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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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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Task Scheduling Optimization Method for Vehicles Serving as Obstacles in Mobile Edge Computing Based IoV Systems.

Mingwei Feng1, Haiqing Yao1, Jie Li1

  • 1Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China.

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This study introduces a new task scheduling algorithm (TSARC) to optimize vehicle-to-infrastructure communication under dense traffic. TSARC improves efficiency and reliability by intelligently migrating computing tasks to roadside units.

Keywords:
channel occlusioninternet of vehiclesmobile edge computingnon-dominated sorting genetic algorithm-IIItask schedulingvehicle-to-infrastructure

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Area of Science:

  • Internet of Vehicles (IoV)
  • Wireless Communication Networks
  • Traffic Flow Modeling

Background:

  • Vehicle-to-infrastructure (V2I) communication links face significant pressure due to increasing vehicle service demands.
  • Existing models struggle to account for dynamic traffic, fading channels, and competitive delays in the Internet of Vehicles (IoV).

Purpose of the Study:

  • To propose a dynamic dense traffic flow model considering fading channels and redefine reliability based on real-time vehicle location.
  • To develop an optimized task scheduling algorithm for roadside units (RSUs) that balances execution time and computational cost.

Main Methods:

  • Proposed a dynamic dense traffic flow model with redefined reliability using real-time vehicle location.
  • Formulated RSU task scheduling as a multi-objective optimization problem.
  • Developed the Task Scheduling Algorithm based on a Reliability Constraint (TSARC), incorporating quick non-dominated sorting, elite strategy, and a reference point mechanism.

Main Results:

  • TSARC demonstrates improved scalability and efficiency compared to the genetic algorithm (GA) across varying task numbers and traffic densities.
  • The algorithm effectively manages channel resource contention and data conflicts in dynamic IoV scenarios.
  • Numerical simulations using British Highway Traffic Flow Data validated TSARC's superior performance.

Conclusions:

  • TSARC offers a robust and flexible solution for optimizing task scheduling in V2I communication under challenging traffic conditions.
  • The proposed model and algorithm effectively address limitations of existing methods, providing better system state measurement and resource scheduling.
  • TSARC enhances IoV system performance by optimizing computation offloading and resource utilization.