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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 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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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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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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A Game-Based Computing Resource Allocation Scheme of Edge Server in Vehicular Edge Computing Networks Considering

Xiangyan Liu1, Jianhong Zheng1, Meng Zhang2

  • 1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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|January 11, 2024
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Summary
This summary is machine-generated.

This study introduces partial task offloading in vehicle edge computing networks (VECNs) to improve the Internet of Vehicles (IoV). The proposed algorithm optimizes resource allocation and task offloading ratios for multiple task vehicles (TaVs), enhancing quality of service (QoS).

Keywords:
computing resource allocationexact potential gameoffloading strategyvehicular edge computing networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Emerging vehicle applications increase the burden on the Internet of Vehicles (IoV).
  • Vehicle Edge Computing Networks (VECNs) offer a solution by enabling partial task offloading.
  • Optimizing task offloading ratios and edge server (ES) resource allocation is crucial for VECNs.

Purpose of the Study:

  • To propose a novel algorithm for computation resource allocation in multi-task vehicle (TaV) systems within VECNs.
  • To jointly optimize service vehicle (SeV) selection, task offloading strategies, and computing resource allocation.
  • To ensure TaVs' quality of service (QoS) by minimizing processing delay.

Main Methods:

  • A best response-based centralized multi-TaV computation resource allocation algorithm (BR-CMCRA) is proposed.
  • Service vehicles (SeVs) are selected based on channel gain from candidate SeVs (CSVs).
  • An exact potential game (EPG) framework is used for step-by-step computation resource allocation to maximize benefits.

Main Results:

  • The BR-CMCRA algorithm effectively allocates computation resources and determines task offloading ratios.
  • The proposed algorithm demonstrates superior performance compared to existing basic algorithms in simulations.
  • The utility function, linked to task processing delay, ensures enhanced QoS for TaVs.

Conclusions:

  • Partial task offloading in VECNs is a viable strategy to manage IoV burdens.
  • The BR-CMCRA algorithm provides an effective solution for resource allocation and task offloading in TaV systems.
  • The developed approach enhances QoS and outperforms traditional methods in VECN environments.