Study:

  • To propose a novel many-to-one matching-based algorithm for efficient sub-task allocation in parallel offloading.
  • To develop preference profiles for IoT and fog nodes to minimize task computation delay.
  • To address the externalities problem arising from dynamic preference profiles in matching algorithms.

Main Methods:

  • Developed a many-to-one matching algorithm for sub-task to fog node allocation.
  • Created preference profiles for Internet of Things (IoT) and fog nodes.
  • Introduced a technique to mitigate externalities caused by dynamic preference profiles.

Main Results:

  • The proposed matching-based offloading technique outperforms existing methods.
  • Achieved a 52% improvement in task latency under high task loads.
  • Demonstrated the effectiveness of preference profiles and externality mitigation.

Conclusions:

  • The novel matching-based algorithm effectively solves sub-task allocation challenges in parallel offloading.
  • The proposed approach enhances task latency and system performance in fog computing environments.
  • This work contributes to the advancement of efficient task management in 6G networks.